Next Article in Journal
Synthesis, Characterization, and Attrition Resistance of Kaolin and Boehmite Alumina-Reinforced La0.7Sr0.3FeO3 Perovskite Catalysts for Chemical Looping Partial Oxidation of Methane
Previous Article in Journal
Layered Double Hydroxide-Based Composites for Concerted Decontamination of Water
Previous Article in Special Issue
Boosting the Hydrogen Evolution Performance of Ultrafine Ruthenium Electrocatalysts by a Hierarchical Phosphide Array Promoter
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Catalysts
Volume 14
Issue 10
10.3390/catal14100669
catalysts-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Utilization of Banana Juice Biomass Waste to Activate CuO/NiO Composites for Electrocatalytic Oxidation of Urea in Alkaline Media

by
Irum Naz
1,
Aneela Tahira
2,
Arfana Begum Mallah
1,
Ihsan Ali Mahar
1,
Asma Hayat
1,
Aqeel Ahmed Shah
3,
Elmuez Dawi
4,*,
Atef AbdElKader
4,
Lama Saleem
5,
Rafat M. Ibrahim
6 and
Zafar Hussain Ibupoto
1,*
1
Institute of Chemistry, University of Sindh, Jamshoro 76080, Pakistan
2
Institute of Chemistry, Shah Abdul Latif University Khairpur Mirs, Sindh 66111, Pakistan
3
Wet Chemistry Laboratory, Department of Metallurgical Engineering, NED University of Engineering and Technology, University Road, Karachi 75270, Pakistan
4
Department of Mathematics and Sciences, College of Humanities and Sciences, Ajman University, Ajman P.O. Box 346, United Arab Emirates
5
Biomolecular Science, Earth and Life Science, Amsterdam University, De Boelelaan 1105, 1081 HV Amsterdam, The Netherlands
6
Physics Department, Faculty of Science, Taibah University, Al-Madaina Al Munawarah 42353, Saudi Arabia
*
Authors to whom correspondence should be addressed.
Catalysts 2024, 14(10), 669; https://doi.org/10.3390/catal14100669
Submission received: 21 August 2024 / Revised: 20 September 2024 / Accepted: 25 September 2024 / Published: 27 September 2024
(This article belongs to the Special Issue Study on Electrocatalytic Activity of Metal Oxides)
Browse Figures
Versions Notes

Abstract

:
The hydrothermal synthesis of CuO/NiO composites was conducted using banana fruit biomass waste. In this study, X-ray powder diffraction, scanning electron microscopy, and Fourier transform infrared spectroscopy were used to investigate the crystalline properties, shape structure, and functional group characterization of CuO/NiO composites. The typical morphology of the prepared materials consisted of irregular nanoparticles arranged into clusters of less than 200 nanometers in size. In spite of this, the CuO/NiO composites showed monoclinic CuO and cubic NiO phases and were therefore successfully synthesized. It was observed that rotten banana fruit juice had a significant impact on the particle size and crystal quality of CuO/NiO composites. This was due to the presence of capping, reducing, and stabilizing agents in banana fruit juice. Under alkaline conditions, the CuO/NiO composites were found to be highly electro catalytically active toward the oxidation of urea. Sample 2, which was prepared by adding 1.2 g of CuO decorated with NiO, showed a linear increase in urea detection ranging from 0.1 mM to 17 mM, with a limit of detection of 0.004 mM. Furthermore, sample 2 of the CuO/NiO composite demonstrated exceptional stability, selectivity, and reproducibility. Consequently, sample 2 of CuO/NiO could effectively detect urea in spinach, lotus root, milk, and curd. The improved performance of sample 2 of the CuO/NiO composite can be attributed to its favorable surface properties, which contain enriched active sites and a rapid charge transfer rate.

