**A Voltammetric Nanodiamond-Coated Screen-Printed Immunosensor for The Determination of a Peanut Allergen in Commercial Food Products †**

**André Carvalho, Maria Freitas , Henri P. A. Nouws \* and Cristina Delerue-Matos**

REQUIMTE/LAQV, Instituto Superior de Engenharia do Porto, Instituto Politécnico do Porto, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal; andcarv16@gmail.com (A.C.); maria.freitas@graq.isep.ipp.pt (M.F.); cmm@isep.ipp.pt (C.D.-M.)

**\*** Correspondence: han@isep.ipp.pt

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** A voltammetric immunosensor was developed to quantify a major peanut allergen, Ara h 1, using screen-printed carbon electrodes (SPCE) as transducers. A sandwich-type immunoassay was performed on nanodiamond-coated SPCEs using an alkaline phosphatase-labeled detection antibody and a mixture containing an enzymatic substrate (3-indoxyl phosphate) and silver nitrate. The immunological interaction was detected through the (linear sweep) voltammetric stripping of the enzymatically deposited silver. The immunosensor's applicability was evaluated by analyzing breakfast cereals, cookies, and energy and cereal bars. Ara h 1 was successfully tracked in these commercial food products.

**Keywords:** peanut allergy; Ara h 1; food allergy; electrochemical immunosensor; screen-printed carbon electrode; nanodiamond

#### **1. Introduction**

Peanuts are integrated into the Mediterranean dietary pattern, and their consumption has been recommended worldwide. Despite its noteworthy nutritional value, reported cases of peanut allergy have increased, and therefore commercial food tracking is essential since in extreme cases peanut intake causes anaphylaxis [1].

Detection of peanut traces in food samples can be achieved using electrochemical immunosensors that benefit from their advantageous features such as rapid detection and high selectivity and sensitivity [2]. Because SPCEs can be connected to portable devices, they can be used for in situ allergen analysis. Few electrochemical immunosensors were reported for the determination of Ara h 1: a sandwich-type gold nanoparticle-coated SPCE [3], a reagentless label-free single-walled carbon nanotube-based biosensor [4] and an impedimetric immunosensor using a gold electrode functionalized with 11-mercaptoundecanoic acid self-assembled monolayer [5].

Among the distinct carbon-based nanomaterials, nanodiamonds (NDs) have not yet been used in the analysis of allergens. Nevertheless, due to their 3D configuration efficient electrode nanostructuration can improve the analytical signal [6].

The present work reports the development of an electrochemical immunosensor for the analysis of the peanut allergen Ara h 1 using SPCEs/NDs. In a sandwich-type assay, the antibody–antigen interaction was detected through Linear Sweep Voltammetry (LSV).

#### **2. Materials and Methods**

#### *2.1. Materials and Solutions*

Electrochemical measurements were performed using an Autolab PGSTAT101 potentiostat–galvanostat controlled by the NOVA software package v.1.10 (Metrohm Auto-

**Citation:** Carvalho, A.; Freitas, M.; Nouws, H.P.A.; Delerue-Matos, C. A Voltammetric Nanodiamond-Coated Screen-Printed Immunosensor for The Determination of a Peanut Allergen in Commercial Food Products. *Chem. Proc.* **2021**, *5*, 10. https://doi.org/10.3390/ CSAC2021-10458

Academic Editor: Núria Serrano

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

lab). Screen-printed carbon electrodes (SPCE, DRP-110) and the connector to interface the electrodes (DRP-CAC) were supplied by Metrohm DropSens.

Albumin from bovine serum (BSA), 3-indoxyl phosphate (3-IP), nanodiamonds (NDs, nanopowder), nitric acid (HNO3), streptavidin-alkaline phosphatase (S-AP), silver nitrate and tris(hydroxymethyl)aminomethane (Tris) were purchased from Sigma-Aldrich. The capture antibody (CAb, anti-Ara h 1), purified natural Ara h 1 and the detection antibody (DAb; biotin, anti-Ara h 1) were obtained from Indoor Biotechnologies.

Solutions of BSA and CAb were prepared in Tris-HNO3 (0.1 M, pH 7.2, Tris buffer); Ara h 1, DAb, S-AP solutions were prepared in Tris buffer containing 1.0% BSA (m/V). The solution containing 3-IP and silver nitrate was prepared in Tris 0.1 M (pH 9.8 + Mg(NO3)2 <sup>2</sup> × <sup>10</sup>−<sup>2</sup> M). A Tris buffer (0.1 M, pH 8.5) was used to extract Ara h 1 from the food samples. The evaluation of the accuracy of the sensor's results was performed using a commercial ELISA kit (Indoor Biotechnologies).

#### *2.2. Methods*

The SPCEs were nanostructurated with NDs (0.10 ng/mL) and the CAb (10 μg/mL) was immobilized overnight. The electrochemical immunoassay consisted of the following incubation steps: (i) Ara h 1 standard solution/food sample extract (30 min); (ii) DAb (250× dilution, 60 min); (iii) S-AP (20,000× dilution, 30 min) and (iv) enzymatic reaction (3-IP (1.0 × <sup>10</sup>−<sup>3</sup> M) and silver nitrate (4.0 × <sup>10</sup>−<sup>4</sup> M), 20 min). Washing between the incubation steps was performed using Tris buffer. The electrochemical analysis of the deposited silver was carried out by Linear Sweep Voltammetry (LSV; voltammograms were recorded using the following parameters: potential range from −0.03 to +0.4 V, scan rate: 50 mV/s). A schematic representation of the sandwich-type immunosensor is presented in Figure 1a.

**Figure 1.** Nanodiamond-based voltammetric immunosensor. (**a**) Schematic representation of the immunosensor construction; (**b**) results obtained for NDs 1 mg/mL dispersed in DMF, DMSO and H2O. Experimental parameters: CAb 10 μg/mL, Ara h 1 (0 and 250 ng/mL), DAb 250<sup>×</sup> dilution, S-AP 200,000<sup>×</sup> dilution, 3-IP (1.0 <sup>×</sup> <sup>10</sup>−<sup>3</sup> M), Ag+ (4.0 <sup>×</sup> <sup>10</sup>−<sup>4</sup> M).

A set of food products was bought in local supermarkets. The extraction procedure was performed as recommended by the Ara h 1 standard supplier. Briefly, 1 g of the food

sample was mixed with 10 mL of the extraction buffer, vortexed for 5 s, incubated for 15 min at 60 ◦C, centrifuged at 2500 rpm for 20 min and stored at −20 ◦C until use.

#### **3. Results and Discussion**

#### *3.1. Optimization of the Experimental Parameters*

NDs were used for the SPCE's nanostructuration. Several solvents (DMF, DMSO and H2O) that are typically employed for the dispersion of carbon-based nanomaterials were tested. The obtained *i*<sup>p</sup> values are presented in Figure 1b, and the signal-to-blank (S/B) ratio in the presence and absence of Ara h 1 (0 and 250 ng/mL) was used to select the best solvent. As can be observed, the dispersion of NDs in H2O provided the optimum condition to proceed the studies. Then, several ND concentrations were tested, from 1.0 to 0.03 mg/mL, and the best performance was obtained for 0.10 mg/mL.

The electrode surface was biofunctionalized with CAb and distinct concentrations between 5.0 and 25 μg/mL were studied. A 10 μg/mL concentration was selected and used to study the adequate DAb dilution (tested range: from 250× to 1000×). In this case a 250× dilution was found to provide the best performance.

Additionally, several dilutions of the streptavidin-alkaline phosphatase conjugate (S-AP) were tested, from 100,000× to 250,000×, and the selected value was 200,000×. To conclude the optimization process and reduce the assay time, the antigen incubation time was tested (30 min and 60 min), and it was verified that a 30 min incubation allowed the appropriate detection of the allergen.

#### *3.2. Analytical Performance*

The analytical responses toward different Ara h 1 concentrations using the nanostructured SPCE/NDs were evaluated. A linear concentration range was established between 25.0 and 500 ng/mL (*i*<sup>p</sup> = (0.027 ± 0.001) [Ara h 1] + (1.41 ± 0.31), r = 0.994, n = 5) with a sensitivity of 0.342 <sup>μ</sup>A·mL·ng−1·cm−2. The limits of detection (LOD) and quantification (LOQ) were 0.78 and 2.6 ng/mL, respectively (calculated using the equations: LOD = 3 Sblank/m and LOQ = 10 Sblank/m where Sblank is the standard deviation of the blank signal and m is the slope of the calibration plot).

#### *3.3. Precision, Recovery and Stability Studies*

The precision of the results provided by the immunosensor was tested using different electrodes on distinct days. A 250 ng/mL Ara h 1 solution was analyzed in triplicate and a relative standard deviation (RSD) of 7.3% was obtained.

To evaluate the accuracy of the results, recovery studies were performed using spiked cookie samples. The result for three replicates of added 250 ng/mL was found to be 75%.

The storage stability of the optimized SPCE/NDs platform was evaluated during several weeks using 0 and 250 ng/mL Ara h 1 solutions. It was verified that the sensing phase was stable for up to two weeks.

#### *3.4. Applicability and Method Validation*

The Ara h 1 content in raw peanuts of unknown variety was quantified. The obtained amount (4.29 ± 0.16 mg/g) was in accordance with previously reported results [3].

The immunosensor's applicability was evaluated by analyzing several commercial products: (1) cereal bar (no peanut); (2) energy bar containing peanut; (3) cookie that "may contain peanut"; (4) granola that "may contain peanut"; (5) pineapple cookie containing 8% of peanut. Examples of the obtained LSV voltammograms are shown in Figure 2a. The results were compared with the ones obtained using a commercial ELISA kit (Figure 2b). The good correlation indicated the accuracy of the results. The results of these analyses are presented in Table 1.

**Figure 2.** Analysis of food products. (**a**) LSV voltammograms (solid lines—presence of Ara h 1; dashed lines—absence of Ara h 1); (**b**) correlation between the obtained results for the analysis of food products using the developed immunosensor and the commercial ELISA kit. (1) Cereal bar (no peanut); (2) energy bar containing peanut; (3) cookie that "may contain peanut"; (4) granola that "may contain peanut"; (5) pineapple cookie containing 8% of peanut.

**Table 1.** Results of the quantification of Ara h 1 (mg/g) in food products using an ELISA kit and the developed voltammetric immunosensor.


\* ND: not detected.

Ara h 1 was not detected in the (1) cereal bar (no peanut) and the (3) cookie that "may contain peanut". On the latter product's label the warning may intend to protect the producer and consumers due to possible line-production cross-contaminations. On the other hand, the presence of Ara h 1 was confirmed and quantified in the following products: the (2) energy bar containing peanut (0.37 ± 0.05 mg/g), (4) granola that "may contain peanut" (0.15 ± 0.01 mg/g) and the (5) pineapple cookie containing 8% of peanut (0.75 ± 0.01 mg/g).

#### **4. Conclusions**

A nanodiamond-coated SPCE immunosensor was developed to track the major peanut allergen Ara h 1 in commercial food products. Within a total assay time of 2 h 20 min, a limit of detection (LOD) of 0.78 ng/mL was achieved. A set of breakfast products were analyzed and the presence and/or absence of Ara h 1 was confirmed and quantified in the peanut-containing products.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/CSAC2021-10458/s1.

**Author Contributions:** Conceptualization, M.F. and H.P.A.N.; methodology, M.F. and H.P.A.N.; validation, H.P.A.N. and C.D.-M.; formal analysis, M.F. and H.P.A.N.; investigation, A.C. and M.F.; resources, H.P.A.N. and C.D.-M.; data curation, M.F.; writing—original draft preparation, A.C. and M.F.; writing—review and editing, H.P.A.N.; visualization, M.F.; supervision, M.F. and H.P.A.N.; project administration, H.P.A.N.; funding acquisition, H.P.A.N. and C.D.-M. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the European Union (FEDER funds through COMPETE POCI-01-0145-FEDER-030735) and National Funds (Fundação para a Ciência e a Tecnologia) through the project PTDC/QUI-QAN/30735/2017—TracAllerSens—Electrochemical sensors for the detection and quantification of trace amounts of allergens in food products.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** This work was also supported by Portuguese national funds (FCT/MCTES, Fundação para a Ciência e a Tecnologia and Ministério da Ciência, Tecnologia e Ensino Superior) through the project UIDB/50006/2020.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Thermal Desorption of Explosives Vapour from Organic Fluorescent Sensors †**

**Edward B. Ogugu, Ross N. Gillanders and Graham A. Turnbull \***

Organic Semiconductor Centre, School of Physics & Astronomy, SUPA, University of St Andrews, Fife KY16 9SS, UK; ebo3@st-andrews.ac.uk (E.B.O.); rg89@st-andrews.ac.uk (R.N.G.)

**\*** Correspondence: gat@st-andrews.ac.uk

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** Organic semiconductors can be used as highly sensitive fluorescent sensors for the detection of trace-level vapours of nitroaromatic explosives. This involves fluorescence quenching of the sensors and indicates the presence of explosives in the surrounding environment. However, for many organic fluorescent sensors, the quenching of fluorescence is irreversible and imposes a limitation in terms of the reusability of the sensors. Here, we present a study of thermal desorption of 2,4-DNT from thin-film explosives sensors made from the commercial fluorescent polymers Super Yellow and poly(9-vinyl carbazole). Thermal cycling of the sensor results in recovery of fluorescence, thereby making them reusable.

**Keywords:** organic semiconductors; nitroaromatic explosives; fluorescence quenching; thermal desorption

**Citation:** Ogugu, E.B.; Gillanders, R.N.; Turnbull, G.A. Thermal Desorption of Explosives Vapour from Organic Fluorescent Sensors. *Chem. Proc.* **2021**, *5*, 11. https:// doi.org/10.3390/CSAC2021-10559

Academic Editor: Ling Zang

Published: 1 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Detection of explosives is critical for homeland security, humanitarian demining, and military operations. Various detection technologies [1] are being used complementarily to mitigate explosive hazards which may arise from terrorist attacks. For example, X-ray [2] can be used in conjunction with other sensing technologies, such as ion mobility spectroscopy [3], canines for airport security [4], or radar imaging together with metal detectors for demining operations [5].

Most of these technologies are either expensive and bulky, or complex, requiring a wellexperienced operator for optimum performance. Organic semiconductors are promising when used as explosives sensors [6–8], as they can be used to design thin film explosive sensors that be easily fabricated by solution processing. Interestingly, the sensors are comparably portable, cheaper [9,10], and can detect trace vapours of explosive molecules with high sensitivity and fast response time [11,12].

When trace-level vapours of explosives encounter an organic fluorescent sensor, molecules from the vapour are absorbed into the film and modify its light-emitting properties [13,14]. Specifically, an electron is transferred from a photogenerated exciton in the sensor to a sorbed nitroaromatic molecule, which results in fluorescence quenching and indicates the presence of explosives in the surrounding environment. The response to ppb levels of explosives is very rapid; however, for many organic fluorescent sensors, the quenching of fluorescence is irreversible or has slow reversibility [15], which makes them single-use sensors and imposes a limitation in terms of reusability.

It is crucial to have sensors that are reusable, especially in security or military operations. This implies the sensors can recover their fluorescence after exposure to analytes, thereby minimising the number of sensors that would be carried out for operations. There are limited data in the literature showing fluorescence recovery of polymer films after quenching due to explosives vapours [10,12,16]. Zhoa and Swager [16] showed that poly (p-phenylene ethynylene) (PPEs) films can rapidly recover their fluorescence after exposure

to DNT and TNT vapours. The measurement was made using a commercially available explosives trace detector called FIDO [11]. In the same experiment, sensory films made from poly(p-phenylenebutadiynylene)s (PPDs) showed much slower recovery, which was attributed to strong binding interactions between the analytes and polymer films.

A promising approach to obtain a reusable organic fluorescent sensor is the application of heat to the fluorescent film, which can thermally desorb the sorbed analytes and result in recovery of the fluorescence [17,18]. Tang et al. [18] showed the thermal desorption of various analytes from carbazole dendrimers films. The heating of the films from 40 ◦C to 80 ◦C under the flow of nitrogen resulted in almost full recovery of the sensors' fluorescence.

Here, we present a study of thermal desorption of 2,4-DNT from thin-film explosives sensors made from the commercial fluorescent polymer Super Yellow (SY). Thermal cycling of the sensor results in a recovery of fluorescence, thereby making them reusable, while additionally providing a route to confirm that the fluorescence quenching arises from the analyte response. To optimise the performance of the sensors in terms of reusability, Super Yellow sensors and blended Super Yellow–Poly(9-vinyl carbazole) (PVK) sensors were fabricated. The sensors were exposed to 2,4-DNT vapour in a custom-made chamber while monitoring their fluorescence, before being heated to desorb the DNT molecules from the sensors. Finally, an improvement of the sensors made from the polymer blend and the effect of temperature on these sensors are discussed. This method can be applied to other organic fluorescent sensors, removing the limitation of single-use sensors.

#### **2. Materials and Methods**

The fluorescent polymers Super Yellow (SY) and poly(9-vinyl carbazole) (PVK), and the solvents used were purchased from Sigma Aldrich (Merck) and were used without further purification. See Figure 1 for the chemical structure of the polymers.

**Figure 1.** Chemical structures of (**a**) Super Yellow (SY) and (**b**) Poly (9-vinyl carbazole) PVK.

#### *2.1. Sample Preparation*

Thin films for sensing were fabricated by first preparing SY and PVK solutions by dissolving in chlorobenzene at 6.5 mg/mL and 20 mg/mL, respectively. The solutions were left on a magnetic stirrer for 24 h at room temperature. For the polymer blend, appropriate mass ratios were measured from the prepared polymer solutions and left for another 24 h on the stirrer for proper mixing of the blend. Thin films for sensing were made by spincoating the prepared solutions on 25 mm<sup>2</sup> glass substrates at 2000 rpm for 60 s, both for the SY sensor and the blend of SY–PVK. Before spin-coating, the substrates were ultrasonically cleaned in Hellmanex III solution, DI-water three times, acetone and isopropanol for 20, 5 × 3, 10, and 10 min, respectively, dried in a nitrogen stream, and then plasma ashed in a 100% oxygen plasma (Plasma Technology MiniFlecto) for 3 min. For optical absorbance measurements, fused quartz windows of 12 mm diameter (from UQG optics) were used as substrates.

#### *2.2. Film Characterisation*

Optical absorption measurements of the thin films were made using a Cary 300 UVvis spectrophotometer, and an Edinburgh Instruments FLS980 fluorimeter was used the

measure emission spectra. Photoluminescence Quantum Yield (PLQY) measurements were performed in an integrating sphere [19], using a Hamamatsu Photonics C9920-02 measurement system with 444 nm and 340 nm excitation wavelengths. The thickness measurement was made using an Ellipsometer (J. A Woollam M2000U).

#### *2.3. Explosives Vapours Sensing*

A custom design vacuum-tight chamber (made of stainless steel) was used for the sensing experiment. The setup for the experiment is the same as that used in our previous work [20], with a slight modification—the sensors were placed in contact with the heater. Excitation of the films was performed using 405 nm continuous wave laser light from a diode laser (Photonic Solutions) after attenuation of the power to 16 μW. Photoluminescence from the sensors was measured using a fibre-coupled CCD spectrometer, taking a measurement every 3 s for 300 s. Explosives vapours were generated using the setup as shown in [21] and nitrogen was used as the carrier gas, and at a flow rate of 6 mL/min. To check for fluorescence recovery, nitrogen gas was used to flush the analyte-exposed sensors, or the sensors heated to 90 ◦C, followed by a flow of nitrogen gas to flush out the desorbed analytes from the chamber.