1. Introduction

In humans, protein catabolism produces urea, a by-product that prevents hyperglycemia and electrical complications associated with excessive NH4+ levels [1]. In normal circumstances, the concentration of urea in the blood ranges from 15 to 40 mg/dL (2.5 to 7.5 mM) [2]. It is important to note that since urea is one of the most common adulterants in milk, dairy producers require highly sensitive and selective detection methods. According to statistics, milk has an average protein content of 3.4%. Milk typically contains urea levels between 3.1 and 6.6 mM, while a urea level of 11.6 mM is considered acceptable by the Indian food safety authority [3]. It is also necessary to add additional urea to milk for its nitrogen content to be increased [4,5]. A low level of urea in bodily fluids may lead to renal dysfunction, cachexia, and liver dysfunction, whereas a high level of urea can cause urinary tract infections, kidney failure, and obstructions [6]. To avoid the adverse effects of urea on human health, it is crucial to control the precise level of urea manipulation in milk and the human body. Due to their excellent stability and electrochemical catalytic characteristics, transition metal oxides are highly desirable for the development of non-enzymatic sensors. In particular, CuO is being investigated as a potential platform for measuring different types of analytes [7,8]. Nickel oxide (NiO) is one of the most studied metal oxides due to its distinct electrochemical properties [9,10]. NiO nanostructures have been synthesized using a variety of standard techniques, such as solvothermal [11], precipitation [12], hydrothermal [13], sol–gel [14] and electrodeposition techniques [15]. In addition to these traditional preparation techniques, the green synthesis of NiO nanostructures offers a straightforward, versatile, affordable, and exceptionally well-produced method since plant extracts provide a variety of green capping, stabilizing, and reducing agents. [16,17]. There has been a recent increase in the popularity of non-enzymatic sensing techniques among researchers due to their special features, including stability, affordability, and selectivity. For this to be accomplished, functional materials must be designed so that they can catalyze analytes under acquired physiological conditions. Since there are currently few non-enzymatic urea-sensing configurations available, it is essential to develop new materials that have enhanced electrocatalytic properties. Urea has been quantified through a variety of analytical techniques, which include infrared spectroscopy [18], (HPLC) high-performance liquid chromatography [19], (GC) gas chromatograph [20], fluorimetry [21], the electroanalytical technique [22], (LCMS) liquid chromatography–mass spectrometry [23], and colorimetry [24,25]. The use of a hydrothermal process results in the gradual production of nanoparticles (NiO NPs) and their composite metal oxides (CuONPs). Due to this approach, a number of significant advantages are achieved, including the ability to modify the crystalline form, structure, and size by modifying the reaction conditions, such as adding green surface-modifying agents, such as banana juice. In addition to being affordable, readily available, and environmentally friendly, synthesis is also highly uniform in its molecular composition. In terms of economics, the hydrothermal process is a high-yielding process. The banana, scientifically known as Musa, is a tropical flowering plant belonging to the Musaceae family. The plant is characterized by a cluster of banana fruit at the top of the plant. Europeans and Americans are most familiar with the Cavendish type of eggplant, which is disease-resistant and mild in flavor. As a nutrient-dense food and a therapy for celiac disease, bananas may even be considered the first “super food”, as the American Medical Association supported them as food for children in the early 20th century. In addition to magnesium, potassium, and manganese, bananas also contain vitamin C, sugars, vitamin B6, and fiber [26]. A variety of bioactive compounds are present in bananas, such as phenolics, biogenic amines, carotenoids, and phytosterols [27,28]. Biomolecules like vitamin C, sugars, and vitamins have tendency to act as efficient reducing, capping, and reducing agents for modifying the surface properties of synthesized materials, thereby enhancing the catalytic sites, charge transfer rate, and stability in alkaline conditions. Consequently, NiO and CuO composite materials have been demonstrated to enhance the electrocatalytic oxidation of urea in 0.1 M of aqueous NaOH solution under the influence of such useful phytochemicals in banana fruit juice biomass waste during the hydrothermal process.

2. Results and Discussion

2.1. Physical Characterization of Various CuO/NiO Composites

An FTIR spectrometer was used to study the functional groups. Figure 1 illustrates the FTIR spectra of pure NiO, pure CuO, and their composites (CuO/NiO) using 0.6 g and 1.2 g of CuO, respectively. The broad O-H stretching vibration band is associated with hydrogen bonds, and the corresponding infrared band between 3600 and 3100 cm−1 corresponds to this vibration band.
In the case of pure NiO, the broad absorption peak located around 3549 cm−1 appeared due to hydrogen bonds between hydroxyl groups, with slight variations between samples [29]. An estimated 1025 cm−1 band was attributed to C-O stretching [30]. An additional band was obtained at 1626 cm−1 that was part of the C=O stretching group [31]. It is possible that these IR bands originated from banana fruit juice and unintentional impurities during the synthesis of these materials. A series of three distinct bands have been assigned to Cu (II)-O stretching frequencies at 517, 605, and 658 cm−1 via an FTIR analysis for samples containing CuO. The results are fully supported by the existing literature and correlate with previously published results [32]. A stretching vibration of carbon dioxide was observed at a frequency of 2200 cm−1. In the prepared samples, FTIR bands were observed at 917 cm−1 that were attributed to trans =C-H out-of-plane bending. The main purpose of using FTIR was to identify the typical metal–oxygen stretching vibrations, and they were successfully identified, hence giving the idea of the successful formation of respective metal oxides. Powder XRD was used to determine the phase purity and crystal quality of synthesized materials, and the diffraction patterns measured are shown in Figure 2. The experimental patterns of XRD were completely in agreement with the standard JCPDS cards for CuO and NiO (JCPDS card No. 03-065-2309 and JCPDS card No. 01-089-7130), respectively, and they demonstrated the monoclinic and cubic phases of CuO and NiO. CuO and NiO clearly reflect both in pure metal oxides and in their composites. This demonstrates a successful synthesis of hybrid materials, as shown in Figure 2. As the synthesized materials were free of any other impurities, diffraction patterns confirmed their high purity. The XRD analysis also showed that NiO is highly coated on CuO’s surface, resulting in weak reflection peaks for CuO in composite systems. Furthermore, the Debye Scherrer equation was employed to estimate the average crystallite size of pure NiO and CuO/NiO composites (sample 1 and sample 2) by considering the diffraction patterns 111, 200, and 220. The observed estimations of the crystallite size for pure NiO, sample 1, and sample 2 were 60.49, 44.75, and 44.62 nm, respectively. However, pure CuO diffraction patterns, such as 110, 002, and 111, were used to estimate the average crystallite size of 45.68 nm. These aspects of analysis using crystallographic results suggest that the synthesized materials exhibited typical nanoscale features.
The morphology of various synthesized materials, including pure NiO, pure CuO, sample 1, and sample 2 of the CuO/NiO composites, was studied using SEM (Figure 3). In Figure 3a,b, pure CuO exhibits a plate shape with dimensions of a few microns, whereas in Figure 3c,d, pure NiO exhibits a micro flake shape. Figure 3e,f illustrate sample 1’s shape structure, which is characterized by irregular particles with a size of a few nanometers. Figure 3g,h illustrate the irregular clustered structure of sample 2 with dimensions between 200 and 300 nm. Moreover, elemental analysis was carried out for sample 1, as well as elemental mapping, as shown in Figure 4a–e. It can be seen that sample 1 possesses Cu, Ni, and O as the main elements, and the distribution of these elements in sample 1 was found to be uniform. Similarly, EDS and elemental mapping was carried out on sample 2 of CuO/NiO, and the findings are shown in Figure 5a–e. It was seen that sample 2 contained Cu, Ni, and O as the main elements, and it was found that they had a homogenous distribution. The stability aspect of sample 2 was studied in terms of morphological changes after the electrochemical tests using SEM, as shown in Figure 5f. It can be noticed that the material has a tendency to retain the cluster shape-oriented nanoparticles, revealing the excellent stability of sample 2.