#### **3. Results and Discussion**

#### *3.1. Photophysical Characterisation*

Figure 2a shows the optical absorption of SY, PVK, and a blend of SY and PVK at a 25% to 75% mass ratio. PVK is transparent in the visible wavelength region, SY absorbance peaks at about 443 nm, while the blend retains the properties of both SY and PVK. To understand whether there is a transfer of energy from the PVK to SY molecules in the polymer blend or the blend films phase-separated into regions of SY and PVK, PL measurements of the films were measured (Figure 2b). The films were excited at 340 nm, in a region where both PVK and SY absorb light. PL emissions from both SY and PVK in the blends indicate that there is incomplete transfer of energy between PVK and SY, and there is some level of phase separation of regions of SY and PVK. A blue shift is seen in the peak emission of SY in the blend, which can be attributed to a separation of the SY molecules by the PVK matrix [22].

**Figure 2.** (**a**) Optical absorption of SY, PVK, and a blend of SY and PVK at 25% to 75% mass ratio, respectively; (**b**) Fluorescence spectra of various concentrations of SY–PVK blend at 340 nm excitation wavelength.

#### *3.2. Fluorescence Quenching and Thermal Release of DNT from Sensors*

Sensing of DNT vapour using 90 nm thin films of SY and thermal desorption to recover the fluorescence of the quenched SY sensors was demonstrated (Figure 3a). For reference (black line), the PL of the sensor (when only exposed to clean nitrogen gas) remains stable throughout the 300 s. The red line shows the fluorescence from the film when exposed to DNT vapour; fluorescence was collected in clean nitrogen for 60 s, then DNT vapour

was introduced in a nitrogen stream (point 1) at 6 mL/min and left to flow for 60 s before cutting off the flow (point 2).

**Figure 3.** Fluorescence quenching due to DNT vapour and thermal desorption of DNT from sensors made of (**a**) SY film, and (**b**) blend of SY and PVK at 24.5% and 75.5% mass ratio, respectively. Key (Red): 1 = DNT flow on; 2 = DNT flow off and clean nitrogen flow on. Key (Blue): 1 = DNT flow on; 2 = DNT flow off; 3 = heat on; 4 = heat off and clean nitrogen flow on.

Although the source of the DNT vapour was turned off and the system flushed with clean nitrogen, the fluorescence continued to decrease, suggesting that there was a continuing diffusion of DNT vapour into the film [14,21] and that there was a strong binding interaction between the DNT molecules and the SY film [17]. An increase in temperature can weaken the binding strength and release the DNT molecules from the thin film. The blue line shows the thermal desorption of DNT molecules from SY films after fluorescence quenching due to DNT vapour exposure. At point 3, the nitrogen gas flow was temporarily stopped and a heating element was turned on (point 3), which ramped up the temperature of the film from room temperature to 90 ◦C (point 4). This resulted in an initial thermal degradation [23], then a turning point at around 180 s. We attribute this change to the point when DNT molecules started desorbing from the film, as fluorescence recovery can be seen. When the nitrogen flow was turned back on (point 4), a further increase in the fluorescence was observed. However, the fluorescence could not reach the reference baseline, this may be due either to thermal degradation or there might be some residual DNT molecules left in the film [17].

To optimise the sensor in terms of thermal stability, films fabricated using a blend of SY and PVK were used for similar DNT vapour sensing—see Figure 3b. PVK was chosen because it has a high glass transition temperature and may likely improve the thermal stability of SY sensors [24]. Thermal desorption of the sorbed DNT molecules (point 3 to 4) resulted in a much higher recovery of fluorescence, almost reaching the reference baseline. An investigation of the complex processes during the temperature ramp is in progress.

#### **4. Conclusions**

We have shown that commercially available conjugated polymer Super Yellow (SY) can be used as a highly sensitive and reusable sensor for nitroaromatic explosives. An increase in the temperature of the sensors weakens the analyte binding interaction and allows the sorbed analytes to diffuse out of the thin film, which results in the recovery of the PL of the sensors. To optimise the sensors, the high thermally stable polymer PVK was blended with SY, which showed an improvement during thermal desorption of the analytes when resetting the sensors. This method can be applied to other organic fluorescent sensors, removing the limitation of single-use sensors.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/CSAC2021-10559/s1.

**Author Contributions:** Conceptualisation E.B.O., R.N.G., and G.A.T.; methodology, E.B.O., R.N.G., and G.A.T.; software, E.B.O.; formal analysis, E.B.O., R.N.G., and G.A.T.; investigation, E.B.O.; data curation, E.B.O.; writing—original draft preparation, E.B.O.; visualization, E.B.O.; writing—review and editing, R.N.G. and G.A.T.; funding acquisition, R.N.G. and G.A.T.; supervision, G.A.T.; project administration, G.A.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Commonwealth Scholarships Commision, and the NATO Science for Peace & Security Programme under grant agreement MYP G5355.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** The research data underpinning this publication can be accessed at https://doi.org/10.17630/b573f7de-553f-4ec4-b907-bfdb62584fc9.

**Acknowledgments:** We would like to acknowledge useful scientific discussions with I. D. W. Samuel.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Ternary Oxidized Carbon Nanohorns/TiO2/PVP Nanohybrid as Sensitive Layer for Chemoresistive Humidity Sensor †**

**Bogdan-Catalin Serban 1,2,\*, Octavian Buiu 1,2,\* , Marius Bumbac 3,4,\* , Roxana Marinescu 1,2, Niculae Dumbravescu 1,2, Viorel Avramescu 1, Cornel Cobianu 1,2,5 , Cristina Mihaela Nicolescu <sup>4</sup> , Mihai Brezeanu 6, Cristiana Radulescu 3,4 and Florin Comanescu <sup>1</sup>**


**Abstract:** The relative humidity (RH) sensing response of a chemoresistive sensor using a novel ternary hybrid nanocomposite film as a sensing element is presented. The sensitive layer was obtained by employing the drop-casting technique for depositing a thin film of nanocomposite between the electrodes of an interdigitated (IDT) structure. The sensing support structure consists of an IDT dual-comb structure fabricated on a oSi-SiO2 substrate. The IDT comprises chromium, as an adhesion layer (10 nm thickness), and a gold layer (100 nm thickness). The sensing capability of a novel thin film based on a ternary hybrid made of oxidated carbon nanohorns–titanium dioxide– polyvinylpyrrolidone (CNHox/TiO2/PVP) nanocomposite was investigated by applying a direct current with known intensity between the two electrodes of the sensing structure, and measuring the resulting voltage difference, while varying the RH from 0% to 100% in a humid nitrogen atmosphere. The ternary hybrid-based thin film's resistance increased when the sensors were exposed to relative humidity ranging from 0 to 100%. It was found that the performance of the new chemoresistive sensor is consistent with that of the capacitive commercial sensor used as a benchmark. Raman spectroscopy was used to provide information on the composition of the sensing layer and on potential interactions between constituents. Several sensing mechanisms were considered and discussed, based on the interaction of water molecules with each component of the ternary nanohybrid. The sensing results obtained lead to the conclusion that the synergic effect of the p-type semiconductor behavior of the CNHox and the PVP swelling process plays a pivotal role in the overall resistance decrease of the sensitive film.

**Keywords:** oxidized carbon nanohorns (CNHox); titanium (IV) oxide (TiO2); polyvinylpyrrolidone (PVP); chemoresistive humidity sensor; swelling

**Citation:** Serban, B.-C.; Buiu, O.; Bumbac, M.; Marinescu, R.; Dumbravescu, N.; Avramescu, V.; Cobianu, C.; Nicolescu, C.M.; Brezeanu, M.; Radulescu, C.; et al. Ternary Oxidized Carbon Nanohorns/TiO2/PVP Nanohybrid as Sensitive Layer for Chemoresistive Humidity Sensor. *Chem. Proc.* **2021**, *5*, 12. https://doi.org/10.3390/ CSAC2021-10616

Academic Editor: Nicole Jaffrezic-Renault

Published: 6 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Relative humidity (RH) sensors have received increasing attention in the last decades due to their importance in many areas of daily life, such as HVAC (heating, ventilation, and air conditioning), food storage, biomedical, climatology, structural health monitoring, agriculture, microelectronics and so forth [1]. The sensing principle, fabrication technology, temperature operating capability, and the sensitive layer play a cardinal role in obtaining superior sensing performances [2]. Due to their excellent sensing properties toward water molecules, abundance and ease of manufacture, low cost, tunable electric properties, and ability to operate under adverse conditions, semiconducting metal oxides (SMOX) have emerged as promising candidates for sensing humidity with high accuracy [3]. TiO2 is one of the most-used SMOXs and has received increased attention in the last decades due to its fast, linear and sensitive response towards RH changes [4].

At the same time, a lot of recently reported work focused on using carbon-based nanomaterials as sensitive layers within the design of humidity sensors [5]. Among these, in the last years, oxidized carbon nanohorns—single—graphene tubules with oxygen functionalities, mostly carboxylic groups (CNHox) and their nanocomposites, have been extensively explored and have proven to be an attractive option [6–10]. Interestingly, the oxidized carbon nanohorns–TiO2 nanohybrid was recently used for enhanced photocatalytic hydrogen production [11].

This paper presents, for the first time to our knowledge, the synthesis and characterization of a film based on a ternary nanocomposite comprising oxidized carbon nanohorns– titania–polyvinylpirrolidone CNHox/TiO2/PVP at 2/1/1 w/w/w ratio. Furthermore, the room temperature RH sensing response of a resistive sensor employing the synthesized sensing film was investigated.

#### **2. Materials and Methods**

#### *2.1. Materials*

All the materials used in the experiments described below were purchased from Sigma Aldrich (Redox Lab Supplies Com, Bucharest, Romania). CNHox (structure shown in Figure 1a) is characterized by diameters between 2 and 5 nm, lengths between 40 and 50 nm, and a specific surface area around 1300–1400 m2/g. PVP, with the structure depicted in Figure 1b, has an average molar weight of 40,000 Da. TiO2 is a nanometric powder (averaged particle size lower than 25 nm), while isopropyl alcohol, (CH3)2CHOH, is a solution 70% w/w in water.

**Figure 1.** The structure of: (**a**) CNHox; (**b**) PVP.

*2.2. Synthesis of the Ternary Hybrid Nanocomposite Sensing Films and Experimental Setup*

The synthesis of the sensitive film based on the ternary nanohybrid CNHox/TiO2/PVP at 2/1/1 w/w/w ratios is described below.

PVP powder (2 mg) was dissolved in 5 mL isopropyl alcohol solution (70% w/w in water) and subjected to stirring in an ultrasonic bath for one hour at room temperature (RT). CNHox (4 mg) was added to this solution, followed by stirring in the ultrasonic bath for

three hours at room temperature. An amount of 2 mg TiO2 nanopowder was added to the previous dispersion, and continuous stirring was performed in the ultrasound bath for 6 h at RT. The dispersions' homogenization was achieved by employing a mild sonication bath (FS20D Fisher Scientific, Dreieich, Germany) at 42 kHz (output power 70 W). This treatment facilitated a relatively uniform distribution of the CNHox and TiO2 in the PVP network. The film was annealed in a two-step sequence according to the following procedure:


Using the drop-casting method, the sensitive film was obtained by depositing the dispersion of CNHox/TiO2/PVP in isopropanol solution over the IDT sensing structure (contact area being masked).

The sensing device consists of a metallic IDT dual-comb structure fabricated on a Si substrate (470 μm thickness) covered by SiO2 (as passivation layer, 1 μm thickness) (Figure 2). The IDT's metal stripes were made by successive deposition of 10 nm chrome (Cr) and 100 nm gold (Au). The sensor has dimensions of 20 × 11 mm<sup>2</sup> [6,7,10].

**Figure 2.** This is a figure. Schemes follow the same formatting.

Raman spectra of the sensing layer were acquired by Lab Ram HR 800 Raman spectrometer (Horiba Europe, Antwerp, Belgium), using a He-Ne laser excitation (633 nm).

Surface topography of the sensing films based on the TiO2/CNHox/PVP ternary nanohybrid was investigated by scanning electron microscopy (SEM-Nova NanoSEM 630, Thermo Scientific, Waltham, MA, USA). For surface visualization, a field emission gun scanning electron microscope/FEG-SEM-Nova NanoSEM 630 (Thermo Scientific, Waltham, MA, USA) (F.E.I.), with superior low voltage resolution and high surface sensitivity imaging, was used.

The RH sensing measurements were conducted in a suitable experimental setup (Figure 3). The dry nitrogen was purged through two vessels in series containing deionized water for varying the RH in the testing chamber from 0% to 100% RH.

**Figure 3.** Experimental setup employed for RH measurements.

The experimental chamber included a tandem of sensors: the resistive sensing structure (abbreviated as DUT—device under testing, Figure 3) using the CNHox/TiO2/PVPbased sensitive film and a capacitive RH commercial sensor (abbreviated as REF, Figure 3). During all measurements, the RH was continuously monitored by the REF sensor. A Keithley 6620 current source (Keithley Instruments GmbH, Germering, Germany), providing current variation between 0.01 and 0.1 A, was employed. The data were collected and analyzed with a PicoLog data logger (PICO Technology, Neots, Cambridgeshire, UK). All experiments were conducted at the ambient temperature [6,7,10].

#### **3. Results**

#### *3.1. Raman Spectroscopy*

The interaction between CNHox, TiO2, and PVP was proven using Raman spectroscopy. Figure 4 shows four Raman spectra recorded in four different positions of the CNHox/TiO2/PVP = 2/1/1 (w/w/w) film, plotted in red, gray, green, and blue color. It can be observed that three active Raman bands (D, G, 2D) were recorded at the wavenumbers of 1318.3, 1591.9, and 2627.8 cm<sup>−</sup>1, which confirm the presence of the nanocarbonic material (CNHox). One can also identify specific TiO2 bands as follows: Eg mode at 149.1 cm−<sup>1</sup> (very sharp and intense), B1g at 397.6 cm<sup>−</sup>1, A1g at 513.9 cm−1, and Eg at 634.7 cm<sup>−</sup>1. The peaks associated with PVP are undetectable, most probably being covered by CNHox. The shift of the Raman peak positions of both CNHox and TiO2 within the ternary nanohybrid compared to the Raman peak positions of each of the two materials considered separately is the most interesting result shown in Figure 4. A plausible explanation for this result can be related to the hydrogen bonds formed between all the components of the synthesized ternary nanohybrid.

**Figure 4.** Raman spectra recorded in four different positions of the film deposited from the ternary nanocomposite CNHox/TiO2/PVP.

#### *3.2. SEM Analysis*

Scanning electron micrographs show that the surface morphology of the coating mixture is relatively homogenous in all the cases (Figure 5).

**Figure 5.** Scanning electron micrographs of the TiO2/CNHox/PVP at X150,000 magnification.

#### *3.3. RH Monitoring Capability of the Ternary Hybrid Nanocomposite*

The RH sensing capability of the ternary hybrid nanocomposite-based sensing layer was investigated by applying a current between the two electrodes and measuring the voltage difference when varying the RH from 0% to 100%. A notable characteristic of these sensors is low power consumption, below 2 mW. The behavior of the manufactured sensors is presented in Figure 6.

**Figure 6.** The response of sensor: "RH curve-blue" presented as a function of time for three measurement cycles, when relative humidity was increased in ten steps from 0% RH to 100% RH; "RH curve-red" shows the characteristic measured for a commercial, capacitive sensor.

The resistance of the ternary hybrid-based thin film increases when RH increases. The ternary nanohybrid-based resistive sensors' overall linearity—in humid nitrogen when varying RH from 0% to 100%—is very good, as shown in Figure 7.

An important parameter, such as response time (in seconds), was estimated for both manufactured and commercial RH sensors (Figure 8). It has been observed that, between 10 and 70% RH, comparable response times for either DUT or REH are measured. Differences appear at an RH below 10% and higher than 70%.

**Figure 7.** The transfer function of the quaternary nanohybrid-based resistive sensors in humid nitrogen (RH = 0–100%).

**Figure 8.** Response time of the tested sensor variation for relative humidity jumps.

#### *3.4. Analysis of the Sensing Mechanism*

The most plausible sensing mechanism considers the *p*-type semiconducting material properties of CNHox and the swelling of the hydrophilic polymer. At the interaction with oxidized carbon nanohorns, H2O molecules donate their electron pairs, decreasing the number of holes in the nanocarbonic materials. Thus, the ternary nanohybrid-based sensing film becomes less conductive. In the same line, the swelling of the hydrophilic polymer PVP increases the distance between the CNHox particles and diminishes the electrically percolating pathways. However, the interaction of water molecules with the surface of TiO2 yields protonic conduction (Grotthuss mechanism), which should increase the sensing film's conduction. Without completely excluding the later mechanism, one can come to the conclusion that the first two effects prevail and play a pivotal role in the overall increasing resistance of the sensitive film.

#### **4. Conclusions**

The RH sensing response of a resistive detector using sensing layers based on a ternary hybrid nanocomposite of CNHox/TiO2/PVP (2/1/1) was reported. The novel sensitive film used within the design of the chemoresistive sensor exhibited an RT response comparable to that of a commercial capacitive humidity sensor. The ternary nanohybridbased resistive sensors' overall linearity—in humid nitrogen when varying RH from 0% to 100%—was shown to be excellent. The estimated response times were comparable to those of the commercial sensor. Several sensing mechanism hypotheses were discussed according to the possible chemical interaction between oxidized carbon nanohorn, titania, PVP, and water molecules. Although the Grotthuss mechanism cannot be excluded, the hole conduction ability of CNHox in conjunction with the swelling of hydrophilic polymer prevails and leads to the overall decreasing conduction of the sensing films.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/CSAC2021-10616/s1.