2.2. Electrochemical Sensing of Urea Using Various Composites of CuO/NiO

The non-enzymatic detection of urea in 0.1 M of NaOH aqueous solution was examined using various electrochemical modes. First, CV at a scan rate of 50 mV/s was used to identify highly electro catalytically active materials among the prepared materials. CV was used to test pure CuO, NiO, and CuO composites obtained by decorating NiO onto 0.6 g and 1.2 g of CuO, labeled sample 1 and sample 2, respectively, as shown in Figure 6a. It was observed that the prepared materials and bare glassy carbon electrode (BGCE) were poorly characterized with redox pairs, except for sample 2, which had an enhanced redox peak current in 0.1 M of NaOH aqueous solution. Additionally, the synthesized materials and the BGCE were tested by CV using 0.1 mM of urea solution in 0.1 M of NaOH at a scan rate of 50 mV/s, as shown in Figure 6b. A CV analysis indicated that composite systems including samples 1 and 2 have better urea sensing capability, whereas sample 2 is superior to sample 1 in terms of urea sensing capability. According to Figure 6b, the relative amounts of oxidation peak current generated by pure NiO, pure CuO, and sample 1 and sample 2 of the composites were 0.0000191 A, 0.0000156 A, 0.0000447 A, and 0.0000586 A, respectively. Accordingly, sample 2 was selected for full non-enzymatic urea sensing studies since it generated the highest oxidation peak current. In Figure 6c, pure NiO, pure CuO, and sample 2 are shown in 0.1 mM of urea separately. Sample 2 exhibited improved electrocatalytic activity due to its modified surface properties like abundant catalytic sites, rapid charge transport, and synergetic effect established between CuO and NiO in a hybrid system. It was demonstrated that nickel-based catalytic materials are highly active in alkaline environments [33]. Nickel-based catalysts have been combined with copper to promote their catalytic activity, and this combination is considered to be one of the most effective catalyst configurations [34,35,36,37,38]. A CV analysis was performed in 0.5 mM of urea on sample 2 for electrode surface kinetics with variations in sweeping scan rates, as shown in Figure 6d. Based on scan rates ranging from 10 mV/s to 310 mV/s, it was observed that the anodic peak potential increased with an increasing sweeping potential, such as from 0.59 V to 0.63 V when using scan rates ranging from 10 mV/s to 310 mV/s, as shown in Figure 6d. In Figure 6e, the redox peak current was plotted against the square root of the scan rate to evaluate the analytical capability of the sample 2-based electrode. As shown in Figure 6e, linear fitting demonstrated excellent analytical behavior with an increasing scan rate, confirmed diffusion controlled reactions on the modified electrode surfaces, and demonstrated outstanding analytical potential with a regression coefficient of 0.99. Generally, urea oxidation on our produced CuO/NiO composite system could be illustrated as shown below; the six-electron transfer process was involved during the oxidation of urea. The following intermediates have been observed to occur at Cu OOH or NiOOH surface sites during the possibility of urea oxidation reaction via the electro-catalytic procedure using catalysts based on Cu and Ni.
CO (NH2)2 + 6OH− → N2 + 5H2O + CO2 + 6e−
CO (NH2)2 + M → [M⋅CO (NH2)2] ads
[M⋅CO (NH2)2] ads + OH− → [M⋅CO (NH⋅NH2)] ads + H2O + e−
[M⋅CO (NH2NH)] ads + OH− → [M⋅CO (NH⋅NH)] ads + H2O + e−
[M⋅CO (NH2N)] ads + OH− → [M⋅ (NH⋅N)] ads + H2O + e−
[M⋅CO (NHN)] ads + OH− → [M⋅C (N2)] ads + H2O + e−
[M⋅CO (N2)] ads + OH− → [M⋅CO⋅OH] ads + N2 + e−
[M⋅COOH] ads + OH− → [M⋅COO] ads + H2O + e−
[M⋅COO] ads + 2OH− → CO32− + M
The linear range, limit of detection, selectivity, stability, and reproducibility of sample 2 based on the CuO/NiO composite were assessed following the scan rate study. The linear range verification was conducted using the CV electrochemical mode at 50 mV/s using sample 2 of CuO/NiO in various urea concentrations prepared in 0.1 M of NaOH through sequential addition, as shown in Figure 7a. According to Figure 7a, the oxidation peak current of urea increased linearly with the addition of urea concentration, indicating a highly concentration-dependent response of the proposed urea sensor using sample 2 as an electrocatalytic material. It was confirmed that sample 2 only favored the oxidation of urea under alkaline conditions despite the fact that the reduction peak was limited. As a result of the sluggish reaction kinetics of urea, the oxidation peak potential was slightly shifted upwards, requiring higher energy for oxidation to occur. Figure 7b illustrates the working linear range of the urea sensor based on the oxidation peak current measured for each urea concentration. The urea sensor showed a highly linear response to urea concentrations ranging from 0.1 mM to 17 mM and a regression coefficient of 0.99, thus demonstrating that the sensor is capable of accurately measuring the concentrations of urea with efficient and reliable analytical quality. According to [36,37,38,39], the detection limit of the proposed urea sensor was estimated at 0.004 mM, and the quantification limit was observed at 0.007 mM. A chronoamperometry measurement was employed in order to validate the CV results for the working linear range of the proposed urea sensor owing to its high sensitivity at a fixed potential for the quantification of urea, as shown in Figure 7c. As a result of the successive addition of urea to 0.1 M of NaOH solution, the urea was detected through chronoamperometry at a fixed potential of 0.6 V against Ag/AgCl. As a result of the sequential addition of urea, there was a significant increase in current production during urea detection, which demonstrated the sensitivity of sample 2 to detecting urea under alkaline conditions. According to Figure 7d, a linear plot was made by plotting the different concentrations detected by sample 2 against the various concentrations. Once again, a well-fitted linear response was observed for urea concentrations ranging from 1 mM to 7 mM with a regression coefficient of 0.99. Based on CV and chronoamperometry observations, sample 2 of CuO/NiO is a suitable study material for monitoring urea in a wide variety of real-world samples. According to these observations about sample 2, phytochemicals were utilized during the synthesis process in order to reduce the particle size, alter the shape orientation, enhance the charge transport at the interface, and produce a unique surface for favorable urea oxidation in alkaline media during synthesis. Linear sweep voltammetry (LSV) was also used in conjunction with CV and chronoamperometry to support the linearity experienced by the sample. According to Figure 8a, the linear range of the urea sensor was in the range of 1 mM to 8 mM. When urea concentrations were sequentially added, the LSV current increased. Figure 8b shows the linear plot for the assessment of the analytical accuracy of the results obtained from the urea sensor via LSV. Figure 8b shows that the linear range of the proposed urea sensor was well demonstrated for urea concentrations ranging from 1 mM to 8 mM. A CV analysis at 50 mV/s was carried out on sample 2 using five independently modified electrodes to determine its reproducibility in 0.5 mM of urea solution. It is extremely important to ensure reproducibility in order to ensure the accuracy of the signal for the urea concentration by successively modifying the electrodes. The reproducibility of five modified electrodes with sample 2 was shown to be highly accurate in measuring urea with negligible changes in the peak current and peak potential, as shown in Figure 8c. Furthermore, a bar graph is shown in Figure 8d to illustrate the variation in peak current between modified electrodes. According to the results, the variation in the peak current response of each modified electrode in 0.5 mM of urea was less than 3%, indicating that sample 2 showed a high degree of reproducibility for urea sensing in alkaline conditions.
As shown in Figure 9a, the stability of the modified electrode using sample 2 of CuO/NiO was also examined using a CV analysis at 50 mV/s in 0.5 mM of urea for 20 repeatable cycles. Through the cycling tests, it was determined that the modified electrode was highly stable due to the negligible loss of peak current and peak potential variation, so a firm adherence to sample 2 with GCE resulted in a high level of stability. As the material can become detached during several repeatable cycles, a stability study is extremely important because it can greatly influence the sensing performance of electrocatalytic materials. Therefore, the CV analysis demonstrated a very high degree of stability for the proposed material during stability testing. The bar graph in Figure 9b shows the urea oxidation peak variation and has a deviation of less than 4%, confirming the excellent stability of sample 2 of CuO/NiO during urea sensing in alkaline media. When it comes to non-enzymatic sensors, selectivity is one of the key parameters that need to be addressed before they can be applied to a real-world analyte analysis system. This purpose was achieved by measuring the selectivity of a urea sensor in the presence of uric acid, glucose, magnesium, and sodium ions in the microenvironment, as shown in Figure 9c. As shown in Figure 9c, the CV analysis was carried out at 50 mV/s using a urea concentration of 0.5 mM and its competing substances. This indicates the highly selective signal of sample 2 only for urea due to the large amount of oxidation peak current produced by the addition of urea compared to other interfering substances. The CV curves of those interfering substances without the use of urea in the NaOH solution were measured; hence, it was clearly found that there was no interference caused by the common interfering agents with the presented sensor configuration, as shown in Figure 9c. As a result of banana fruit juice, sample 2 has a selective response to urea. This could be attributed to the surface modification caused by banana fruit juice. A study of the charge transport between the electrode and electrolyte of various synthesized materials was carried out using electrochemical impedance spectroscopy (EIS). Figure 9d shows the simulation Nyquist plots for pure NiO, pure CuO, and samples 1 and 2. According to Figure 9d, sample 2 of CuO/NiO exhibited a small arc, indicating a low charge transfer resistance compared to the other materials. As shown in Figure 9d, the Nyquist plot is associated with an equivalent circuit containing elements such as solution resistance (Rs), charge transfer resistance (Rct), and constant phase element (CPE). Solution resistance is related to the conductivity of the electrolyte, the constant phase element is correlated with the capacitance of a double layer, and the charge transfer resistance is related to the conductivity of the material. Meanwhile, the charge transfer resistance rates of pure NiO, pure CuO, sample 1, and sample 2 were found to be 4250, 7340, 768, and 425 Ohms, respectively. From EIS perspectives, it is evident that sample 2 has high electrical conductivity along with enriched electrocatalytic properties, as indicated by the CV analysis, thus demonstrating that it can outperform functionality for non-enzymatic urea oxidation in alkaline media. As active surface area information plays a major role in the electrocatalytic performance of the prepared material, it is crucial to investigate it. As shown in Figure 10a–d, non-faradic CV analyses were conducted at varying sweeping scan rates using pure NiO, pure CuO, sample 1, and sample 2. According to these CV runs, there were no redox behaviors under these selected potential ranges, indicating a typical non-Faradic behavior and a highly suitable method for estimating electrochemical active surface areas (ECSAs). An approach based on the literature was used for the calculation of ECSA values for different materials [39]. The ECSA is typically calculated by plotting anodic and cathodic sides currents against the different scan rates and calculating the slope from the linear result. As shown in Figure 10e, the ECSA values obtained for pure NiO, pure CuO, sample 1, and sample 2 were 27.96 nF/cm2, 11.93 nF/cm2, 10.96 nF/cm2, and 31.07 nF/cm2, respectively. It is clear from this analysis that sample 2 was enriched abundantly in surface active sites for the favorable oxidation of urea, and thus, it was demonstrated as an alternative and efficient electrocatalytic material for the oxidation of urea in alkaline conditions. As a result of the EIS and ECSA studies, the CV-based performance of sample 2 of CuO/NiO is strongly supported and is clearly indicative of the potential use of this material for enzyme-free urea detection in real-world samples.
It was determined that sample 2 contained the highest ECSA value at 31.07 nF/cm2, followed by pure NiO at 27.96 nF/cm2, pure CuO at 11.93 nF/cm2, and sample 1 at 10.95 nF/cm2. Based on a real-life application of sample 2, curd, milk, spinach, and lotus root were tested.
Several samples, including lotus root and spinach, were chopped into tiny pieces and heated for two hours in 100 mL of deionized water at 75 °C. Once the extracts were gathered, they were used to quantify the urea concentration. A further 50 mL of (0.1 M NaOH) solution was added to 1 mL of extract. Total amounts of 5 μL, 10 μL, and 15 μL were used. In order to obtain the true solution for curd and milk separately, we used 1 mL of curd and milk in 50 mL of 0.1 M NaOH. The same procedure was repeated three times with 5 μL of solution (NaOH 0.1 M) in order to quantify the urea concentration in 50 mL of solution (NaOH 0.1 M). In Table 1, Table 2, Table 3 and Table 4, the results of the real sample analysis are presented. According to Table 1, Table 2, Table 3 and Table 4, the relative value of urea concentration increased as the extract value increased for every real sample. In Table 5, the performance of sample 2 towards urea is compared with that of various electrocatalytic materials that were recently reported. It is evident that the performance of CuO/NiO for urea detection is characterized by its significant linear range, low cost, scalability, ease of synthesis, and low energy consumption.