**Author Contributions:** Conceptualization, B.-C.S., C.C. and O.B.; methodology, B.-C.S., C.C., V.A., N.D. and O.B.; software, M.B. (Marius Bumbac), C.M.N. and M.B. (Mihai Brezeanu); validation, B.-C.S., C.C., N.D., O.B. and M.B. (Marius Bumbac); investigation, N.D., B.-C.S., V.A., C.C., R.M. and F.C.; resources, O.B.; writing—original draft preparation, B.-C.S., C.C., O.B. and M.B. (Marius Bumbac); writing—review and editing, all co-authors; visualization, O.B.; supervision, B.-C.S., C.C. and M.B. (Marius Bumbac); project administration, B.-C.S. and O.B.; funding acquisition, C.R., O.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** The authors would like to acknowledge the financial support provided by: the Romanian Ministry of Research and Education, via the "Nucleu Program" called "MICRO-NANO-SIS PLUS", grant number P.N. 19 16, UEFISCDI contract number 364PED—23 October 2020.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Abstract* **Eucalyptus Biochar as a Sustainable Nanomaterial for Electrochemical Sensors †**

**Annalisa Scroccarello 1,\*, Flavio Della Pelle <sup>1</sup> , Qurat Ul Ain Bukhari 1, Filippo Silveri 1, Daniele Zappi 2, Enrico Cozzoni <sup>3</sup> and Dario Compagnone <sup>1</sup>**



**Citation:** Scroccarello, A.; Pelle, F.D.; Bukhari, Q.U.A.; Silveri, F.; Zappi, D.; Cozzoni, E.; Compagnone, D. Eucalyptus Biochar as a Sustainable Nanomaterial for Electrochemical Sensors. *Chem. Proc.* **2021**, *5*, 13. https://doi.org/10.3390/ CSAC2021-10618

Academic Editor: Elisabetta Comini

Published: 6 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Abstract:** Carbonaceous-based nanomaterials (C-NMs) are the pillar of myriad sensing and catalytic electrochemical applications. In this field, the search for environmentally sustainable C-NMs from renewable sources became a duty in the development of nano-sensors. Herein, water-soluble carbon nanofibers (CF) were produced from eucalyptus scraps-based biochar (BH) through an ultrasound treatment, assisted by sodium cholate used as a stabilizing agent. Noteworthy, thanks to the use of the bio-stabilizing agent, the nanofibers were dispersed in water avoiding the use of organic solvents. The BH-CF was investigated as sensing material onto commercial screen-printed electrodes via dropcasting (BH-SPE) and as thin-film fully integrated into a lab-made flexible electrode. The thin film was produced via BH-CF vacuum filtration followed by the film transferring to a thermo-adhesive plastic substrate through thermal lamination. This approach gave rise to a conductive BH-CF film (BH-Film) easily embodied in a lab-made electrode produced with office-grade instrumentation (i.e., craft-cutter machine, thermal laminator) and materials (i.e., laminating pouches, stencil). The BH-CF amount was optimized and the resulting film morphologically characterized, then, the electrochemical performances were studied. The BH-CF electrochemical features were investigated towards a broad range of analytes containing phenol moieties, discrimination between orto- and mono-phenolic structures were achieved for all the studied compounds. As proof of applicability, the BH-CF-based sensors were challenged for simultaneous determination of mono-phenols and ortho-diphenols in olive oil extracts. LODs ≤ 0.5 μM and ≤ 3.8 μM were obtained for hydroxytyrosol (o-diphenol reference standard) and Tyrosol (m-phenols reference standard), respectively. Moreover, a high inter-sensors precision (RSD calibration-slopes ≤ 7%, *n* = 3) and quantitative recoveries in sample analysis (recoveries 91–111%, RSD ≤ 6%) were obtained. Here, a solvent-free strategy to obtain water-soluble BH-CF was proposed, and their usability to sensor fabrication and modification proved. This work demonstrated as cost-effective and sustainable renewable sources, rationally used, can lead to obtain useful nanomaterials.

**Keywords:** biochar; sensor; nanomaterial

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/CSAC2021-10618/s1.

### *Abstract* **Graphene Nanoflakes Incorporating Natural Phytochemicals Containing Catechols as Functional Material for Sensors †**

**Filippo Silveri \*, Flavio Della Pelle , Daniel Rojas and Dario Compagnone**

Faculty of Bioscience and Technology for Food, Agriculture and Environment University of Teramo, Via Renato Balzarini 1, 64100 Teramo, Italy; fdellapelle@unite.it (F.D.P.); jdrojastizon@unite.it (D.R.); dcompagnone@unite.it (D.C.)

**\*** Correspondence: fsilveri@unite.it

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** Phytochemical products start to be employed to assist 2D nanomaterials exfoliation. However, a lack of studies regarding the molecules involved and their capacity to give rise to functional materials is evident. In this work, a novel green liquid-phase exfoliation strategy (LPE) is proposed, wherein a flavonoid namely catechin (CT) exclusively assists the exfoliation of bulk graphite in conductive water-soluble graphene nanoflakes (GF). Physicochemical and electrochemical methods have been employed to characterize the morphological, structural, and electrochemical features of the GF-CT. Surprisingly, the obtained GF-CT integrates well-defined electroactive quinoid adducts. The resulting few-layers graphene flakes intercalated with CT aromatic skeleton ensure strict electrical contact among graphene sheets, whereas the fully reversible quinoid electrochemistry (ΔE = 28 mV, Ip, a/Ip, c = ~1) is attributed to the residual catechol moieties, which work as an electrochemical mediator. The GF-CT intimate electrochemistry is generated directly during the LPE of graphite, not requiring any modification or electro-polymerization steps, resulting in stable (8 months) and reproducible material. The electrocatalytic activity has been proven towards hydrazine (HY) and β-nicotinamide adenine dinucleotide (NADH), a pollutant and a coenzyme, respectively. High sensitivity in extended linear ranges (HY: LOD = 0.1 μM, L.R. 0.5–150 μM; NADH: LOD = 0.6 μM, L.R. 2.5–200 μM) at low overpotential (+0.15 V) was obtained using amperometry, avoiding electrodefouling. Improved performances, compared with graphite commercial electrodes and graphene exfoliated with a conventional surfactant, were obtained. The GF-CT was successfully used to perform the detection of HY and NADH (recoveries 94–107%, RSD ≤ 8%) in environmental and biological matrices, proving the material exploitability even in challenging analytical applications. On course studies aim to combine the intrinsic conductivity of the GF-CT with flexible substrates, in order to construct flexible electrodes/devices able to house GF-CT-exclusively composed conductive films. In our opinion, the proposed GF-CT elects itself as a cost-effective and sustainable material, particularly captivating in the (bio)sensoristics scenario.

**Keywords:** nanostructured-functional-material; grapheme; 2D-materials; mediator; phytochemicals; catechol-moieties; liquid-phase-exfoliation

**Supplementary Materials:** The following are available at https://www.mdpi.com/article/10.3390/ CSAC2021-10619/s1.

**Citation:** Silveri, F.; Pelle, F.D.; Rojas, D.; Compagnone, D. Graphene Nanoflakes Incorporating Natural Phytochemicals Containing Catechols as Functional Material for Sensors. *Chem. Proc.* **2021**, *5*, 14. https:// doi.org/10.3390/CSAC2021-10619

Academic Editor: Elisabetta Comini

Published: 6 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

### *Proceeding Paper* **Numerical and Experimental Modeling of Paper-Based Actuators †**

**Ashutosh Kumar , Hojat Heidari-Bafroui , Amer Charbaji , Nasim Rahmani, Constantine Anagnostopoulos and Mohammad Faghri \***

> Microfluidics Laboratory, Department of Mechanical, Industrial and Systems Engineering, University of Rhode Island, Kingston, RI 02881, USA; ashutosh@uri.edu (A.K.); h\_heidari@uri.edu (H.H.-B.); charbaji@uri.edu (A.C.); nara7@uri.edu (N.R.); anagnostopoulos@uri.edu (C.A.)

**\*** Correspondence: faghrim@uri.edu

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** Microfluidic paper-based analytical devices (μPADs) have witnessed a great extent of innovation over the past decade, developing new components and materials assisting the diagnosis of different diseases and sensing of a wide range of biological, chemical, optical, and electrochemical phenomena. The novel paper-based cantilever (PBC) actuator is one the major components that allows autonomous loading and control of multiple fluid reagents required for the accurate operation of paper-based microfluidic devices. This paper provides an extensive overview of numerical and experimental modeling of fluidically controlled PBC actuators for automation of the paper-based assay. The PBC model undergoing hygro-expansion utilizes quasi-static 2D fluid loaded structure governed by the Euler–Bernoulli beam theory for small and moderately large deflections. The solution for the model can avail the response of paper-based actuators for response deflection θ, within 0◦ to 10◦ under the assumption of insignificant cross-sectional deformation. The actuation of PBC obtained using a quasi-static theory shows that our results are consistent with quantitative experiments demonstrating the adequacy of models.

**Keywords:** paper-based cantilever; microfluidic analytical device; paper-based actuator

#### **1. Introduction**

The first usage of paper as a substrate material goes back to the early 1800s with litmus paper, the oldest form of pH indicator, for analytical testing of chlorine and carbonic oxide [1]. Developed by Müller and Clegg [2], the first microfluidics channel on filter paper was utilized to elute a mixture of pigment. However, in 2007, the Whiteside's Group of Harvard University gave a new push to the endless possibilities of paper-based microfluidics by introducing a patterned paper as a platform for portable devices [3]. Due to the several benefits of paper for making microfluidic paper-based analytical devices (μPADs), it has attracted extreme attention. Paper is a very cheap and renewable material since it is made of cellulose, the most abundant organic polymer on Earth, and it is also biocompatible and can be used for numerous biological and chemical applications [4,5]; thanks to capillary forces, an external force is not needed for fluid transport in paper.

Paper-based microfluidic devices are made up of different sections that serve different purposes. The simpler devices generally have a sample port, transport channels, reaction zones, and a detection zone. Devices that perform more complex reactions or enzymelinked immunosorbent assay (ELISA) protocols require proper sample timing and control as it flows into the different reaction zones. Such control is usually achieved by the use of a suitable valving system in the device.

Different valving systems have been developed for use in paper-based microfluidic devices. Some of these valving systems were simple, autonomous and required little-to-no

**Citation:** Kumar, A.; Heidari-Bafroui, H.; Charbaji, A.; Rahmani, N.; Anagnostopoulos, C.; Faghri, M. Numerical and Experimental Modeling of Paper-Based Actuators. *Chem. Proc.* **2021**, *5*, 15. https://doi.org/10.3390/ CSAC2021-10468

Academic Editor: Manel del Valle

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

operator input while others required some operator involvement or an external power source to operate. Li et al. [6] reported a method to stop and to promote wicking by manually separating and re-joining two paper channels using a sliding separator or a switch valve. Jahanshahi-Anbuhi et al. [7] utilize the switch valve, which they refer to as a flap, in a paper-based sensor for the detection of pesticides. Han et al. [8] built on the concept and created a 3D slip-PAD in which the operator connects the different fluidic channels by manipulation of the cartridge that holds the paper-based device. Martinez et al. [9] reported on a press valve in a multilayer paper-based device that had small gaps between the paper layers which were created by the finite thickness of the tape in between the layers. The operator would need to press the buttons to mechanically bridge the discrete channels to promote wicking. Rodriguez et al. [10] and Jayawardane et al. [11] utilized discarded separators in their devices that would be removed after a predetermined amount of time to connect appropriate fluidic channels. Noh et al. [12] and Lutz et al. [13] provided designs for metering valve systems that were based on paraffin wax and dissolvable sugar, respectively. While this valving system is cheap, controls the flow rate and requires no operator input, it suffers from the fact that it can only impede the flow rate of the sample without completely stopping it until it is required to move to the next section of the device. Chen et al. [14] and Gerbers et al. [15] developed novel autonomous two- and three-dimensional microfluidic valves involving no external actuation based on altering the hydrophobicity of a multilayered structure by means of a surfactant. With this technique, they were able to control the order and mixing time of the sample and multiple reagents autonomously. However, these valves required long response times and large volumes of actuation fluids. Lai et al. [16] provided a design for a timing valve in their device that consisted of a surfactant and a wax barrier to provide appropriate time delays to sequentially handle the multiple fluidic operations of the device. Koo et al. [17] used an electrowetting valve in which a dielectric material that is normally hydrophobic, is polarized and becomes hydrophilic when the valve is actuated with an applied potential difference. Li et al. [18] developed a magnetic valve with the use of a blend of ferromagnetic nanoparticles and polydimethylsiloxane that magnetized the paper cantilever. Although the operation of the magnetic valve was autonomous, it required an applied potential to actuate the cantilever. Phillips et al. [19] used a thin-film electric heater to thermally actuate a wax valve to pass or block the flow of the sample. Kong et al. [20] reported actuators based on selective wetting of folded paper strips. These strips reduced the actuator's response time to within two seconds from wetting, while utilizing a very small volume of actuation fluid in the order of four microliters. Toley et al. [21] developed a valving toolkit that utilized an expanding element, which was a compressed sponge valve, to control the flow of the fluid in the device. Fu et al. [22] developed a thermally actuated cantilever valve that utilizes a shape-memory polymer.

This paper provides an overview of the behavior of a fluidically controlled paperbased cantilever (PBC) for the automation of a paper-based assay that is exploited in a fluidic circuit to sequentially load several reagents to the analyte detection area. For this purpose, first a simple paper-based cantilever (PBC) will be modeled. Different scenarios for using the cantilever will then be presented. Finally, a mathematical model will be developed for better understanding of the model and for comparing with the experimental results.

#### **2. Methods**

A fluidic circuit is used to conduct complex immunoassay to sequentially load two or more additional reagents in addition to the sample fluid. Once a user pipettes a certain amount of sample fluid on the sample port of the paper-based cantilever, as the sample fluid transfers to the free end of the PBC due to the capillary force, and hence, the hygroexpansion of cellulose fibers, the cantilever starts to bend downward and contacts the stationary component within several seconds. This paper incorporates experimental, analytical and numerical methods for analysis and the results from different methods were compared to check for adequacy of the model.

#### *2.1. Materials*

The following materials were used in preparing, fabrication, and testing the cantilever valves used in this study. Filter paper (GE Healthcare Whatman grade 4—1004917 & grade 41—1441866), chromatography paper (GE Healthcare Whatman 1—3001878), deionized water, and food coloring (Wilton Icing Colors). The stationary component was a 1 × 1 cm chromatography paper. The dimensions of the cantilevers were using a vector graphics software (CorelDraw X6). The cantilevers were then cut out from paper, in the crossmachine direction, using a laser engraver (Epilog mini 40W). An 8-megapixel video camera with 30 frames per seconds capability was used to record the actuation of the cantilever valve. A media player (Avidemux) was used to playback the recording and collect the data.

#### *2.2. Modeling*

2.2.1. Concept of Design Single Cantilever Design

The design of the single cantilever device comprises a paper-based cantilever, reagent pad, stationary structure and a support structure. Figure 1 demonstrates the single cantilever design of PBC in two conditions (unloaded and loaded). The PBC remains in a normally open position with the separation distance 'd', until the sample fluid is loaded. Upon loading with sample fluid, the PBC absorbs the sample fluid though wicking and the actuator activates and results in deflection, as shown in Figure 1, resulting in the transfer of sample fluid to the reagent pad.

**Figure 1.** Single cantilever design of PBC actuator device.

#### Double Cantilever Design

In the double cantilever design, two cantilevers are utilized to allow for the sequential loading of reagents into an area of interest on the paper-based device. Figure 2 shows the diagram of the double cantilever design and Figure 3 shows the sequence of operation for this architecture. The operation of this device is fairly simple; first, the user just needs to pipette a certain amount of activation liquid (e.g., DI water) on the sample pad initiating the actuation of the cantilever on the right, shortly after the addition of the activation fluid, as seen in Figure 3b. This permits the flow of the reagent on the righthand side into the middle section of the device to react with any chemicals dried there. In the meantime, part of the activation fluid is flowing through the timing channel to the left and activates the second cantilever after a predetermined duration of time, as seen in Figure 3d. This connects both zones and thus enables the passage of the reagent on the left into the area of interest to react with the material there, as seen in Figure 3e.

**Figure 2.** Addition of activation fluid on sample pad for double cantilever design of PBC actuator device.

**Figure 3.** Sequence of operation of the double cantilever design. (**a**) Initial state of device after pipetting activation fluid, (**b**) Loading of the first reagent on-to detection zone upon activation of the first cantilever. (**c**) Activation fluid passing from the delay channel for actuation of the second cantilever. (**d**) Loading of the second reagent on-to the detection zone upon activation of the second cantilever. (**e**) Final state of the device after mixing of two reagents.

2.2.2. Experimental Model

A picture of the experiment is shown in Figure 4, consisting of PBC and a capillary tube to load the fluid and obtain the response deflection.

**Figure 4.** Experimental model for PBC actuator.

In order to measure the deflection of the PBC, Whatman grade 41 filter paper was selected. The filter paper was cut in the cross-machine direction with a 4 mm width and a 40 mm length using an Epilog Mini laser engraver. The picture of samples for PBC can be seen in Figure 5.

**Figure 5.** Paper-based cantilevers (PBCs).

A 2 mm in diameter capillary tube was used to introduce fluid into the PBC. A fixture for the position of PBC and capillary was designed and utilized to reduce the uncontrollable error of running experiments (Figure 6).

**Figure 6.** Fixture for experiment.

2.2.3. Mathematical Model

This paper models the response of PBC on the fluidic loading of the paper-based cantilever. The PBC undergoes hygro-expansion when exposed to fluid, which results in the actuation of the cantilever. The mathematical modeling of the PBC determines the response deflection, the Euler–Bernoulli beam theory will be used with the assumption that the cross-section remains normal to the axis of the cantilever deflection and that cross-sectional deformation is not significant.

#### Modeling of Flow in Paper

The capillary model is adapted to simulate the flow in paper for this experiment. This model will be utilized to develop an analytical expression for fluid imbibition into paper due to capillary action. According to the Washburn equation [23]:

$$L\_w = \sqrt{\frac{R\gamma \cos \theta}{2\eta}} t \tag{1}$$

where, *LW*—wetted length, *R*—pore radius, *γ*—the surface tension of the liquid, *t*—the time taken for liquid to seep into the capillary, *θ*—the contact angle of liquid on the capillary walls and *η*—the viscosity of fluid.

#### Modeling of PBC

The quasi 2D fluid structure mode is adapted for analysis. Geometry is inspired by the bending of the paper-based cantilever when exposed to fluid. Cantilever actuation is considered as the system output defined by fluid loading, please refer Table 1 for parameters.


Consider the PBC with a uniform rectangular cross-section loaded by a water column, as shown in Figure 7. Moderately large static deflection (Appendix A) of PBC is governed by equation [24],

$$EI\frac{d^4w}{dx^4} + N\frac{d^2w}{dx^2} + q = 0\tag{2}$$

where, *E*—Young's modulus of the wet paper, *I*—second moment of inertia, *N*—internal (axial) stress in PBC and *q*—auxiliary loading is the equivalent transverse loading resulting in the same deflection of the PBC.

Internal (axial) stress in PBC is given by,

$$N = EA\epsilon \tag{3}$$

where, *A*—cross-sectional area of PBC and – membrane strain (due to hygro-expansion and rotation of PBC) [25].

The auxiliary loading of PBC is given by,

$$q = \rho g \left(\frac{V\_f}{l}\right) \tag{4}$$

where, *Vf* —volume of fluid imbibition into PBC.

**Figure 7.** Static actuation model of PBC.

Substituting the above equation to governing Equation (2) we get,

$$w^{IV} + \lambda^2 w^{II} = \mathbb{Q} \tag{5}$$

where,

$$
\lambda = l \sqrt{\frac{N}{EI}} \tag{6}
$$

$$Q = \frac{-\rho \lg\left(\frac{V\_f}{T}\right)}{EI} \tag{7}$$

In the above equations, *λ* and *Q* are parameters associated with the type of paper and fluid used for experiment respectively.

In order to solve Equation (2) uniquely, four boundary conditions are required. A four combination of in-plane boundary condition for PBC are given as, deflection: fixed-end x = 0, *w*(*x*) = 0; slope: fixed-end x = 0, *w<sup>I</sup>* (*x*) = 0, slope; bending moment: free-end x = l, *wI I*(*x*) = 0; shear force: free-end x = l, *wIII*(*x*) = 0.

Non-Dimensional Model of PBC

To get a better idea of the relative size of the terms, we need a non-dimensionalized Equation (5) as per Table 2.

**Table 2.** PBC parameters.


Thus, the non-dimensional form of Equation (5) yields to,

$$w^{\*IV} + \lambda^{\*2} w^{\*II} = \mathbb{Q}^\* \tag{8}$$

The solution to the problems depends on a four combination of in-plane boundary condition for the non-dimensional model given as,

$$\text{Fixed} - \text{end } \mathbf{x}^\* = 0, \; w^\*(\mathbf{x}^\*) = 0 \tag{9}$$

$$\text{Fixed} - \text{end } \mathbf{x}^\* = 0, \; w^{\*I}(\mathbf{x}^\*) = 0 \tag{10}$$

$$\text{Free}-\text{end }\mathfrak{x}^\* = \mathfrak{l}, \; w^{\*II}(\mathfrak{x}^\*) = 0 \tag{11}$$

$$\text{Free}-\text{end }\mathbf{x}^\* = \mathbf{l}, \; w^{\*III}(\mathbf{x}^\*) = \mathbf{0} \tag{12}$$

To summarize, Equation (8), together with boundary conditions, related the deflection, hygro-expansive stress and loading of PBC.

Solution for PBC

The problem is solved under the assumption of fixed-free end conditions, and the fluid load is distributed according to the square of wetted length as per the Washburn equation mentioned earlier. There is a considerable elastoplastic effect [26] of PBC response due to fluid imbibition.

In order to determine the deflection shape of the PBC, consider Equation (8). The roots of the characteristic equation are 0, 0, ±*iλ*. Therefore, the general solution of the homogeneous equation is,

$$w\_{\mathcal{G}}^{\*}(\mathbf{x}^{\*}) = \mathbb{C}\_{0} + \mathbb{C}\_{1}\mathbf{x}^{\*} + \mathbb{C}\_{2}\cos(\boldsymbol{\lambda}^{\*}\mathbf{x}^{\*}) + \mathbb{C}\_{3}\sin(\boldsymbol{\lambda}^{\*}\mathbf{x}^{\*})\tag{13}$$

As a particular solution of the inhomogeneous equation,

$$w\_p^\*(\mathbf{x}^\*) = \mathbb{C}\mathbf{x}^{\*2} \tag{14}$$

Substituting the above solution to the governing Equation (8), we get,

$$C = \frac{Q^\*}{2\lambda^{\*2}} = -\frac{1}{2} \left(\frac{\rho g V\_f l}{N R\_\circ}\right) \tag{15}$$

The general solution for Equation (4) is sum of the general solution of the homogeneous equation, and the particular solution of the inhomogeneous equation,

$$w^\*(\mathbf{x}^\*) = w\_{\mathcal{X}}^\* + w\_p^\* \tag{16}$$

There are four unknowns, and four boundary conditions as per Equation (4) for transverse deflection. The coefficients determined from boundary conditions are *C*<sup>0</sup> = −*C*/10, *C*<sup>1</sup> = −4*C*/100, *C*<sup>2</sup> = *C*/10, and *C*<sup>3</sup> = 9*C*/1000.

The general solution for deflection of PBC,

$$w\_{\mathcal{S}}^\*(\mathbf{x}^\*) = \mathbb{C}\_0 + \mathbb{C}\_1 \mathbf{x}^\* + \mathbb{C}\_2 \cos(\lambda^\* \mathbf{x}^\*) + \mathbb{C}\_3 \sin(\lambda^\* \mathbf{x}^\*) + \mathbb{C} \mathbf{x}^{\*2} \tag{17}$$

#### **3. Results**

*3.1. Analytical Solution*

Analytical solution for the problem is obtained by substituting the value of coefficients in Equation (17).

$$w\_{\mathcal{S}}^{\*}(\mathbf{x}^{\*}) = -\frac{\mathbb{C}}{10} - \frac{4\mathbb{C}}{100}\mathbf{x}^{\*} + \frac{\mathbb{C}}{10}\cos(\boldsymbol{\lambda}^{\*}\mathbf{x}^{\*}) + \frac{9\mathbb{C}}{1000}\sin(\boldsymbol{\lambda}^{\*}\mathbf{x}^{\*}) + \mathbb{C}\mathbf{x}^{\*2} \tag{18}$$

Eliminating C between Equations (15) and (18), we get

$$w\_{\mathcal{S}}^{\*}(\mathbf{x}^{\*}) = -\frac{Q^{\*}}{2000\lambda^{\*2}} \left( 100\cos\lambda^{\*}\mathbf{x}^{\*} + 9\sin\lambda^{\*}\mathbf{x}^{\*} + 1000\mathbf{x}^{\*2} - 40\mathbf{x}^{\*} - 100 \right) \tag{19}$$

For the future reduction of the solution, typical values for the variables can be obtained from Table 3.

An output of MATLAB for the analytical solution for variable values is compatible with the information indicated in Table 3.


**Table 3.** Variable used to obtain response deflection of PBC.

#### *3.2. Numerical Solution*

The numerical solution for the problem is obtained by using d-solve function, an output from MATLAB for the analytical solution for the water and variable values are compatible with the information indicated in Table 3.

#### *3.3. Comparison of Numerical and Analytical Solution*

A comparison of the analytical and numerical solution is made in Figure 8, where the solid color and dash lines in plot represent analytical and numerical solutions, respectively.

**Figure 8.** Analytical and numerical solution for PBC.

#### *3.4. Experimental Results*

In order to validate the obtained results for the response deflection of the PBC, data for the maximum deflection and change in height of the capillary tube were obtained from the experiment, as presented in Table 4. The experiment was conducted with five replicas of PBC, fabricated at the Microfluidics Laboratory URI, to study the effect of fluidic loading on response deflection (actuation) of PBC.

**Table 4.** Experimental results for PBC.


#### *3.5. Parametric Model*

The parametric plots for the fluid (water) loading and internal (axial) stress are shown in Figures 9 and 10, respectively.

**Figure 9.** Parametric model for fluid loading *Q*∗.

**Figure 10.** Parametric Model for internal stress parameter *λ*∗.

Figure 9 displays the relationship between the characteristic length and response deflection of PBC for the increasing value of the fluid loading parameter, *Q*∗. Figure 10 displays the relationship between the characteristic length and response deflection of PBC for the increasing value of the internal stress parameter, *λ*∗.

#### *3.6. Transverse Displacement of the Free end of PBC*

Figure 11 displays the relationship between the length of the cantilever response deflection of the free end of PBC for different wetted lengths.

#### *3.7. Model Summary*

The interpretation for the experiment mode is:


**Figure 11.** Response deflection of free end of PBC for different wetted length.

#### **4. Discussion**

#### *4.1. Summary of Solutions*

The maximum deflection of PBC obtained from different models is found to be identical, i.e., 3.04 mm. Thus, the models are adequate and can be used to compare how the response deflection is affected by different fluid loadings.

#### *4.2. Non-Dimensional Model*

The non-dimensional model was utilized to compare the magnitude of deflection, fluid loading and internal (axial) stress for PBC. For color water loading, the maximum deflection is found to be around one order of magnitude larger than the internal stress, and more than two orders of magnitude smaller than the fluid loading. The other way to interpret the non-dimensional model is that 1000 units of water loading will induce 250 units of internal stress responsible for membrane strain and 25 units of maximum deflection at the free end of PBC.

#### *4.3. Parametric Model*

Comparison for response deflection of PBCs were carried out and the results are shared in Section 3.5.

The increasing value of fluid loading parameter, *Q*∗, results in the higher maximum deflection of PBC. The fluid loading parameter is considered as the mass of fluid acting transversely on PBC, the phenomenon of imbibition of fluid from the capillary is carried out until the paper attains its saturation, and at this point the PBC will correspond to maximum deflection. Different paper capillaries possess different amounts of fluid based on their physical properties. The higher value of *Q*∗ signifies the high fluid carrying capacity of paper. The graph in Figure 9 demonstrates the increasing response deflection of PBC.

Internal stress parameter *λ*∗ is another significant factor that needs to be addressed for the analysis of parametric mode. Generally, internal stress results in stress emerging in the structure of loading. In the case of paper, it is comfortable to adapt these resultant stresses in PBC due to the phenomenon of hygro-expansion and bending (rotation). In case of large deflection, these components cancel out others. Figure 10 plots the response deflection of PBC with the increasing value of internal stress parameter *λ*∗. Contrary to parameter *Q*∗, the increasing value of parameter *λ*∗ will result in a lower maximum deflection of PBC.

#### *4.4. Transverse Displacement of Free End of PBC*

It is interesting to analyze the response deflection of the free end for different wetted lengths of PBC. The problem is modeled as a quasi-static 2D fluid structure; however, the fluid flow is governed by Washburn, which makes the model susceptible to dynamic conditions. Instead of analyzing the time dependent solution of the model, the response variable is studied for different wetted lengths. The wetted length of PBC progresses as the

square root of the time elapsed, so 5 instants in time are included in the Figure 11, when the value of wetting length is equal to 20, 40, 60, 80 and 100 percent of overall length of PBC.

Due to the fact that Equation (2) is developed for the moderately large static deflection of PBC, it allows for very minimal room to obtain a solution for the axial displacement of the free end of PBC. The model is developed with the limitation of deflection θ, within 0◦ to 10◦. Please refer to Appendix A for details.

#### **5. Conclusions**

In this paper, an autonomous paper-based actuation system was introduced in order to sequentially load different reagents into the area of interest. For the purpose of this study, the concluded result will be utilized to model the behavior of PBC on fluidic loading. The solution for the model can be utilized to obtain the maximum deflection of PBC under the assumption, 1◦ ≤ θ ≤ 10◦. For small and large deflection, the model shall be modified under the similar assumption of cross-sectional deformation.

**Author Contributions:** Conceptualization, A.K., H.H.-B. and A.C.; methodology, A.K., H.H.-B. and A.C.; software, A.K. and H.H.-B.; validation, A.K., H.H.-B., A.C. and N.R.; formal analysis, A.K., H.H.-B.; investigation, A.K., H.H.-B. and A.C.; resources, N.R., C.A. and M.F.; data curation, A.K. and H.H.-B.; writing—original draft preparation, A.K., H.H.-B. and A.C.; writing—review and editing, A.K., H.H.-B., A.C., N.R., C.A. and M.F.; visualization, A.K. and H.H.-B.; supervision, N.R., C.A. and M.F.; project administration, N.R., C.A. and M.F.; funding acquisition, C.A. and M.F. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** The authors would like to acknowledge the ideas and suggestions that Winfield Smith has shared during the execution of this study in addition to his valuable help and support.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **Appendix A**

Generalized Relationship [27], Generalized Stress for cantilever in (x, z) plan,

Bending Moment, *M* = *σxx z dA* [Nm]

Normal Stress, *N* = *σxx dA* [N]

Shear Stress, *V* = *σxz dA* [N]

From Euler-Bernoulli hypothesis, and substitution in Axial Force N and Bending Moment M, for constant E and I we get:

$$\begin{array}{c} \mathcal{M} = EI\mathbf{x} \text{ [Nm]}\\ N = EA\,\,\epsilon^0 \,\, [\mathbf{N}] \end{array}$$

**Figure A1.** Generalized planar stress for cantilever.

Force equilibrium equation, in x-direction:

$$\frac{dN}{dx} = 0$$

Force equilibrium equation, in z-direction:

$$\frac{d}{d\mathbf{x}}\left(V + N\frac{dw}{d\mathbf{x}}\right) + q = 0$$

Moment equilibrium equation, about y-axis:

$$\frac{dM}{dx} = V$$

Cantilever equilibrium equation,

$$\frac{d^2M}{dx^2} + N\frac{d^2w}{dx^2} + q(x) = 0$$

Substituting value of M from pervious equation, we get.

$$\frac{d^2}{d\mathbf{x}^2}(EI\mathbf{x}) + N\frac{d^2w}{dx^2} + q(\mathbf{x}) = 0$$

$$\text{where } \mathbf{x} = \frac{d^2 w(x)}{dx^2}$$

#### **References**


### *Proceeding Paper* **Study of Gas-Sensing Properties of Titania Nanotubes for Health and Safety Applications †**

**Vardan Galstyan \*, Nicola Poli and Elisabetta Comini**

Sensor Lab., Department of Information Engineering, University of Brescia, Via Valotti 9, 25133 Brescia, Italy; nicola.poli@unibs.it (N.P.); elisabetta.comini@unibs.it (E.C.)

**\*** Correspondence: vardan.galstyan@unibs.it

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** We studied the preparation and gas-sensing performance of a hybrid nanomaterial based on titania nanotubes and graphene derivatives. We fabricated the hybrid structure with tunable chemical-sensing properties, achieved by tailoring the structure and composition of graphene oxide and coupling it with titania nanotubes. The parameters of manufactured sensing structures were investigated for hydrogen and ammonia. Our experimental findings indicate that this research may demonstrate an efficient way to enhance the gas-sensing properties of metal oxide nanomaterials for health and safety applications.

**Keywords:** nanomaterials; titania; graphene; chemical sensor; gas sensor

**Citation:** Galstyan, V.; Poli, N.; Comini, E. Study of Gas-Sensing Properties of Titania Nanotubes for Health and Safety Applications. *Chem. Proc.* **2021**, *5*, 16. https://doi.org/10.3390/ CSAC2021-10625

Academic Editor: Ye Zhou

Published: 7 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

#### **1. Introduction**

Modern gas-sensing systems based on nanotechnology may enable reliable and continuous detection of different gaseous compounds to control atmospheric pollutants and protect human health [1–5]. With their quantum-mechanical properties, wide-bandgap semiconductor nanostructures can affect the characteristics of functional devices [6–8]. Therefore, the application of semiconductor nanomaterials in the development of chemical gas sensors is of great interest [9–11]. Highly ordered transition metal oxide nanostructures have been considered as promising materials for applications in chemical gas sensors due to their good chemical stability and functional properties [12]. In this regard, well-ordered and highly aligned titania nanotubes, with their superior electron transport properties and large surface area, are very attractive structures for the fabrication of gas-sensing systems [13–17]. Herein, we report the preparation and investigation of sensing properties of titania-based nanotubular structures for their application in gas-detection devices. We studied the effect of the additive material on the functionalities of nanotubes to optimize their sensing performance. The morphology, structure, and composition of prepared materials were examined. The sensing properties of materials were studied for hydrogen (H2) and ammonia (NH3). We have analyzed the interaction mechanism between the prepared nanotubes and gaseous compounds, considering their structural and compositional modifications. The obtained results demonstrate that the fabricated sensing materials have potential for application in detection systems [17].

#### **2. Materials and Methods**

Titania nanotubes were prepared as follows: The metallic titanium films were deposited on alumina substrates by radio frequency magnetron sputtering. Then, metallic films were anodized in a two-electrode system Teflon cell at room temperature. We reported the detailed information on anodization procedure in our previous reports [16,18]. The prepared materials were crystallized via thermal treatment in a tubular furnace at 400 ◦C for 6 h. We reported the crystallization regimes and analysis of the samples in [19].

The morphological analysis of samples (Figure 1) was performed by means of a LEO 1525 scanning electron microscope (SEM) equipped with a field emission gun. In order to fabricate the hybrid material, we prepared an aqueous dispersion of graphene oxide. Then, we drop-casted the prepared dispersion on the surface of titania nanotubes. To carry out gas-sensing measurements, platinum electrodes and a heater were deposited on the surface of the sensing structures and on the backside of substrates by DC magnetron sputtering. The gas-sensing tests were performed in a test chamber and the measurements were controlled by a computer-controlled gas flow system. The sensor based on pure titania is denoted as S1 and the sensor based on the composite material is denoted as S2 (Figure 2).

**Figure 1.** SEM images of the obtained samples. (**a**) The surface morphology of pristine titania nanotubes with different resolutions; (**b**,**c**) the morphologies of the fabricated composite material with different resolutions.

**Figure 2.** The dynamic response of obtained S1 and S2 sensors for different concentrations of H2 and NH3 at 200 ◦C.

#### **3. Results and Discussion**

The results of the morphological analysis of samples are shown in Figure 1. The SEM observations confirmed that highly ordered titania nanotubes were successfully prepared. The tube diameter was 30 nm (Figure 1a). Figure 1b,c shows the surface morphology of the composite structure. As can be seen, the surface of the titania nanotubes was covered by graphene oxide sheets. Figure 2 presents the dynamic response of the fabricated S1 and S2 sensors for concentrations of 120, 240, and 480 ppm of H2 and 10, 20, and 30 ppm

of NH3. The sensing measurements were carried out at 200 ◦C. The graphene oxide significantly increased the response of the titania nanotubes towards H2. Meanwhile, very small differences were observed between the sensing behavior of the S1 and S2 structures towards NH3. The drastic enhancement in the response of the S2 sensor compared to S1 can be attributed to the depletion layer formed between the titania nanotubes and graphene oxide. In this case, the presence of more active centers improved the adsorption of H2 on the surface of the hybrid material, which is important for its sensitivity.

#### **4. Conclusions**

We fabricated a hybrid structure based on titania nanotubes and graphene oxide. Then, we investigated its gas-sensing performance for H2 and NH3. Our experimental findings show that the depletion layer formed between two materials plays a crucial role in tuning the sensing response of the hybrid structure. The hybrid material exhibited a better sensing response towards H2 compared to pristine titania nanotubes, indicating that this this is an efficient and promising way to enhance the sensing parameters of metal oxide gas sensors. Moreover, a noticeable difference between the responses of the composite structure and pristine nanotubes towards NH3 was not observed, which indicates an enhancement in the selectivity of the composite.

**Funding:** This work was partially funded by the NATO Science for Peace and Security Programme under grant No. G5634 "Advanced Electro-Optical Chemical Sensors"; by the "Multi-Messenger and Machine Learning Monitoring of SARS-CoV-2 for occupational health & safety" (4M SARS-CoV-2) project under the Special Integrative Fund for Research (FISR), Ministry of University and Research (MUR), Italy; and by the MIUR "Smart Cities and Communities and social innovation", for the project titled "SWaRM Net/Smart Water Resource Management—Networks".

**Institutional Review Board Statement:** Not applicable.

**Data Availability Statement:** The data that support the findings of this study are available from the corresponding author (V.G.), upon reasonable request.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Abstract* **Validation of Spent Coffee Grounds as Precursors for the Development of Sustainable Carbon Dot-Based for Fe3+ Optical Sensing †**

**Diana M. A. Crista \*, Joaquim C. G. Esteves da Silva and Luís Pinto da Silva**

Chemistry Research Unit (CIQUP), Faculty of Sciences of University of Porto, R. Campo Alegre 687, 4169-007 Porto, Portugal; jcsilva@fc.up.pt (J.C.G.E.d.S.); luis.silva@fc.up.pt (L.P.d.S.)

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** Carbon dots (CDs) are fluorescence carbon-based nanomaterials that possess several properties such as photoluminescence, biocompatibility and good water solubility. They can be fabricated from a large variety of precursors; however, most available organic molecules are still expensive and their use or synthesis can lead to significant challenges to the environment and human health. It has become desirable to use biomass waste as alternative precursors in the synthesis of CDs, given that biomass waste material is ubiquitous, nontoxic, cheap and renewable. Spent coffee grounds (SCGs) are the residues of the treatment of coffee powder can be a potential carbon source to a more environmentally sustainable synthesis route. In this work, we fabricated SCG-based CDs via one-pot and solvent-free carbonization at 200 ◦C of solid samples generating particles with sizes between 2.1 and 3.9 nm. These carbon nanoparticles exhibited blue fluorescence and excitationdependent emission of carbon dots with moderate quantum yields (2.9–5.8%). The presence of heavy metals in water resources, such as Fe3+, can lead to adverse health effects. SCG-based CDs showed potential for being used as optical Fe3+ optical sensors, with Life Cycle Assessment (LCA) studies validating the SCGs as more sustainable precursors than classical precursors, both considering a weight- or function-based functional unit.

**Keywords:** spent coffee grounds; carbon dots; sustainability; sensing

Academic Editor: Elena Benito-Peña

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/ 10.3390/CSAC2021-10452/s1.

**Funding:** Acknowledgment to projects PTDC/QUI-QFI/2870/2020 and UIDB/00081/2020. Also acknowledge for for funding the PhD grant SFRH/BD/144423/2019 (D.M.A.C.), and funding Scientific Employement Stimulus CEECIND/01425/2017 (L.P.d.S.).