3. Materials and Methods

3.1. Chemical Reagents

All of the following substances were purchased from Sigma Aldrich, Karachi, Pakistan and used without further purification: sodium hydroxide, urea, ethanol, glucose, uric acid, copper chloride pentahydrate, sodium chloride, magnesium chloride, nickel chloride hexahydrate, sodium chloride, ascorbic acid, 5% Nafion, hydrochloric acid, and alumina paste. During the synthesis and non-enzymatic detection of urea, all solutions were prepared in deionized water.

3.2. Preparation of CuO/NiO Composites Using Banana Juice with Hydrothermal Method

Using mechanical stirring, 20 g of banana was mixed with 100 mL of deionized water. We prepared pristine CuO and NiO by dissolving 0.1 M copper chloride pentahydrate and nickel chloride hexahydrate in separate beakers containing a total volume of 100 mL, followed by adding 10 mL of 26% aqueous ammonia solution and 90 mL of deionized water to each beaker. Afterwards, both beakers were covered with aluminum sheets and placed in an electric oven for five hours at 95 °C. A simple filtration process was used to obtain the precipitates of copper and nickel hydroxides on the filter paper. The metal hydroxide was then thermally annealed for four hours at 500 °C in order to transform it into metal oxide. CuO/NiO composites were synthesized using 0.6 g and 1.2 g of CuO in 0.1 M of nickel chloride hexahydrate growth solution with 10 mL of 26% aqueous ammonia solution using 15 mL and 25 mL of banana juice, respectively. Both growth solutions were subjected to a hydrothermal reaction in an electric oven at 95 °C for five hours. Ordinary filter paper was used to collect the precipitates. This was followed by 4 h of thermal annealing at 500 °C.
Infrared (IR) spectrometers were used to analyze functional groups in the range of 400–4000 cm−1. The morphology and surface features of the as-synthesized materials were examined using a Joel JSM-6380 scanning electron microscope (SEM); however, the crystalline arrangements and purity of the as-prepared composite systems were investigated using powder X-ray diffraction (Panalytical Xpert pro).

3.3. Fabrication of Non-Enzymatic Sensing Electrode Using CuO/NiO Composites

Drop casting was used to deposit different NiO and CuO nanostructures and CuO/NiO hybrid materials onto a glassy carbon electrode (GCE) to produce a non-enzymatic electrode. Following this, the electrodes were tested in a three-electrode cell configuration to determine whether they were functional electrodes. There were three electrode cells set up along the modified GCE, with a reference electrode made of silver–silver chloride (Ag/AgCl) filled with 3.0 M of KCl and a counter electrode made of platinum wire. An alumina slurry (0.05 μM) was used to clean the GCE, followed by absolute ethanol and deionized water. The catalyst ink was prepared by mixing 3 mL deionized water with 30 μL 5% Nafion. This produced 10 mg of NiO and CuO nanostructures and their respective CuO/NiO composites. An amount of 10 μL of it was put onto the cleaned GCE and allowed to dry for twenty minutes. The urea concentrations were prepared in 0.1 M aqueous NaOH 0.1 M for non-enzymatic sensing. The schematics in Scheme 1 illustrate this.

4. Conclusions

In summary, rotten banana fruit juice served as a reducing, capping, and stabilizing agent for the design of new composite systems of CuO/NiO during modified hydrothermal processing. As a result of coating NiO onto 0.6 g and 1.2 g of CuO, two composite systems were prepared and labeled as samples 1 and 2. XRD, FTIR, and SEM analysis were used to study the physical properties of the materials. Sample 2 was found to be highly active for the oxidation of urea in alkaline media among the composites. Based on sample 2, the proposed urea sensor exhibited a wide linear range of 0.1 to 17 mM and a low limit of detection of 0.004 mM. In sample 2, excellent stability, reproducibility, selectivity, and practical application were demonstrated. It was shown through structural and electrochemical investigations that the CuO content, particle size, shape orientation, crystal defects, surface modification, rapid charge transport, and enriched surface active sites are all important factors in driving urea oxidation in alkaline conditions. It is proposed that rotten banana juice could be used as a green tool to synthesize new electrocatalytic materials using metal oxides for a wide range of electrochemical applications, including non-enzymatic sensing, energy conversion, and storage.

Author Contributions

Methodology, I.N., A.T., I.A.M., A.H., L.S. and R.M.I.; Software, A.A. and L.S.; Validation, I.N., A.T., A.B.M., A.H., A.A., L.S. and R.M.I.; Formal analysis, I.N. and E.D.; Investigation, A.A.S.; Resources, I.N., A.B.M., I.A.M., A.H., A.A.S., A.A., L.S. and R.M.I.; Data curation, A.T., A.B.M., I.A.M., A.A.S., A.A. and Z.H.I.; Writing—original draft, E.D. and Z.H.I.; Writing—review & editing, E.D. and Z.H.I.; Visualization, A.T. and R.M.I.; Supervision, A.B.M., I.A.M., A.H. and A.A.S.; Project administration, Z.H.I.; Funding acquisition, E.D. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data and materials can be made available upon request to corresponding author.

Acknowledgments

The authors would like to gratefully acknowledge the Higher Education Commission Pakistan for partial support under project NRPU/8350/8330. The authors would also like to acknowledge partial funding from Ajman University, Grant ID: DRG ref. 2023-IRG-HBS-2 (RESHUSC-001), RTG-2023-HBS. This publication is part of the R&D project PID2021-126235OB-C32 funded by MCIN/AEI/10.13039/501100011033/and FEDER funds.

Conflicts of Interest

The authors declare no competing interests in the presented research work.