**Conflicts of Interest:** The authors declare no conflict of interest.

**Citation:** Crista, D.M.A.; da Silva, J.C.G.E.; da Silva, L.P. Validation of Spent Coffee Grounds as Precursors for the Development of Sustainable Carbon Dot-Based for Fe3+ Optical Sensing. *Chem. Proc.* **2021**, *5*, 17. https://doi.org/10.3390/CSAC2021- 10452

**<sup>\*</sup>** Correspondence: up200702319@fc.up.pt

### *Proceeding Paper* **Unravelling Plant-Pathogen Interactions: Proximal Optical Sensing as an Effective Tool for Early Detect Plant Diseases †**

**Mafalda Reis-Pereira 1,2,\* , Rui C. Martins 3,\*, Aníbal Filipe Silva 1,3 , Fernando Tavares 1,4 , Filipe Santos <sup>2</sup> and Mário Cunha 1,2,\***


**Abstract:** This study analyzed the potential of proximal optical sensing as an effective approach for early disease detection. A compact, modular sensing system, combining direct UV–Vis spectroscopy with optical fibers, supported by a principal component analysis (PCA), was applied to evaluate the modifications promoted by the bacteria *Xanthomonas euvesicatoria* in tomato leaves (cv. cherry). Plant infection was achieved by spraying a bacterial suspension (108 CFU mL−1) until run-off occurred, and a similar approach was followed for the control group, where only water was applied. A total of 270 spectral measurements were performed on leaves, on five different time instances, including pre- and post-inoculation measurements. PCA was then applied to the acquired data from both healthy and inoculated leaves, which allowed their distinction and differentiation, three days after inoculation, when unhealthy plants were still asymptomatic.

**Keywords:** plant disease detection; plant pathology; proximal sensing; spectroscopy; precision agriculture; principal component analysis

#### **1. Introduction**

Biotic agents, specifically pests and pathogens, cause significant losses in crop yields, with levels that can range between 20% and 40% [1]. Chemical phytosanitary products are usually applied to prevent and combat these organisms. However, their usage can negatively impact the environment, mainly when applied to treat plant diseases that appear suddenly and spread to large scales [2].

Nowadays, phytopathology methods are considered major challenges because, to be implemented, they often rely on the presence of indicator visible signs of the infection (disease symptoms), which frequently only manifest themselves at the middle to late stages of the process, compromising the effectiveness of phytosanitary measures [3]. An example is the scouting technique, which involves inspecting a crop field to detect and identify infected plant through disease symptoms [4]. Despite being extremely useful, this approach requires specialized trained observers (who must be capable of identifying disease symptoms and distinguishing them from those caused by other abiotic stresses (e.g., nutritional and physiological disorders)), and can be labor-intensive, time-consuming, and expensive [5–11]. Moreover, this approach can be an inefficient in the early stages of the infection and on large areas. Other strategies consist of laboratory-based techniques,

**Citation:** Reis-Pereira, M.; Martins, R.C.; Silva, A.F.; Tavares, F.; Santos, F.; Cunha, M. Unravelling Plant-Pathogen Interactions: Proximal Optical Sensing as an Effective Tool for Early Detect Plant Diseases. *Chem. Proc.* **2021**, *5*, 18. https://doi.org/10.3390/ CSAC2021-10560

Academic Editor: Elena Benito-Peña

Published: 1 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

namely serological and molecular tests, largely used due to their sensitivity, accuracy, and effectiveness. They include enzyme-linked immunosorbent assay (ELISA) and polymerase chain reaction (PCR) methods, being the first serological approach based on protein in the detection of causative diseases and the second molecular technique based on the DNA sequence of the pathogen. Their development boosted plant disease diagnosis, since they allow the simultaneous processing of several samples and perform a precise pathogen identification. Furthermore, PCR enables the detection of pathogens that have not been cultured. Nevertheless, these procedures present some limitations, especially in the early phase of the infection process, due to the uneven spread of pathogens inside plants, compromising their effectiveness in analyzing asymptomatic samples [9,12–14]. Other drawbacks can also be enumerated. They require several hours to be completed, require the realization of detailed sampling procedures, and destructive sample preparation, not allowing a follow-up of the disease progression [12,13].

Therefore, the necessity of developing fast, accurate, and selective in vivo techniques for plant disease detection arises. These innovative approaches must provide complementary information to the current methods applied in the phytopathology field and combine with them. Several non-invasive methods have been developed in the last decade, which proved to be sensitive, consistent, standardize, rapid, cost-effective, and have highthroughput [15]. Hyperspectral spectroscopy (HS) is one of them and seems to be effective in estimating a wide variety of plant chemical, biophysical, and metabolic traits in living tissue [16–22], namely foliar structure, plant chemical composition, water concentration, and metabolic status [23]. Through spectral measurements in the visible (Vis, 400–700 nm), nearinfrared (NIR, 700–1100 nm), and shortwave infrared wavelengths (SWIR, 1100–2500 nm), this approach assesses changes in optical properties of leaves, which derive from interactions between light, chemical bonds, and cellular structure [24]. Briefly, modifications in plants' reflectance in the Vis range are mostly related to pigment concentration and physiological processes, such as photosynthesis. In turn, changes in the NIR are correlated with leaf structure and internal scattering processes. The SWIR region is affected by leaf structural and chemical composition (including lignins and proteins), as well as water content [25–29].

Since phytopathogens induce physiological, biochemical, and structural changes in host plants, HS seems to be promising in plant disease detection, identification, and quantification [30–38]. Hyperspectral sensors can be used alone or mounted in different platforms, allowing the performance of mapping, monitoring, scouting, and application tasks [2]. Their flexibility allows them to assess leaf, single-plant, canopy (proximal sensing), and even plot and regional scales (remote sensing) [2]. Some examples, sorted by measurement scale, include handheld sensors, rail systems, vehicle, and tractor-mounted systems, drones UAVs, as well as aircrafts and satellites [39].

Despite the possibilities provided by these optical devices for simple, rapid, nondestructive disease detection and identification, its application is still very limited, due to the scarcity of extensive agronomic and phytopathological studies aiming to explore their full potential. Their technology readiness levels (TRL) are close to TRL3 (analytical and experimental critical function, and/or characteristic proof-of-concept) [40]. Hence, this study aimed to evaluate the potential of UV–Vis spectroscopy to detect diseased tomato leaves and discriminate between healthy and infected leaves, through a multitemporal approach. Furthermore, the capability of this technology in detecting changes in the reflectance spectrum of infected leaves was analyzed, before the first symptoms became visible.

#### **2. Materials and Methods**

#### *2.1. Experimental Design*

Tomato (*Solanum lycopersicum* L.) plants, of the cultivar cherry, were grown in 200 mL pots containing a commercial potting substrate, in a *walk-in* plant growth chamber under controlled conditions (temperature of 25–27 ◦C, humidity of approximately 60%, and

photoperiod of 12/12 h). Plants were divided into two groups, one of them being inoculated with *Xanthomonas euvesicatoria* LMG 905 (Xeu) bacteria and the other being treated with sterile distilled water only (control group, Con). Plants were inoculated in the laboratory, at the growth stage of 5–6 fully expanded leaves, by spraying until they became fully wet, and run-off occurred. The bacterial suspensions used for these inoculation assays consisted of <sup>1</sup> × 108 cells/mL. They were prepared from a 48 hour-old culture, grown on YDC medium (yeast extract, 10.0 g; dextrose, 20.0 g; CaCO3, 20.0 g; agar, 15.0 g; distilled water up to 1.0 L). The inoculated plants were then covered with transparent polythene bags for 48 h to increase the relative humidity that fosters bacterial entry into plant tissues through natural openings, such as stomata [41]. Plants were monitored daily for symptom development for 5 days.

At the same time, to verify if the bacteria cultures used in these inoculation tests were viable, 20 μL of Xeu solution were cultured in different Petri dishes containing YDC media. After 48 h, it was possible to observe the bacteria growth in both nutrient media, proving that bacteria were viable at inoculation.

#### *2.2. Spectral Measurements*

Hyperspectral data were collected in vivo from the adaxial side of healthy and infected tomato plant leaves, using a compact benchtop system consisting of a D2 (deuterium) light source (Ocean Optics, model DH-2000-BAL, Ostfildern, Germany), spectrometer (Ocean Optics, model HR4000, Ostfildern, Germany), transmission optical fiber bundle (UV), and stainless-steel slitted reflection probe for sample measurement. The spectrometer operated in the 195–1100 nm wavelength range, with a high spectral response and optical resolution of 0.025 nm (full width at half maximum—FWHM). The measurements were carried out using an experimental setup in the laboratory. A LED light source was placed beneath the leaf and provided homogeneous illumination to its entire surface. The light signal from the sample analyzed was guided to the entrance lens of the spectrometer by the fiber-optic cable placed perpendicularly 1 cm above the measured surface. Specialized software was used for data acquisition and processing. Data acquisition was performed with 10 scans for an integration period of 60 ms, in three leaves per plant, on nine locations on each leaf.

#### *2.3. Data Pre-Processing*

Spectral pre-processing was performed, in order to remove possible artifacts, e.g., baseline shifts, Mie and Rayleigh scattering, and stray light. Also, a pretreatment with a fast fourier transform (FFT) was carried out on spectral data to smooth/denoise it. FFT is an algorithm that computes the discrete Fourier transform (DFT) of a sequence or its inverse (IDFT). Fourier analysis converts a signal from its original domain (often time or space) to a representation in the frequency domain and vice versa. The DFT is obtained by decomposing a sequence of values into components of different frequencies [42]. Spectral data pre-processing was performed with RStudio software.

#### *2.4. Data Processing—Analytical Techniques*

Spectral data was subjected to a principal component analysis (PCA), a multivariate data analysis technique was used to reduce the dimensionality, while preserving its structure by projecting it into a new coordinate system. This technique allows the preservation of the total variance of the dataset and minimizes the mean square approximate errors. PCA uses eigenvectors and eigenvalues to define the reduced subspace (representing the original coordinate system). It originates principal components (PC), which are linear combinations of interrelated variables. PC1 accounts for the maximum possible proportion of the variance information of the original dataset (explained by the eigenvalue), and subsequent principal components (PC2, PC3, ... ) account for the maximum proportion of the unexplained residual variance, and so forth [43,44].

Contigous hyperspectral wavebands present redundant information [45]. The application of a PCA allows the transformation of this type of high-dimensional data into a few

wavebands that contain most of the information in the original bands. The importance of these hyperspectral bands in each PC is then established based on the magnitude of eigenvectors or factor loadings for crop biophysical and biochemical traits, being that the higher the eigenvector, the higher is the importance of the band. So, PCA allows the selection of the best wavebands to model biophysical and biochemical quantities and the elimination of redundant bands (by highlighting the main bands) [46].

#### **3. Results**

The spectral response properties of tomato leaves to the stress caused by *Xanthomonas euvesicatoria* LMG 905 is very important for discriminating bacterial infection levels in precise pest management using hyperspectral proximal sensing data. The averaged raw spectral curves of healthy and diseased tomato leaves were slightly different in some spectral ranges, namely through the visible region of the wavelength spectrum (~480–680 nm) (please refer to Figure 1) Similarly, the spectral measurements assessed on infected leaf tissue presented a decrease in signal intensity throughout the sampling period (24–144 h), which accompanied the appearance of the first visual symptoms of the disease after 72 h.

**Figure 1.** Spectral measurement curve evolution for tomato leaves inoculated with bacteria *Xanthomonas euvesicatoria* LMG 905, within the sampling period (24–144 h). Leaf spectral curves were assessed in vivo on the adaxial side of fully expanded leaves, on the spectral region from 195 to 1100 nm.

Figure 2 presents the principal components (PC) Gabriel plot for the healthy (Con) and diseased (Xeu) leaves spectra, three days after inoculation (before the appearance of the first symptoms). The PCA algorithm has obtained two PCs, accounting for 99.6% of the total variance. PC1 (94.3%) discriminates the effects on the variance of these two types of tomato leaves, which is more evident in PC2 (5.3%).

**Figure 2.** Gabriel plot of PC1, PC2, and PC3 resulting from the PCA of the dataset three days after inoculation (all leaves were asymptomatic, showing no symptoms of the disease caused by *Xanthomonas euvesicatoria* LMG 905).

The wavelengths that have a higher contribution in these PC are in the interval of ~454–654 nm (visible range of the wavelength spectrum). The ones between ~492–510 nm (essentially the blue region of the electromagnetic spectrum) explain 30% of the variance of the PC1, whereas ~454–461 nm (blue region) explain 40% of the variance of the PC2 and 50% of the PC3. In all the first four dimensions of this analysis, the wavelengths ranging from approximately 445–480 nm (blue) and 580–700 nm (red) were the ones that explain most of the variance of the data.

This evidence can be related to the symptoms caused by Xeu, since these bacteria cause small, brown, angular lesions on leaves (which can be surrounded by a yellow halo with time), affecting the levels of photosynthetic pigments (contributing especially to the reduction of the chlorophyll levels, whose absorption features are more evident in the blue and red ranges of the Vis spectral region), cellular content, and structural arrangement.

#### **4. Discussion**

The spectral behavior of tomato plants depends on their biochemical and structural profile. In brief, plants' spectral signature in the visible spectral region (400–700 nm) depends mainly on the content of photosynthetic pigments. These compounds are good absorbers of red and blue wavelengths. Of the major pigments, Chlorophyll a (Chl a) has maximum absorption in the 410–430 and 600–690 nm regions, whereas Chlorophyll b

(Chl b) has maximum absorption in the 450–470 nm range. In healthy plants, chlorophyll concentration is approximately ten times higher than that of other pigments, thus masking out the specific absorption features of these compounds. The green part of the spectrum, on the other hand, is less strongly absorbed, resulting in a reflectance peak in the green domain (at about 550 nm) [25]. Hence, when a light source illuminates healthy plants, they will preferentially absorb red and blue wavelengths, being the green part of the incident light less absorbed and, consequently, more reflected, leading to their green appearance [26]. In the NIR region, the plants' spectral response is related to their structure, structural components, and internal scattering processes. Likewise, the SWIR region is also affected by leaf structural and chemical composition (including the action of lignin's and proteins) and water content [25–29].

Since phytopathogens cause changes in plants' biochemical and structural composition, affecting the levels of photosynthetic pigments and structural elements, tracking changes in plants' spectral behavior can allow an indirect analysis of their phytosanitary status. Generally, unhealthy plants have more reflection in the red region and lower reflectance in the NIR region. Briefly, stress usually promotes an increase of reflectance over the whole spectrum, since it causes a rapid decrease of chlorophylls, which increases reflectance in the Vis range and exposes the absorption characteristics of other pigments, such as carotenoids (responsible for the yellowing of the leaves) and xanthophylls (responsible for the reddening of the leaves). With continuing stress, leaf structures decompose, resulting in extra intra-leaf scattering and an increased NIR signal. At the same time, concentrations of brown pigments, which absorb radiance in the Vis and at the onset of the NIR, can increase leading to a flattening of the red edge. Absorption in the SWIR decreases, due to reduced leaf moisture. With a decay of the leaf tissue, the absorption features characteristics of healthy plants gradually disappear [47].

Our findings seem to be in accord with the previous information, showing evidence that UV–Vis spectroscopy can be suitable for plant disease assessment in laboratory conditions. Data collected in a randomized experimental design, combined with a PCA, allowed the discrimination of healthy and diseased tomato leaves, even at the third day after bacteria inoculation, when no visual symptoms were observable. Most of the variance of the data can be comprised with the first four PCs. In all of them, the wavelengths that explain most of the variance of the data ranged from approximately 445–480 nm (blue) and 580–700 nm (red), which was expected, since *Xanthomonas euvesicatoria* causes tissue lesions, degrading the chlorophylls levels and affecting their absorption features in these spectral regions.

Therefore, our results can be related to those obtained in different research, where sensor-based approaches proved to be capable of assessing modifications in plants' spectral behavior, allowing the detection, identification, and quantification of different types of plant diseases [44,48–51]. They involve the capture and analysis of the optical properties of plants, within different regions of the electromagnetic spectrum and their relationship with modifications in plant physiology, namely alterations in tissue color, structural composition, and transpiration rate [19]. These non-invasive methods have been explored in the last decade, presenting the benefits of being sensitive, consistent, standard, high-throughput, rapid, and cost-effective [47], surpassing the limitations of the current methods used in plant disease detection.

#### **5. Conclusions**

The present study suggests that UV–Vis spectroscopy can be a potential tool for the early detection of plant diseases under laboratory conditions, even when unhealthy plants are asymptomatic. Despite these findings, its application is still very limited, due to the scarcity of comprehensive agronomic and phytopathological studies aiming to explore their full potential, as well as the development of applied advanced statistical approaches for data analysis. More research is necessary, especially in field conditions, where more external factors have to surpass, including atmospheric, edaphic, and biotic conditions. Future research should also include more stress levels to discriminate not only healthy leaves from the diseased ones but also different levels of disease severity.

**Author Contributions:** Conceptualization, M.R.-P., R.C.M., A.F.S., F.T., F.S. and M.C.; methodology, M.R.-P., R.C.M., A.F.S., F.T., F.S. and M.C.; validation, M.R.-P., R.C.M. and M.C.; formal analysis, M.R.-P., R.C.M. and M.C.; investigation, M.R.-P., R.C.M. and M.C.; resources, R.C.M., F.T., F.S. and M.C.; data curation, M.R.-P. and R.C.M.; writing—original draft preparation, M.R.-P.; writing—review and editing, R.C.M., A.F.S., F.T., F.S. and M.C.; supervision, F.T., F.S. and M.C.; funding acquisition, M.R.-P., R.C.M., A.F.S., F.T., F.S. and M.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** Mafalda Reis-Pereira and Aníbal Filipe Silva were supported by fellowships from Fundação para a Ciência e a Tecnologia (FCT), with the references SFRH/BD/146564/2019 and DFA/BD/9136/2020, respectively. Rui C. Martins acknowledges Fundação para a Ciência e Tecnologia (FCT) research contract grant (CEEIND/017801/2018). This research was supported by the project 'SpecTOM— Metabolomics Tomography Spectroscopy System', University of Porto, Fundação Amadeus Dias, and Santander-Universities Grant.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Development of a Gas Sensor for** *Eucalyptol* **Supervision: A Supporting Tool for Extreme Wildfire Management †**

**Cátia Magro 1,\* , Marcelo Morais 1, Paulo A. Ribeiro <sup>2</sup> , Susana Sério <sup>2</sup> , Pedro Vieira <sup>1</sup> and Maria Raposo <sup>2</sup>**


**Abstract:** Recent research on volatile organic compounds (VOC) released by the heated vegetation has shown that, under specific conditions (e.g., extreme heat, humidity, wind, and topography), VOC might foster wildfire ignition sources and explain sudden changes in fire behavior, particularly in the most susceptible and flammable forests (eucalypt forests). This work aims to develop an electronic nose (e-nose) based on a sensor's array to monitor the concentration of eucalyptol, the major VOC compound of the *Eucalyptus globulus* tree. The detection of this target compound was achieved by measuring the impedance spectra of layer-by-layer developed thin films based on polyethyleneimine, poly(allylamine hydrochloride), and graphene oxide, by injecting the analyte into a custom-made vacuum chamber system. The obtained results were analyzed by the principal component analysis method. The developed e-nose sensor was able to distinguish different concentrations in a range from 411 to 1095 ppm.

**Keywords:** wildfires; volatile organic compounds; eucalyptol; electronic nose; impedance spectroscopy

#### **1. Introduction**

Extreme wildfires cause the loss of various human lives and have a significant impact on the biodiversity of ecosystems. These phenomena are, still, not yet fully understood. Recent studies have proposed a new theory, suggesting that flammable gases generated from heated vegetation, in particular, Volatile Organic Compounds (VOC) common in Mediterranean plants may, under some topographic and wind conditions, accumulate in locations where, after the arrival of the ignition source, they rapidly burst into flames as occurs in explosions [1,2]. VOC can exhibit a flammable nature, enabling fire ignition sources and sudden changes in the fire behavior [3].

The electronic nose (e-nose) system, which comprises an array of sensors with partial specificity and an appropriate pattern recognition system, can recognize complex gases. Moreover, e-noses have shown favorable efficiency in monitoring applications, making them a potential tool for the study of VOC [4–6]. Although the e-nose exhibits high sensitivity, forest environments consist of a complex mixture of gases and therefore the used sensors must be able to detect, classify and quantify the target compound. To improve the sensor's sensitivity, different materials can be used as coatings, thus enhancing the chemical and physical properties of the sensor [7–9].

The purpose of this work was the development of a custom-made measuring system attached to an e-nose system to monitor eucalyptol in a range of concentrations, from 411 to 1095 ppm. The concentration range was chosen based on the concentrations found in

**Citation:** Magro, C.; Morais, M.; Ribeiro, P.A.; Sério, S.; Vieira, P.; Raposo, M. Development of a Gas Sensor for *Eucalyptol* Supervision: A Supporting Tool for Extreme Wildfire Management. *Chem. Proc.* **2021**, *5*, 19. https://doi.org/10.3390/ CSAC2021-10432

Academic Editor: Nicole Jaffrezic-Renault

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Mediterranean vegetation (including the Eucalyptus tree [10]) and a range that was the lower flammability limit for terpenes (<1% (*v*/*v*)) [11], for laboratory safety reasons.

Thus, different layer-by-layer (LbL) thin films were developed in order to attain the best combinations for the monitoring of the target compound. The thin films were produced with polyethyleneimine (PEI), poly(allylamine hydrochloride) (PAH), and graphene oxide (GO), namely (PEI/GO) and (PAH/GO). These films have been already described in [12–14] and, thus, may have an interesting potential to monitor VOC, namely eucalyptol.

#### **2. Materials and Methods**

The developed e-nose consists of an array of sensing devices based on ceramic solid supports with deposited gold interdigitated electrodes (IDE), comprising eight "fingers" each, with dimensions of 22.8 × 7.6 × 0.7 mm and each "finger" has 200 μm of width. These solid supports were acquired from DropSens (Oviedo, Asturias, Spain) [15].

The thin films were deposited on the sensors IDE by the layer-by-layer (LbL) technique, which consists of the alternate deposition of polyelectrolytes layers with opposite electrical charges, to obtain several bilayers. The polyelectrolytes used to built-up the thin films layers were polyethyleneimine (PEI), poly(allylamine hydrochloride) (PAH), and graphene oxide (GO), all purchased from Sigma-Aldrich, Steinheim, Germany. The aqueous solutions of the polyelectrolytes were prepared with a 10−<sup>2</sup> M concentration of each polyelectrolyte. Each of the aqueous solutions were prepared with ultrapure water, obtained in a Milli-Q ultrapure water system (Millipore GmbH, Billerica, MA, USA). This process was carried out by alternated adsorption of the positive PEI or PAH polyelectrolytes and the negatively charged GO molecules.

After each adsorption of the polyelectrolyte layers, the solid support was immersed in water in order to remove any polyelectrolyte molecules that were not completely adsorbed. The immersion time, in which the adsorption of the molecules takes place, was 60 s for each of the polyelectrolytes used and 30 s for the washing process. After the adsorption of each bilayer, the thin film was dried using nitrogen gas stream (99% purity, Air Liquide, Algés, Portugal). Thus, thin films of (PEI/GO) and (PAH/GO), with 5 bilayers each, (PEI/GO)5 and (PAH/GO)5, were produced.

The eucalyptol (99%) used for the experiments was purchased from Sigma-Aldrich, Steinheim, Germany. To test the response of the sensor when exposed to the eucalyptol, a range of concentrations from 411 to 1095 ppm was evaluated.

The measurements of each sensor were performed inside a custom-made vacuum chamber system designed by the author's team, which is depicted in Figure 1.

**Figure 1.** Schematic illustration of the experimental setup, including the custom-made vacuum chamber.

A sample holder containing the sensor was placed inside the chamber which presents an approximate volume of 58 L. A rotatory vacuum pump was also connected so that primary vacuum could be achieved and maintained inside the chamber during the tests, enabling a "clean" environment for the measurements. The sample holder was connected to a Solartron 1260 Impedance Analyzer (Solartron Analytical, AMETEK scientific instruments, Berwyn, PA, USA), in order to measure the impedance spectra at the IDE terminals. The chamber also has two inputs for the inlet of the eucalyptol and compressed air in the chamber, allowing the interaction between the target compound and the sensor.

In order to start the testing process, the sample holder with the sensor was placed inside the chamber and connected to the Impedance Analyzer. After that, the vacuum pump was switched on to achieve a pressure of 1.3 × <sup>10</sup>−<sup>3</sup> mbar. Following this, the eucalyptol would be evaporated into the chamber by the opening of a needle valve that connects the chamber with the mixture of eucalyptol. Subsequently, and after the evaporation of the compound and reaching a certain pressure level, the inlet was open to inject the compressed air until the pressure of 1.3 × <sup>10</sup>−<sup>3</sup> mbar was attained. Afterward, the electrical measurements were conducted in a frequency range of 1 Hz to 1 MHz, and an AC signal voltage of 25 mV. This process was then repeated for each eucalyptol concentration.

The electrical impedance spectra data features were assessed with the Principal Component Analysis (PCA) method to reduce the data size and to obtain a new space of orthogonal components in which different concentration patterns can be observed. The principal component analysis (PCA) plots were obtained by performing the normalization (Z-Score normalization (value-μ)/ϑ, μ and ϑ being the mean value and the standard deviation of the samples, respectively) of the impedance spectroscopy data.

#### **3. Results and Discussion**

Figure 2 shows the electric impedance spectra of the sensors, coated with thin films of (PEI/GO)5 and (PAH/GO)5, when in contact with different concentrations of eucalyptol, for different frequencies.

**Figure 2.** Impedance spectra measured with (PEI/GO)5 (**a**) and (PAH/GO)5 (**b**) LbL films inside of the chamber filled with different eucalyptol concentrations. In the insets are shown the evolution of the impedance at 63 and 10 kHz as a function of concentration, respectively.

In order to have a more thorough analysis, a normalization was performed on the data, using the following equation [12]:

$$\frac{PP(C) - PP(0ppm)}{PP(0ppm)}\tag{1}$$

where *PP*(*C*) corresponds to a physical property at a given eucalyptol concentration, and *PP*(0*ppm*) to a measure at a reference concentration, in this case when there was 0 ppm of eucalyptol. In each of the cases represented in the insets of Figure 2a,b, the physical property used was the impedance, at a constant frequency, where the effects of the eucalyptol are better represented. In both cases, it may be observed that an increase in the concentration of eucalyptol results in the decrease in the sensors' impedance values.

Furthermore, a preliminary analysis of the electronic nose concept was performed by the mean of the Principal Component Analysis (PCA). Thus, the data of each one of the sensors, (PEI/GO)5 and (PAH/GO)5, when in the presence of different tested concentrations of eucalyptol were plotted, as shown in Figure 3.

**Figure 3.** PCA plot for both sensors used with thin films of (PEI/GO)5 and (PAH/GO)5, for a range of eucalyptol from 0 to 1095 ppm.

By the PCA analysis, it can be observed that the e-nose assembled, with two sensors, each one coated with thin films of (PAH/GO)5 and (PEI/GO)5, can distinguish between the blank and the different concentrations of eucalyptol that they interacted with.

In fact, the choice of the GO as the upper layer seems to increase the efficiency and discrimination of the measurements, as the molecules of the thin film may react with the target compound enabling a better adsorption, since it possesses many functional groups.

#### **4. Conclusions**

The electronic nose, consisting of an array of two sensors coated with (PEI/GO)5 and (PAH/GO)5 thin films, built-up with the LbL technique, was able to detect eucalyptol and distinguish three different concentrations levels, as the PCA technique has shown. The use of graphene oxide as the thin film bilayer increased the interaction between the custom-made chamber's head space and the thin film coating, and consequently the efficiency of the impedance measurements and capability of the e-nose to distinguish different eucalyptol concentrations.

The present work presents a novel and preliminary study, with an in-deep effort in the build-up of the custom-made chamber. It should be also noted that this project is under development; thus, the build-up of different coatings, e.g., different combination of polyelectrolytes and/or other sensing materials, to improve the e-nose performance, is expected.

**Author Contributions:** Conceptualization, C.M., M.M., M.R. and P.A.R.; methodology, C.M.; software, M.M.; validation, C.M., M.R. and S.S.; formal analysis, C.M., M.R. and S.S.; investigation, M.M. and C.M.; resources, P.A.R., M.R., S.S. and P.V.; data curation, M.M.; writing—original draft preparation, M.M.; writing—review and editing, C.M., M.R. and S.S.; supervision, C.M., M.R. and S.S.; project administration, P.V.; funding acquisition, P.A.R., M.R. and P.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the project PCIF/GFC/0078/2018—Influence of forest VOCs (volatile organic compounds) on extreme fire behaviour from Fundação para a Ciência e a Tecnologia (FCT). The research facilities leading to these results has received support from the Portuguese funding agency FCT—Fundação para a Ciência e a Tecnologia—within projects PTDC/FIS- NAN/0909/2014, UID/FIS/04559/2020 to LIBPhys-UNL from the FCT/MCTES/PIDDAC, and the Bilateral Project entitled "Deteção de Estrogénio- um Contaminante Emergente em Corpos Hídricos" within the scope of "Cooperação Transnacional\_FCT (Portugal)-CAPES (Brazil) 2018".

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** C. Magro acknowledges NOVA.ID of NOVA-FCT for her postdoc fellowship.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Bioactive Compound Profiling and Nutritional Composition of Three Species from the Amaranthaceae Family †**

**Bernabe Nuñez-Estevez 1,2, Tiane C. Finimundy <sup>2</sup> , Maria Carpena <sup>1</sup> , Marta Barral-Martinez <sup>1</sup> , Ricardo Calhelha <sup>2</sup> , Tânia C. S. P. Pires <sup>2</sup> , Paz Otero <sup>1</sup> , Pascual Garcia-Perez <sup>1</sup> , Jesus Simal-Gandara <sup>1</sup> , Isabel C. F. R. Ferreira <sup>2</sup> , Miguel A. Prieto 1,2,\* and Lillian Barros 2,\***


**Abstract:** In this work, the chemical and nutritional composition of three Amaranthaceae species (*Alternanthera sessilis*, *Dicliptera chinensis*, and *Dysphania ambrosioides*) was studied. The results showed a differential flavonoid content in the three species: *A. sessilis* and *D. ambrosioides* showed similar flavonoid contents (15.1 ± 0.6 and 15.1 ± 0.1 mg/g extract, respectively), followed by *D. chinensis* (11.4 ± 0.1 mg/g extract). On the other hand, the nutritional results showed a high protein content in all species (16.9–13.9 ± 0.1 g/100 g dw) and revealed the presence of organic acids, such as oxalic and succinic acid. Therefore, bioactive compounds, together with protein and organic acids, could be of great value to the food industry.

**Keywords:** medicinal plants; phenolic compounds; nutritional value; phytochemistry

#### **1. Introduction**

Several plant species have played an important role in traditional medicine worldwide, as humans have been using plants as a natural remedy for a multitude of diseases for 60,000 years [1]. In particular, the plants from Amaranthaceae family biosynthesize several bioactive compounds with beneficial biological activities, including essential oils, betalains, terpenoids, and phenolic compounds [2]. Different phytochemical studies have verified the different biological activities associated with the plant extracts belonging to this family, such as antioxidant, antidiabetic, antitumor, antibacterial, anti-inflammatory, among others [3].

Specially, three Amaranthaceae species, namely: *Alternanthera sessilis* (L.) R.Br. ex Dc, *Dicliptera chinensis* (L.) Juss. and *Dysphania ambrosioides* (L.) Mosyakin and Clemants. These species have been little explored in terms of their phytochemical valorization. *A. sessillis* has been used in traditional Malaysian medicine, both as an infusion and as food, while in China, its leaves have even been used for the treatment of eye and skin diseases, snake bites, and wound healing [4]. *D. chinensis* has a major distribution in southern China, Bangladesh, northern India, and Vietnam [5], where it was traditionally used with detoxifying and diuretic purposes, thanks to the production of organic acids, flavonoids, terpenoids, steroids, and polysaccharides [6]. Finally, *D. ambrosioides*, distributed throughout South America, is known to be used in traditional medicine as a remedy for parasitic diseases, and it is still currently used to treat parasitosis because of the presence of ascaridol [7].

Due to the health-enhancing potential attributed to Amaranthaceae species, in this work the nutritional characterization and chemical composition, in terms of phenolic

**Citation:** Nuñez-Estevez, B.; Finimundy, T.C.; Carpena, M.; Barral-Martinez, M.; Calhelha, R.; Pires, T.C.S.P.; Otero, P.; Garcia-Perez, P.; Simal-Gandara, J.; Ferreira, I.C.F.R.; et al. Bioactive Compound Profiling and Nutritional Composition of Three Species from the Amaranthaceae Family. *Chem. Proc.* **2021**, *5*, 20. https:// doi.org/10.3390/CSAC2021-10563

Academic Editor: Giorgio Senesi

Published: 1 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

compounds, will be carried out. As a result, this research could be considered as the starting point for a more targeted search for bioactive compounds [8] biosynthesized by these underexplored plant species.

#### **2. Materials and Methods**

#### *2.1. Plant Material and Nutritional and Chemical Characterization*

The samples proceeding from the Amaranthaceae species involved in this work, *A. sessilis*, *D. chinensis*, and *D. ambrosioides*, were thoroughly washed, air-dried, crushed, and sieved to obtain plant homogenates, which were stored at −80 ◦C until use.

The nutritional characterization (ashes, proteins, lipids, and carbohydrates, as well as energy) of the three plants was carried out following the methodology adapted previously [9]. The determinations were carried out by duplicate, and results were expressed in terms of percentage of composition for ashes, proteins, lipids, and carbohydrates, whereas energy was expressed as the mean ± standard deviation (SD) in kcal/100 g dry weight (dw).

The chemical composition (total sugars, fatty acids, and organic acids) was evaluated following the methodology also described by Barros et al. (2013) [9]. The determinations were performed in duplicate, and the results were expressed as the mean ± SD in g/100 g dw for total sugars and organic acids composition, whereas fatty acids were expressed as the relative percentage of saturated fatty acids (SFAs), monounsaturated fatty acids (MUFAs), and polyunsaturated fatty acids (PUFAs).

#### *2.2. Sample Extraction and the Determination of Phenolic Compounds*

For the determination of phenolic compounds, 1 g of each sample was macerated using 50 mL of ethanol/water (80:20 *v*/*v*) as solvent. This mixture was stirred at room temperature for 1 h and then filtered. This process was repeated twice, and extracts were collected and concentrated at 40 ◦C in a rotary evaporator, to remove the alcoholic fraction. The aqueous phase was frozen and freeze-dried. For the identification of phenolic compounds, a Dionex Ultimate 3000 UPLC system (Thermo Scientific, San Jose, CA, USA) was used, following a previous methodology [10]. The determination was performed by a diode array detector (DAD) and mass spectrometry (MS) (LTQ XL mass spectrometer, Thermo Finnigan, San Jose, CA, USA) working in negative mode. Once identified and quantified, compounds were grouped by their parental skeleton, being expressed as luteolin derivatives (LD), apigenin derivatives (AD), kaempferol derivatives (KD), quercetin derivatives (QD), and isorhamnetin derivatives (ID), in mg/g dw.

#### **3. Results and Discussion**

#### *3.1. Nutritional Characterization*

The results for the nutritional composition of Amaranthaceae plants are shown in Figure 1. The inorganic content of all plants, represented by the ashes, showed similar values, being higher than those of other plants of the same family from the genus *Amaranthum* [11] For proteins, *D. chinensis* (16.8 g/100 g dw) and *A. sessilis* (16.1 g/100 g dw) show similar values, higher than those of *D. ambrosioides* (13.9 g/100 g dw). These values are comparable to other species such as *Chenopodium quinoa* (quinoa), and they were also higher than other cereals, such as wheat, maize, or rice [12] With respect to lipids, the values for *A. sessilis* (0.74 g/100 g dw) were very low compared with the other species, which ranged between 1.1–1.8 g/100 g dw, being comparable to the lipid content of fruits and vegetables [12]. Regarding the carbohydrate content, the results were also very similar between the three species: 68.8 g/100 g dw for *D. chinensis*, 73.2 g/100 g dw for *A. sessilis*, and 71.5 g/100 g dw for *D. ambrosioides*, being in accordance with the carbohydrate contents of *C. quinoa* and other cereals, as well as other foods, such as chocolate, flour or bread. [12] Finally, the energy value did not vary much either, ranging from 350–365 kcal/100 g dw with *A. sessilis* being the species with the highest energy intake.

**Figure 1.** Nutritional characterization (ashes, proteins, lipids, and carbohydrates), and energy determination of three Amaranthaceae plants: (**A**) *A. sessilis*, (**B**) *D. chinensis* and (**C**) *D. ambrosioides*. The results for nutrient content were expressed as relative content in percentage, whereas energy was expressed as mean ± SD, in kcal/100 g dw.

#### *3.2. Chemical Characterization*

The results for the chemical characterization of Amarathaceae species, in terms of total sugars, organic acids, and fatty acids contents are shown in Figure 2. The results for free sugars were, in decreasing order, 6.33 g/100 g dw for *D. chinensis*, 4.13 g/100 g dw for *A. sessilis*, and 0.34 g/100 g dw for *D. ambrosioides*. In this case, *A. sessilis* has the most similar content to that estimated for *C. quinoa* (2–3 g/100 g dw) [12].

**Figure 2.** Chemical characterization (total free sugar, organic acids, and fatty acids contents) of three Amaranthaceae plants: (**A**) *A. sessilis*, (**B**) *D. chinensis*, and (**C**) *D. ambrosioides*. Results for total sugars and organic acids were expressed as g/100 g dw, vertical bars indicate standard deviation. The results for fatty acids content were expressed as relative abundance, in percentage. SFA: saturated fatty acids, MUFA: monounsaturated fatty acids, PUFA: polyunsaturated fatty acids.

Organic acids were detected in all three plant species studied, with a total content of ~9.13 g/100 g dw for *D. chinensis*, 8.43 g/100 g dw for *A. sessilis*, and 5.43 g/100 g dw for *D. ambrosioides*. Oxalic acid stood out as the organic acid present in the highest concentrations in all species, especially in the case of *A. sessilis* (data not shown). This acid is associated with reduced dietary Ca2+ availability and various kidney diseases [13]. Succinic acid and fumaric acid were also detected in *D. chinensis*, although in lower proportions. Both acids are in high demand by the food, cosmetic and pharmaceutical industries [14]. In addition, in the case of *C. quinoa*, previous data showed the presence of oxalic, citric and fumaric acid [15], revealing a similar profile for this functional food in terms of organic acids content.