References

  1. Häberle, J.; Boddaert, N.; Burlina, A.; Chakrapani, A.; Dixon, M.; Huemer, M.; Karall, D.; Martinelli, D.; Crespo, P.S.; Santer, R. Suggested guidelines for the diagnosis and management of urea cycle disorders. Orphanet J. Rare Dis. 2012, 7, 1–30. [Google Scholar] [CrossRef] [PubMed]
  2. Botewad, S.N.; Gaikwad, D.K.; Girhe, N.B.; Thorat, H.N.; Pawar, P.P. Urea biosensors: A comprehensive review. Biotechnol. Appl. Biochem. 2023, 70, 485–501. [Google Scholar] [CrossRef] [PubMed]
  3. Ezhilan, M.; Gumpu, M.B.; Ramachandra, B.L.; Nesakumar, N.; Babu, K.J.; Krishnan, U.M.; Rayappan, J.B.B. Design and development of electrochemical biosensor for the simultaneous detection of melamine and urea in adulterated milk samples. Sens. Actuators B Chem. 2017, 238, 1283–1292. [Google Scholar] [CrossRef]
  4. Rahmanian, R.; Mozaffari, S.A. Electrochemical fabrication of ZnO-polyvinyl alcohol nanostructured hybrid film for application to urea biosensor. Sens. Actuators B Chem. 2015, 207, 772–781. [Google Scholar] [CrossRef]
  5. Srivastava, R.K.; Srivastava, S.; Narayanan, T.N.; Mahlotra, B.D.; Vajtai, R.; Ajayan, P.M.; Srivastava, A. Functionalized multilayered graphene platform for urea sensor. ACS Nano 2012, 6, 168–175. [Google Scholar] [CrossRef]
  6. Sharma, A.; Rawat, K.; Bohidar, H.; Solanki, P.R. Studies on clay-gelatin nanocomposite as urea sensor. Appl. Clay Sci. 2017, 146, 297–305. [Google Scholar] [CrossRef]
  7. Wang, J. Amperometric biosensors for clinical and therapeutic drug monitoring: A review. J. Pharm. Biomed. Anal. 1999, 19, 47–53. [Google Scholar] [CrossRef]
  8. Ismail, K.M.; Hassan, S.S.; Medany, S.S.; Hefnawy, M.A. A facile sonochemical synthesis of the Zn-based metal–organic framework for electrochemical sensing of paracetamol. Mater. Adv. 2024, 5, 5870–5884. [Google Scholar] [CrossRef]
  9. Wang, B.; Chen, J.S.; Lou, X.W.; Wang, Z.; Madhavi, S. Green synthesis of NiO nanobelts with exceptional pseudo-capacitive properties. Adv. Energy Mater. 2012, 2. [Google Scholar] [CrossRef]
  10. Miraki, M.; Karimi-Maleh, H.; Taher, M.A.; Cheraghi, S.; Karimi, F.; Agarwal, S.; Gupta, V.K. Voltammetric amplified platform based on ionic liquid/NiO nanocomposite for determination of benserazide and levodopa. J. Mol. Liq. 2019, 278, 672–676. [Google Scholar] [CrossRef]
  11. Xu, J.; Wang, M.; Liu, Y.; Li, J.; Cui, H. One-pot solvothermal synthesis of size-controlled NiO nanoparticles. Adv. Powder Technol. 2019, 30, 861–868. [Google Scholar] [CrossRef]
  12. Kaur, N.; Singh, J.; Kaur, G.; Kumar, S.; Kukkar, D.; Rawat, M. CTAB assisted co-precipitation synthesis of NiO nanoparticles and their efficient potential towards the removal of industrial dyes. Micro Nano Lett. 2019, 14, 856–859. [Google Scholar] [CrossRef]
  13. Zhou, Q.; Lu, Z.; Wei, Z.; Xu, L.; Gui, Y.; Chen, W. Hydrothermal synthesis of hierarchical ultrathin NiO nanoflakes for high-performance CH4 sensing. Front. Chem. 2018, 6, 194. [Google Scholar] [CrossRef] [PubMed]
  14. Zorkipli, N.N.M.; Kaus, N.H.M.; Mohamad, A.A. Synthesis of NiO nanoparticles through sol-gel method. Procedia Chem. 2016, 19, 626–631. [Google Scholar] [CrossRef]
  15. Sohail, I.; Hussain, Z.; Khan, A.N.; Yaqoob, K. Synthesis and characterization of electrodeposited NiO thin film on electrode grade carbon plate for supercapacitor applications. Mater. Res. Express 2017, 4, 116412. [Google Scholar] [CrossRef]
  16. Ezhilarasi, A.A.; Vijaya, J.J.; Kaviyarasu, K.; Maaza, M.; Ayeshamariam, A.; Kennedy, L.J. Green synthesis of NiO nanoparticles using Moringa oleifera extract and their biomedical applications: Cytotoxicity effect of nanoparticles against HT-29 cancer cells. J. Photochem. Photobiol. B Biol. 2016, 164, 352–360. [Google Scholar] [CrossRef]
  17. Ezhilarasi, A.A.; Vijaya, J.J.; Kaviyarasu, K.; Kennedy, L.J.; Ramalingam, R.J.; Al-Lohedan, H.A. Green synthesis of NiO nanoparticles using Aegle marmelos leaf extract for the evaluation of in-vitro cytotoxicity, antibacterial and photocatalytic properties. J. Photochem. Photobiol. B Biol. 2018, 180, 39–50. [Google Scholar] [CrossRef]