Concerning the relative abundance of fatty acids in Amaranthaceae plants (pie charts in Figure 2), SFAs, MUFAs, and PUFAs were detected in these species. According to the data obtained, the plant with the highest amount of SFA was *D. ambrosioides* with 73.1% of the total fatty acids (Figure 2A), followed by *A. sessilis* with 70.2% and *D. chinensis* with 47.4%. As for the MUFA, all plants exhibited very similar values between them, ranging 8.23–11.8%. Finally, the results for PUFAs showed that *D. chinensis* had the highest abundance with 44.4% (Figure 2B), while *D. ambrosioides* and *A. sessilis* presented a similar abundance (19.8% and 15.1%, respectively). According to the data, the most abundant fatty acid in the three species was hexadecanoic acid, with the range of abundance in the percentage of total fatty acids in the three plants being 32–40%. A previous study identified the fatty acids of *A. sessilis*, in which the second most abundant fatty acid was hexadecanoic acid [16]. Regarding the high content in PUFAs for *D. chinensis*, and due to the beneficial properties associated with these bioactive compounds as antioxidant and cardioprotective agents, it is suggested that this species presents the healthiest chemical profile.

#### *3.3. The Determination of Phenolic Compounds*

The phenolic profiling of hydroethanolic extracts of Amaranthaceae plants was performed by UPLC-DAD-ESI/MS, revealing that flavonoids were the most abundant family of phenolic compounds in these species. The results for the determination of phenolic compounds are shown in Figure 3. In the case of *A. sessilis* extracts, luteolin derivatives (LD) were the most abundant compounds, with concentrations of 9.7 mg/g extract, with luteolin-8-*C*-(rhamnosyl)ketodeoxyhexoside as the most prevalent derivative (Figure 3A). To a lesser extent, apigenin derivatives (AD), kaempferol derivatives (KD), and quercetin derivatives (QD) were also reported (<1.8 mg/g extract) (Figure 3A). This plant has two varieties distinguished by their colors, red and green, and previous studies have shown that the red variety has a better nutritional composition, a higher content of phenolic compounds, and a greater antioxidant capacity [12]. Thus, the phenolic composition of *A. sessilis* has been seen to be affected by the variety employed.

**Figure 3.** Identification of phenolic compounds (fatty acids, organic acids and tocopherols) of three plants belonging to the Amantharaceae family: (**A**) *A. sessilis*, (**B**) *D. chinensis* and (**C**) *D. ambrosioides*. LD: luteolin derivatives, AD: apigenin derivatives, KD: kaempferol derivatives, QD: quercetin derivatives, and ID: isorhamnetin derivatives.

With respect to *D. chinensis*, the extracts essentially contained different ADs, accounting for 10.82 mg/g extract (Figure 3B), being apigenin 6-C-glucoside-8-C-arabinoside the most prevalent compound. This is the first time, to the best of our knowledge, that the phenolic profiling of *D. chinensis* is determined, being spotted as a potential natural source of apigenin, largely characterized as a bioactive compound [17].

Finally, the results for *D. ambrosioides* (Figure 3C) showed that this was the only species presenting isorhamnetin derivatives (ID), which were the most abundant compounds in the hydroethanolic extracts (7.58 mg/g extract), and isorhamnetin-3-*O*-rutinoside were quantified as the main phenolic compound. Additionally, IDs, LDs, KDs, and QDs were also identified in this species in lower concentrations, as well as a lack of ADs (Figure 3C). In previous studies on *D. ambrosioides*, the highest concentration of phenolic compounds extracted was obtained by methanolic extracts, with 87.7 ± 1.4 μg of gallic acid equivalents/mg extract and 57 ± 1.4 μg quercetin equivalents/mg extract. Moreover, the

same authors identified quercetin as the most abundant phenolic compound on *D. ambrosioides* [18], which is in accordance with our results, since quercetin-*O*-rhamnosyl-pentoside was the second most abundant compound in the hydroethanolic extracts. This suggests a critical role of solvent on the extraction of phenolic compounds from Amaranthaceae plants.

#### **4. Conclusions**

In this work, the determination of nutritional and chemical characterization, as well as the phenolic profiling of three Amaranthaceae species largely used in traditional medicine was developed. In this regard, similar chemical profiles were obtained for all species, with comparable inorganic, protein, lipid, and carbohydrate contents. The results on fatty acid composition revealed that *D. chinensis* showed the healthier profile with a high proportion of PUFAs. Finally, the determination of phenolic compounds suggested a species-dependent biosynthesis of these compounds, with luteolin derivatives, apigenin derivatives, and isorhamnetin derivatives presenting as the most prevalent phytoconstituents on *A. sessilis*, *D. chinensis*, and *D. ambrosioides*, respectively. Overall, our results shed light on the characterization of these species from a nutritional point of view and suggested that Amaranthaceae species can be considered as sources of bioactive compounds to be applied in the food, cosmetic, and pharmacological industries.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/CSAC2021-10563/s1.

**Author Contributions:** Conceptualization, methodology, software validation, formal analysis, investigation, resources, data curation, writing—original draft preparation, writing—review and editing, visualization, supervision B.N.-E., T.C.F., M.C., M.B.-M., R.C., T.C.S.P.P., P.O., P.G.-P., J.S.-G., I.C.F.R.F., M.A.P. and L.B. All authors have read and agreed to the published version of the manuscript.

**Funding:** By EcoChestnut Project (Erasmus+ KA202) that supports the work of B.N.-E.; the program Grupos de Referencia Competitiva (GRUPO AA1-GRC 2018) that supports the work of M.B.-M.; Authors are grateful to the Ibero-American Program on Science and Technology (CYTED—AQUA-CIBUS, P317RT0003), to the Bio-Based Industries Joint Undertaking (JU) under grant agreement No 888003, and to the UP4HEALTH Project (H2020-BBI-JTI-2019) that supports the work of Paz Otero. and P. Garcia-Perez. The research leading to these results was funded by Xunta de Galicia supporting the program EXCELENCIA-ED431F 2020/12; to the Ibero-American Program on Science and Technology (CYTED—AQUA-CIBUS, P317RT0003). The JU receives support from the European Union's Horizon 2020 research and innovation program and the Bio-Based Industries Consortium. The project SYSTEMIC Knowledge Hub on Nutrition and Food Security has received funding from national research funding parties in Belgium (FWO), France (INRA), Germany (BLE), Italy (MIPAAF), Latvia (IZM), Norway (RCN), Portugal (FCT), and Spain (AEI) in a joint action of JPI HDHL, JPI-OCEANS and FACCE-JPI launched in 2019 under the ERA-NET ERA-HDHL (No. 696295). The authors are also grateful to FCT, Portugal, for financial support through national funds FCT/MCTES to the CIMO (UIDB/00690/2020); and L.B. and R.C. thank the national funding by FCT, P.I., through the institutional and individual scientific employment program-contract for their contracts.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** We would like to thank MICINN for supporting the Ramón y Cajal grant for M.A.P. (RYC-2017-22891) and University of Vigo for supporting the predoctoral grant of M.C. (Uvigo-00VI 131H 6410211).

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning †**

**Edgar G. Mendez-Lopez <sup>1</sup> , Jersson X. Leon-Medina 2,\* and Diego A. Tibaduiza <sup>1</sup>**


**Abstract:** Electronic tongue type sensor arrays are made of different materials with the property of capturing signals independently by each sensor. The signals captured when conducting electrochemical tests often have high dimensionality, which increases when performing the data unfolding process. This unfolding process consists of arranging the data coming from different experiments, sensors, and sample times, thus the obtained information is arranged in a two-dimensional matrix. In this work, a description of a tool for the analysis of electronic tongue signals is developed. This tool is developed in Matlab® App Designer, to process and classify the data from different substances analyzed by an electronic tongue type sensor array. The data processing is carried out through the execution of the following stages: (1) data unfolding, (2) normalization, (3) dimensionality reduction, (4) classification through a supervised machine learning model, and finally (5) a cross-validation procedure to calculate a set of classification performance measures. Some important characteristics of this tool are the possibility to tune the parameters of the dimensionality reduction and classifier algorithms, and also plot the two and three-dimensional scatter plot of the features after reduced the dimensionality. This to see the data separability between classes and compatibility in each class. This interface is successfully tested with two electronic tongue sensor array datasets with multi-frequency large amplitude pulse voltammetry (MLAPV) signals. The developed graphical user interface allows comparing different methods in each of the mentioned stages to find the best combination of methods and thus obtain the highest values of classification performance measures.

**Keywords:** electronic tongue; graphical user interface; feature extraction; dimensionality reduction; classification; machine learning

#### **1. Introduction**

The data set obtained from an MLAPV (multifrequency large amplitude pulse voltammetry) electronic tongue device comes from various types of sensors and their magnitudes can have different scales [1]. These signals are characterized by having high dimensionality [2]. This can cause problems in Machine Learning models, both in pattern recognition and in the accuracy of data classification [3]. Due to this, it is necessary to perform the correct processing of these data sets to obtain high precision values for the classification of liquid substances.

In 2020, Leon-Medina et al. [2] developed a methodology that seeks to improve the classification accuracy with an approach based on non-linear feature extraction of signals obtained with electronic tongue type sensor array devices. This methodology is composed of several stages: (1) Data unfolding, (2) Normalization, (3) Non-linear dimensionality

**Citation:** Mendez-Lopez, E.G.; Leon-Medina, J.X.; Tibaduiza, D.A. Development of a Pattern Recognition Tool for the Classification of Electronic Tongue Signals Using Machine Learning. *Chem. Proc.* **2021**, *5*, 21. https://doi.org/10.3390/ CSAC2021-10447

Academic Editor: Nicole Jaffrezic-Renault

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

reduction, (4) Classification by means of a supervised machine learning model and finally a (5) Cross validation [2]. The application of the methodology in each stage includes the execution of algorithms in the software Matlab®. These algorithms contain a series of parameters that must be configured. As a result of the application of the methodology, the value of the classification accuracy and the confusion matrix of the classification model used are obtained, together with their performance metrics.

Due to the number of stages and the different configuration options of the parameters in the algorithms, the need was generated to develop a tool that would facilitate the application of this methodology, guiding the user through the different stages and making the configuration of the algorithms more user-friendly. One of the main advantages of a graphical user interface (GUI) is that it makes an implemented system easy to use, understand and evaluate [4].

Section 2, describes two tests performed by the developed GUI, as well as the datasets used in each one and the operation of the GUI. Then, Section 3, illustrates the main findings obtained during the two tests applying the methodology of data processing through the GUI. Finally, Section 4 shows the main conclusions in data processing through the GUI.

#### **2. Materials and Methods**

The measurements of the responses of an electronic tongue system are discretized currents in time. In this way, a measurement is obtained at each instant of time for each of the electrodes that make up the electronic tongue device, obtaining a matrix of size *I* × *K* where *I* are the experimental tests and *K* are the time instants of the signal collected by each electrode. Due to the electronic tongue system has an array of sensors and taking *J* as the number of electrodes. A data unfolding procedure is executed to convert the three-dimensional matrix *I* × *J* × *K*, in a two-dimensional matrix *I* × *(J · K)* [2]. Figure 1 shows an illustrative graph of the Data Unfolding process.

**Figure 1.** Data unfolding procedure.

In this work, two tests with the developed tool are performed using two different datasets. These tests are described below:

For the first test, a dataset obtained by means of a MLAPV electronic tongue developed by Liu et al. [5] is used. The electronic tongue consisted of a platinum pillar auxiliary sensor, an Ag/AgCl reference sensor, and six working electrodes made of different materials, gold, platinum, palladium, titanium, tungsten, and silver. In the experiment, the fourth titanium electrode was damaged, so it was not considered in the data analysis [5]. Seven liquids or aqueous matrices were used to collect the data from the *first dataset*: (1) red wine, (2) Chinese liquor, (3) beer, (4) black tea, (5) oolong tea, (6) you maofeng and (7) you pu'er. Each one with three different concentrations (14%, 25% and 100%) of the original solution mixed with distilled water, to which three replications were made, that is, 9 samples for each liquid [2], for a total of 63 samples. With 2050 measurement points per sensor and

5 sensors in the electronic tongue, when performing the Unfolding procedure of the data (described above, see Figure 1), the dataset is composed of a matrix of size *63* × *10,250*.

The second test uses a dataset obtained from the study by Zhang et al. [6]. This second dataset contains the data collected from an MLAPV electronic tongue with five working electrodes made of gold, silver, palladium, tungsten and silver. The auxiliary electrode used is platinum pillar and the reference electrode is Ag/AgCl [7]. For this study, 13 liquids or aqueous matrices (number of samples) were used: beer (19), red wine (8), white liqueur (6), black tea (9), tea Maofeng (9), pu'er tea (9), Oolong tea (9), coffee (9), milk (9), cola (6), vinegar (9), medicine (6), and salt (6), for a total of 114 samples [6]. Like the first dataset, in the *second dataset* there are 2050 measurement points per sensor and 5 sensors in the electronic tongue, when performing the Unfolding procedure of the data, the second dataset has a size of *114* × *10,250*.

The developed GUI is an application made in Matlab® App Designer, it is made up of seven tabs. Only the first tab is enabled at the beginning of the GUI, as shown in Figure 2a). By means of the *Browser* button in the *Data Selection* section, the file containing the dataset previously ordered with the unfolding process is selected. Subsequently, the data is loaded in the GUI through the button *Load*, after this, the size of the dataset is shown in the GUI, Figure 2b) illustrates this process.

**Figure 2.** (**a**) GUI Initial state. (**b**) dataset selection.

With the dataset loaded, the *Browser* button is enabled in the *Class Labels Selection* to select, in the same way as was done with the dataset, the file *Class Labels*. Once this vector is loaded, the number of classes used can be viewed, see Figure 3a.

After selecting the data files, the *Normalization* tab is enabled, in which the method for data normalization can be selected, see Figure 3b. With normalized data, the *Dimensionality Reduction* tab is enabled where the Feature Extraction technique [8] to reduce the dimensionality of the data can be selected, additionally there is a *Parameters* **section** where it is possible to configure certain parameters depending on the selected dimensionality reduction technique, see Figure 3c. With the data in low dimensionality, the *Plot* tab is enabled for the selection and visualization of the variables in 2D and scatter plots, see Figure 3d. Simultaneously, the *Classification/Validation* tab is enabled where there are four classifiers, along with some parameters that can be configured depending on the selected classifier, see Figure 3e. Executing the classification stage, the *Cross Validation* section is enabled, which contains three validation techniques, see Figure 3f. At the end of the procedure, the *Results* tab is enabled, where the classification performance metrics [9] and the confusion matrix are shown, see Figure 3g. At the same time, the *History* tab is enabled, in which a summary of the different techniques and methods used in data processing is presented, see Figure 3h. Figure 3 shows the sequence of enabling the GUI tabs throughout the data processing in each of the stages.

**Figure 3.** Sequence of enabling the stages in the developed GUI of the tool for classification of electronic tongue signals. (**a**) *User Data Tab*: Selecting the dataset and vector from Class Labels; (**b**) *Normalization Tab*: Data Normalization; (**c**) *Dimensionality Reduction Tab*: Data dimensionality reduction; (**d**) *Plot Tab*: 2D and scatter graphics display; (**e**) *Classification Tab*: Classifier selection; (**f**) *Validation Tab*: Selection of the cross-validation method; (**g**) *Results Tab*: Visualization of the Confusion Matrix and metrics of the classification model; (**h**) *History Tab*: Summary of tests carried out.

In relation to the plots int the GUI, the data loaded in the GUI, as well as the normalized data and data after the dimensionality reduction process can be visualized. A table or graph visualization can be obtained by each experiment in the corresponding tabs. In Figure 4, the original data are observed, in the Normalization and Dimensionality Reduction stage, the graphs are made for sample 7 in the same stages.

**Figure 4.** Viewing data as a sample chart or table. (**a**) Original data; (**b**) Standardized data; (**c**) Data after dimensionality reduction.

#### **3. Results**

Through the GUI, the following tests are performed with the datasets described above, see Section 2.

#### *3.1. Comparison Plots 2D and Scatter*

In the *Plot* tab the 2D and scatter graphs obtained after applying a dimensionality reduction technique are displayed. Figure 5 shows the graphs obtained with three different dimensionality reduction techniques applied to the first dataset. Additionally, new graphs generated by selecting different dimensions are observed, the label corresponds to each class of liquid in the dataset. Each graph can be saved in a file independently. The parameters used for each dimensionality reduction technique are described below:

**Figure 5.** Data representation after dimensionality reduction. (**a**) Dimensionality reduction method = Isomap, Dim = 8, K = 54, Plot Dim1 2D = 1, 2 (Default), Plot Dim1 3D = 1, 2, 3 (Default), Plot Dim2 2D = 3, 4, Plot Dim2 3D = 4, 5, 6; (**b**) Dimensionality reduction method = Locally Linear Embedding (LLE), Dim = 8, K = 54, Plot Dim1 2D = 1, 2, Plot Dim1 3D = 1, 2, 3, Plot Dim2 2D = 4, 7, Plot Dim2 3D = 4, 6, 8; (**c**) Dimensionality reduction method = Laplacian Eigenmaps, Dim = 8, K = 54, Plot Dim1 2D = 1, 2, Plot Dim1 3D = 1, 2, 3, Plot Dim2 2D = 2, 8, Plot Dim2 3D = 3, 5, 7.

#### *3.2. Classification Accuracy Behavior*

Two tests are described below to observe the behavior of the classification accuracy. These tests are applied to the second dataset based on the developed methodology of [7]. First, the number of *k* neighbors of the *k*-NN Classifier is modified, varying its value from 1 to 16, but keeping the number of dimensions fixed at 8 in the PCA dimensionality reduction technique. In the second test, the number of PCA dimensions is varied from 3 to 16, but the number of neighbors is fixed equal to 2. In both tests, the group scaling method (GRPS) is used to normalize the data and 5-Fold Cross validation is performed. The results of the tests carried out are described below in Figure 6.

**Figure 6.** Confusion matrix results, and accuracy behavior varying the number of target dimensions and number of *k* neighbors. (**a**) Confusion matrix and performance metrics of the classification model for the Accuracy of 94.73% obtained in the first test with a parameter *k = 2*; (**b**) Confusion matrix and performance metrics of the classification model for the Accuracy of 50% obtained in the first test with a parameter *k = 16*; (**c**) Summary of the trials of the first trial displayed in the *History tab* of GUI; (**d**) Excel file exported from *History tab* for the first test; (**e**) Graph of Accuracy vs. Number of *k* Neighbors, obtained from the results in the first test. (**f**) Graph of Accuracy vs. Number of Dimensions, obtained from the results in the second test.

#### **4. Conclusions**

This work showed the development of a tool for the processing of data contained acquired by an electronic tongue type sensor array. First, the GUI design allows the user to be guided intuitively through the signal processing methodology by enabling the tabs, but at the same time it allows the user to choose the different techniques and methods, as well as the parameter configuration. Second, the GUI offers the visualization of the data, by means of tables or graphically, both the original data and those transformed in the Normalization and dimensionality reduction stages. Another advantage is the visualization of 2D and 3D scatter graphics, where the user can observe the distribution of the samples, according to the selected feature extraction technique, choosing between different combinations of dimensions. In the same way, this tool offers the visualization of the results in the confusion matrix and the performance classification metrics of the classification model, finally it provides a summary table of the tests carried out in such a way that the user can easily compare the results obtained.

**Author Contributions:** All authors contributed to the development of this work, specifically their contributions are as follow: conceptualization, D.A.T. and J.X.L.-M.; data organization and preprocessing, J.X.L.-M. and E.G.M.-L.; methodology, J.X.L.-M. and E.G.M.-L.; validation, J.X.L.-M. and D.A.T. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by FONDO DE CIENCIA TECNOLOGÍA E INNOVACION FCTeI DEL SISTEMA GENERAL DE REGALÍAS SGR. The authors express their gratitude to the Administrative Department of Science, Technology and Innovation—Colciencias with the grant 779—"Convocatoria para la Formación de Capital Humano de Alto Nivel para el Departamento de Boyacá 2017" for sponsoring the research presented herein.

**Informed Consent Statement:** Not applicable.

**Acknowledgments:** Jersson X. Leon-Medina is grateful with Colciencias and Gobernación de Boyacá for his PhD fellowship. Jersson X. Leon-Medina thanks Miryam Rincón Joya from the Department of Physics of the National University of Colombia and Leydi Julieta Cardenas Flechas, who is currently a Ph.D. student, for their introduction to the electronic tongue sensor array field of research.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Proceeding Paper* **Electronic Nose for Bladder Cancer Detection †**

**Heena Tyagi 1,\*, Emma Daulton 1, Ayman S. Bannaga 2,3 , Ramesh P. Arasaradnam 2,3,4,5 and James A. Covington <sup>1</sup>**


**Abstract:** This study outlines the use of an electronic nose as a method for the detection of VOCs as biomarkers of bladder cancer. Here, an AlphaMOS FOX 4000 electronic nose was used for the analysis of urine samples from 15 bladder cancer and 41 non-cancerous patients. The FOX 4000 consists of 18 MOS sensors that were used to differentiate the two groups. The results obtained were analysed using s MultiSens Analyzer and RStudio. The results showed a high separation with sensitivity and specificity of 0.93 and 0.88, respectively, using a Sparse Logistic Regression and 0.93 and 0.76 using a Random Forest classifier. We conclude that the electronic nose shows potential for discriminating bladder cancer from non-cancer subjects using urine samples.

**Keywords:** electronic nose; bladder cancer; AlphaMOS FOX 4000; VOCs

#### **1. Introduction**

Bladder cancer (BC) is the eighth most common cancer worldwide. In the UK, there were 12,434 new cases and 6458 fatalities in 2020 [1]. Fortunately, the survival rate for bladder cancer remains good, with almost every three out of four people surviving the disease for one or more years [2]. Even with this high survival rate, there has been no significant improvement over the past ten years. The most common BC screening methods are cystoscopy, urine cytology, and urine tests, including bladder tumour antigen (BTA) test, nuclear matrix protein 22 (NMP22), urinary bladder cancer antigen (UBC), and fibrin degradation products (FDP) [3,4]. Unfortunately, none of these are effective enough for early diagnosis of BC and they are both expensive and invasive [5,6]. Therefore, there is a need for a more disease-specific, non-invasive, highly sensitive and low-cost screening test for BC.

The use of Volatile Organic Compounds (VOCs) has provided a new perspective for the early detection of cancer. The alterations in VOCs emitted from the body reflect the changes inside the body caused by this disease. VOCs can be measured from a range of different biological sources including urine [7], saliva [8], breath [9], faeces [10] and blood [11]. Measuring VOCs can be simple and non-invasive, mapping well onto the needs of a screening test [12]. Different studies that have previously been performed to analyse VOCs to diagnose BC, have mainly focused on urine and faeces [13,14]. The most common approach is to use GC-MS (Gas Chromatography-Mass Spectrometry). However, the analysis time is long (tens of minutes) and it is expensive, both in terms of equipment and running costs [15]. An alternative is to use an electronic nose (eNose), an instrument designed to mimic the human olfactory system. The eNose is widely used in

**Citation:** Tyagi, H.; Daulton, E.; Bannaga, A.S.; Arasaradnam, R.P.; Covington, J.A. Electronic Nose for Bladder Cancer Detection. *Chem. Proc.* **2021**, *5*, 22. https://doi.org/10.3390/ CSAC2021-10438

Academic Editor: Nicole Jaffrezic-Renault

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

the food and beverage industry [16], environment control, pharmaceutical companies [17] and biomedical applications [18]. Several previous studies have been undertaken for the detection of bladder cancer using the eNose. Van De Goor et al. used breath to distinguish head and neck, colon and bladder cancer using an electronic nose [19]. Another study conducted by Bernabei et al. was able to identify 100% of patients with urinary tract cancer (bladder and prostate cancer combined) from healthy controls [20]. A further study showed that bladder cancer was identified with the use of fluorescence urinary VOCs detection with a sensitivity of 84.2% and a specificity of 87.8% [21].

In our study, we aimed to identify and test the potential of urinary biomarkers to distinguish between BC and non-cancerous groups using an electronic nose. This is the first study conducted using an AlphaMOS FOX 4000 eNose to identify bladder cancer from non-cancerous samples using urinary VOCs.

#### **2. Materials and Methods**

#### *2.1. Study Design*

A total of 56 patients were recruited at University Hospital Coventry and Warwickshire NHS Trust, UK. This study was approved by Coventry and Warwickshire and North-East Yorkshire NHS Ethics Committees (Ref. 18717 and Ref. 260179). Out of 56 patients, 15 were confirmed with BC, and 41 non-cancerous (symptoms suggestive of but excluded after further investigations). The demographics for these groups are shown in Table 1.

**Table 1.** Demographic for the study.


The samples were collected and stored in standard sterile specimen containers and frozen for 2 h at −80 ◦C. The samples were then shipped to the University of Warwick for testing, where the samples were defrosted in a laboratory fridge at 4 ◦C and 3 mL of sample aliquoted into 10 mL glass vials [22].

#### *2.2. AlphaMOS FOX 4000 (Toulouse, France)*

The AlphaMOS Fox 4000 is an eNose that comprises 18 commercial metal oxide sensors (MOS) distributed in three temperature-controlled chambers. There are 6 p-type sensors and 12 n-type sensors. The output of the sensors is measured as resistance. The FOX 4000 is fitted with a CombiPAL HS-100 auto-sampler using a 2.5 mL gas syringe. In testing, the samples were placed in the autosampler, then were agitated, and heated to 40 ◦C for 10 min. The headspace was then injected into the eNose at a rate of 200 mL/min into a flow of 150 mL/min of zero air. Each sample was analysed for 180 s by all the 18 MOX sensors.

#### *2.3. Statistical Analysis*

The sensor's responses were extracted using AlphaSoft (AlphaMOS v12.36) and then analysed using a MultiSens Analyzer (JLM Innovation GmbH, Tübingen, Germany) and RStudio (Version 1.4.1106). AlphaSoft is a software product developed to control AlphaMOS instruments. The output generated by the program was processed and exported in ASCII format. These files were further analysed using a MultiSens Analyzer (JLM Innovation GmbH, Germany) for multivariate analysis. Due to the high dimensionality of the data, the maximum change in resistance was extracted per sensor and used as the input features for a PCA (Principal Component Analysis) and an LDA (Linear Discriminant Analysis). The feature matrix was also exported and further processed using a custom

analysis pipeline created in RStudio. Here, 10-fold cross-validation was performed where the data was divided into 10 equally sized groups, with nine groups being used for model training and then applied to the 10th group as a test set. This was repeated 10 times until all the samples had been in a test group. SMOTE (Synthetic Minority Over-Sampling Technique) was performed on the data groups due to the high imbalance in the sample size for BC and the non-cancerous group. It generated syntenic balanced, which were then used to train the classifier [23]. This was undertaken inside the training fold so as not to affect the test result. Two classification models were applied to the data, specifically Random Forest (RF) and Sparse Logistic Regression (SLR), which we have successfully used before in similar studies [24]. From the resultant probabilities, statistical parameters were calculated, including Receiver Operator Characteristic (ROC) curve, area under the curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV).

#### **3. Results**

The typical output from the FOX 4000 eNose is shown in Figure 1, where each curve represents the response of a sensor to a BC urine sample. Here, the sensor response is defined as intensity, which is the change in resistance from the baseline divided by the baseline resistance.

**Figure 1.** A typical output from AlphaMOS FOX 4000 to a BC urine sample.

The PCA results obtained from the MultiSens Analyzer are shown in Figure 2. The data shows that most of the sample variance can be plotted in the first principal component (85.3%). Furthermore, there is reasonable separation (though not perfect) between the BC and non-cancerous groups.

**Figure 2.** PCA output from BC and non-cancerous groups.

LDA was also performed on the data, as shown in Figure 3, to show the maximum potential separation between BC and Non-cancer samples.

**Figure 3.** LDA output from BC and non-cancerous groups.

Finally, output statistical parameters were calculated, the results of which are shown in Table 2.

**Table 2.** Statistical output from FOX.


The highest separation between the BC and non-cancerous group was obtained using Sparse Logistic Regression with an AUC (Area Under the Curve) of 0.92. The sensitivity and specificity obtained were 0.93 and 0.88, respectively. The high sensitivity and specificity signify that the SLR correctly predicted 36 out of 41 non-cancerous patients and was able to identify 14 BC patients out of 15.

The RF classifier was able to achieve a sensitivity of 0.93 with a specificity of 0.76. The AUC for this classifier was 0.86. With the RF classifier, the model was able to correctly identify 31 of the non-cancerous samples and 14 BC samples out of 15.

This shows that eNose can distinguish cancer samples from non-cancerous samples. The ROC curve for random forest classifier distinguishing BC and the non-cancerous group is shown in Figure 4.

**Figure 4.** (**a**) illustrates ROC curve distinguishing BC from Non-cancerous group using Sparse Logistic Regression and (**b**) illustrates ROC curve distinguishing BC from Non-cancerous group using Random Forest classifier.

#### **4. Discussion**

In this paper, we have shown that the AlphaMOS FOX4000 Electronic nose was able to distinguish bladder cancer urine samples from non-cancerous samples based on their VOC profile. Our findings prove that eNose can be used to accurately separate these two groups. This is the first study to compare bladder cancer urine samples from noncancerous samples using this eNose, which has the advantage of being fully automated allowing large numbers of samples to be tested easily.

In our study, we were able to separate the bladder cancer urine samples from the noncancerous group with a high AUC of 0.92 and 0.86 using SLR and RF classifiers, respectively. For the classification of BC and non-cancerous groups using SLR, the sensitivity and the specificity obtained were 0.93 and 0.88, respectively. The threshold value for classification of the two groups was 0.13 and the *p*-value was <0.001. For the RF classifier, the sensitivity and specificity obtained were 0.93 and 0.76. The threshold value and *p*-value were 0.29 and <0.001, respectively.

We found that eNose was able to identify 14 BC samples out of 15, and 36 out of 41 non-cancerous samples using Sparse Logistic Regression classifier. However, our study is limited by the small number of samples, and we did not attempt to identify the specific VOC biomarkers involved. A previous study with a commercial eNose (Sensigent Cyranose 320) also showed high sensitivity and specificity [14], with comparable results to those found here. The sensitivity of eNose highly depends upon the material of the sensors and the environmental conditions, such as humidity and temperature [25]. A further limitation of our study was the lack of healthy controls for comparison. Further investigation is required to understand the specific chemicals associated with separating BC from noncancer and to test samples from a larger patient group.

**Author Contributions:** Conceptualization, H.T., J.A.C. and R.P.A.; methodology, H.T. and E.D.; formal analysis, H.T.; investigation, H.T. and E.D.; resources, A.S.B., J.A.C. and R.P.A.; data curation, H.T.; writing—original draft preparation, H.T.; writing—review and editing, H.T., E.D. and J.A.C. visualization, H.T. and J.A.C.; supervision, J.A.C. and R.P.A. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Institutional Review Board Statement:** This study was approved by Coventry and Warwickshire and North-East Yorkshire NHS Ethics Committees (Ref. 18717 and Ref. 260179).

**Informed Consent Statement:** Informed consents were obtained from all subjects involved in the study.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**


### *Abstract* **Voltammetric Detection of Mercury Ions at Poly(azulene-EDTA)-like Screen Printed Modified Electrodes †**

**George-Octavian Buica 1,\*, Georgiana-Luiza Tatu (Arnold) 1, Eleonora-Mihaela Ungureanu <sup>1</sup> and Gabriela Geanina Vasile <sup>2</sup>**


**Abstract:** In recent years, many applications have been developed for the detection of different toxic metals (As, Cd, Cu, Hg, Ni, Pb) in water samples. The classic analytical methods (ICP-MS, AAS with graphite furnace, ICP-EOS with ultrasonic nebulizer) not only require a longer analysis time (pretreatment of the sample and analysis), but also the costs involved are higher as a result of expensive equipment, costs associated with the method validation process and qualified staff. The use of modified electrodes for trace metals analysi from wastewater samples represents a modern approach which can provide accurate, fast results with selectivity and sensitivity. Thus, here we present the development of the previously obtained glassy carbon-modified electrodes based on poly(2,2 -(ethane-1,2-diylbis(2-(azulen-2-ylamino)-2-oxoethyl)azanediyl))diacetic acid, (polyL) in laboratory-scale studies. In order to analyze Hg(II) ion content from aqueous samples, an assembly system made of carbon screen-printed modified electrodes (SPEs) modified with polyL selective complexing polymeric films coupled with a portable potentiostat was used. The detection of Hg(II) ions was accomplished by chemical accumulation in an open circuit followed by anodic stripping using the differential pulse voltammetry technique. The calibration curve of the analytical method was situated in the range of 20 ppb to 150 ppb (y = 0.0051x + 0.123, R<sup>2</sup> = 0.9951), with a detection limit of 6 ppb. The precision value for the lower limit of the calibration curve was 20%, while for the upper limit, the value was 10.5%. The novelty of the method consists not only of the low cost of the analysis, but also of the possibility to provide real-time reliable information about the Hg(II) concentration in wastewater using a small and portable device.

**Keywords:** complexing polymer; modified electrode; voltammetric detection; mercury analysis

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10 .3390/CSAC2021-10630/s1.

**Author Contributions:** G.-O.B. and G.G.V. designed the study and methodology, supervised, and wrote the main manuscript text. E.-M.U. and G.-L.T. performed the electrochemical experiments. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Romanian National Authority for Scientific Research, UEFIS-CDI, under grant PN-III-P2-2.1-PED-2019-0730, contract no. 293PED/2020.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Citation:** Buica, G.-O.; Tatu, G.-L.; Ungureanu, E.-M.; Vasile, G.G. Voltammetric Detection of Mercury Ions at Poly(azulene-EDTA)-like Screen Printed Modified Electrodes. *Chem. Proc.* **2021**, *5*, 23. https:// doi.org/10.3390/CSAC2021-10630

Academic Editor: Nicole Jaffrezic-Renault

Published: 7 July 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Acknowledgments:** The authors gratefully acknowledge the financial support of the Romanian National Authority for Scientific Research, UEFISCDI, under grant PN-III-P2-2.1-PED-2019-0730, contract no. 293PED/2020.

**Conflicts of Interest:** The authors declare no conflict of interest.

### *Abstract* **Electrochemical Immunosensor for Simultaneous Determination of Emerging Autoimmune Disease Biomarkers in Human Serum †**

**Esther Sánchez-Tirado \* , Sara Guerrero, Araceli González-Cortés , Lourdes Agüí, Paloma Yáñez-Sedeño and José Manuel Pingarrón**

> Department of Analytical Chemistry, Faculty of Chemistry, University Complutense of Madrid, Avda. Complutense s/n, 28040 Madrid, Spain; sguerr02@ucm.es (S.G.); aracelig@ucm.es (A.G.-C.); mlagui@ucm.es (L.A.); yseo@ucm.es (P.Y.-S.); pingarro@ucm.es (J.M.P.) **\*** Correspondence: esther.sanchez@ucm.es

† Presented at the 1st International Electronic Conference on Chemical Sensors and Analytical Chemistry, 1–15 July 2021; Available online: https://csac2021.sciforum.net/.

**Abstract:** Rheumatoid arthritis is an autoimmune disorder characterized by persistent erosive synovitis, systemic inflammation and the presence of autoantibodies, which play an important role in inducing inflammation and joint damage, releasing pro-inflammatory cytokines from monocytes and macrophages [1,2]. Likewise, neutrophil activating protein-2 (CXCL7) is a platelet-derived growth factor belonging to the CXC chemokine subfamily, which is expressed in serum, synovial fluid and synovial tissue of patients developing rheumatoid arthritis during the first twelve weeks, being useful to reflect local pathological changes [3]. Besides, matrix metalloproteinase-3 (MMP-3), which is induced by inflammatory cytokines such as interleukin-1 (IL-1) and tumor necrosis factor alpha (TNFα) in rheumatoid synovium, degrades several extracellular matrix components of cartilage and plays central roles in rheumatoid joint destruction [4]. Therefore, monitoring serum CXCL7 and MMP-3 levels is useful for predicting the disease activity in rheumatoid arthritis. In this work, the construction and analytical performance of a dual electrochemical platform for the simultaneous determination of CXCL7 and MMP-3 is described. After the optimization of experimental variables involved in the preparation and implementation of the biosensor, the analytical usefulness of the developed configuration was demonstrated by its application to the determination of these biomarkers in serum samples from healthy individuals and patients with rheumatoid arthritis. To carry out the simultaneous determination of CXCL7 and MMP3 in human serum, just a fifty-fold sample dilution in PBS of pH 7.4 was required. In addition, the results obtained using the dual immunosensor were compared with those provided by the respective ELISA immunoassays, yielding no significant differences between the two methods. It is important to highlight that reagents consumption, four times smaller using the dual immunosensor than that required in the ELISA protocol, and an assay time of 2 h 50 min versus almost 5 h, counted in both cases after incubation of the capture antibody, are advantageous features of the dual immunosensor [5].

**Keywords:** rheumatoid arthritis; CXCL7; MMP-3; immunosensor; simultaneous determination; human serum samples

**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

**Supplementary Materials:** The following are available online at https://www.mdpi.com/article/10.339 0/CSAC2021-10437/s1.

**Author Contributions:** Conceptualization, P.Y.-S.; methodology, E.S.-T., S.G., A.G.-C. and L.A.; software, E.S.-T. and S.G.; validation, E.S.-T., S.G., A.G.-C. and L.A.; formal analysis, E.S.-T., S.G., A.G.-C. and L.A.; investigation, E.S.-T., S.G., A.G.-C. and L.A.; resources, A.G.-C., L.A. and P.Y.-S.; data curation, E.S.-T. and S.G.; validation, E.S.-T., S.G., A.G.-C. L.A., P.Y.-S.; writing—original draft

**Citation:** Sánchez-Tirado, E.; Guerrero, S.; González-Cortés, A.; Agüí, L.; Yáñez-Sedeño, P.; Pingarrón, J.M. Electrochemical Immunosensor for Simultaneous Determination of Emerging Autoimmune Disease Biomarkers in Human Serum. *Chem. Proc.* **2021**, *5*, 24. https://doi.org/ 10.3390/CSAC2021-10437

Academic Editor: Nicole Jaffrezic-Renault

Published: 30 June 2021

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

preparation, E.S.-T., S.G., A.G.-C., L.A. and P.Y.-S.; writing—review and editing, P.Y.-S. and J.M.P.; visualization, E.S.-T., S.G., A.G.-C. and L.A.; resources, A.G.-C., L.A. and P.Y.-S.; supervision, A.G.-C., L.A., P.Y.-S.; funding acquisition, A.G.-C., P.Y.-S. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by Spanish Ministerio de Ciencia, Innovación y Universidades, grant number RTI2018-096135-B-I00 and TRANSNANOAVANSENS-CM Program from the Comunidad de Madrid, grant number S2018/NMT-4349.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Guerrero, S.; Sánchez-Tirado, E.; Agúí, L.; González-Cortés, A.; Yáñez-Sedeño, P.; Pingarrón, J.M. Simultaneous determina-tion of CXCL7 chemokine and MMP3 metalloproteinase as biomarkers for rheumatoid arthritis. *Talanta* **2021**, *234*, 122705.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