  18. Jha, S.N.; Jaiswal, P.; Borah, A.; Gautam, A.K.; Srivastava, N. Detection and Quantification of Urea in Milk Using Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy. Food Bioprocess Technol. 2015, 8, 926–933. [Google Scholar] [CrossRef]
  19. Boggs, B.K.; King, R.L.; Botte, G.G. Urea electrolysis: Direct hydrogen production from urine. RSC Adv. 2009, 32, 4859–4861. [Google Scholar] [CrossRef] [PubMed]
  20. Xie, W.-Q.; Yu, K.-X.; Gong, Y.-X. Rapid and quantitative determination of urea in milk by reaction headspace gas chromatography. Microchem. J. 2019, 147, 838–841. [Google Scholar] [CrossRef]
  21. Nie, F.; Wang, N.; Xu, P.; Zheng, J. Determination of urea in milk based on N-bromosuccinimide–dichlorofluorescein postchemiluminescence method. J. Food Drug Anal. 2017, 25, 472–477. [Google Scholar] [CrossRef] [PubMed]
  22. Ma, W.J.; Luo, C.H.; Lin, J.L.; Chou, S.H.; Chen, P.H.; Syu, M.J.; Kuo, S.H.; Lai, S.C. A Portable Low-Power Acquisition System with a Urease Bioelectrochemical Sensor for Potentiometric Detection of Urea Concentrations. Sensors 2016, 16, 474. [Google Scholar] [CrossRef] [PubMed]
  23. Abernethy, G.; Higgs, K. Rapid detection of economic adulterants in fresh milk by liquid chromatography–tandem mass spectrometry. J. Chromatogr. A 2013, 1288, 10–20. [Google Scholar] [CrossRef] [PubMed]
  24. Deng, H.-H.; Hong, G.-L.; Lin, F.-L.; Liu, A.-L.; Xia, X.-H.; Chen, W. Colorimetric detection of urea, urease, and urease inhibitor based on the peroxidase-like activity of gold nanoparticles. Anal. Chim. Acta 2016, 915, 74–80. [Google Scholar] [CrossRef]
  25. Chowdhury, S.; Nugraha, A.S.; O’May, R.; Wang, X.; Cheng, P.; Xin, R.; Osman, S.M.; Hossain, M.S.; Yamauchi, Y.; Masud, M.K. Bimetallic metal-organic framework-derived porous one-dimensional carbon materials for electrochemical sensing of dopamine. Chem. Eng. J. 2024, 492, 152124. [Google Scholar] [CrossRef]
  26. The Nutrition Source. Available online: https://nutritionsource.hsph.harvard.edu/food-features/bananas/#:~:text=Source%20Of&text=One%20serving%2C%20or%20one%20medium,fiber%2C%20and%20450%20mg%20potassium (accessed on 24 September 2024).
  27. Singh, B.; Singh, J.P.; Kaur, A.; Singh, N. Bioactive compounds in banana and their associated health benefits—A review. Food Chem. 2016, 206, 1–11. [Google Scholar] [CrossRef]
  28. Kaimal, R.; Dube, A.; Al Souwaileh, A.; Wu, J.J.; Anandan, S. A copper metal–organic framework-based electrochemical sensor for identification of glutathione in pharmaceutical samples. Analyst 2024, 149, 947–957. [Google Scholar] [CrossRef]
  29. Radhakrishnan, A.A.; Beena, B.B. Structural and optical absorption analysis of CuO nanoparticles. Indian J. Adv. Chem. Sci 2014, 2, 158–161. [Google Scholar]
  30. Bayat, M.; Zargar, M.; Astarkhanova, T.; Pakina, E.; Ladan, S.; Lyashko, M.; Shkurkin, S.I. Facile biogenic synthesis and characterization of seven metal-based nanoparticles conjugated with phytochemical bioactives using fragaria ananassa leaf extract. Molecules 2021, 26, 3025. [Google Scholar] [CrossRef]
  31. Wan, Y.; Liang, Q.; Cong, T.; Wang, X.; Tao, Y.; Sun, M.; Li, Z.; Xu, S. Novel catalyst of zinc tetraamino-phthalocyanine supported by multi-walled carbon nanotubes with enhanced visible-light photocatalytic activity. RSC Adv. 2015, 5, 66286–66293. [Google Scholar] [CrossRef]
  32. Prakash, I.; Muralidharan, P.; Nallamuthu, N.; Venkateswarlu, M.; Satyanarayana, N. Preparation and characterization of nanocrystallite size cuprous oxide. Mater. Res. Bull. 2007, 42, 1619–1624. [Google Scholar] [CrossRef]
  33. Xu, W.; Wu, Z.; Tao, S. Urea-based fuel cells and electrocatalysts for urea oxidation. Energy Technol. 2016, 4, 1329–1337. [Google Scholar] [CrossRef]
  34. Ma, L.; Lin, Y.; Wang, Y.; Li, J.; Wang, E.; Qiu, M.; Yu, Y. Aligned 2-D nanosheet Cu2O film: Oriented deposition on Cu foil and its photoelectrochemical property. J. Phys. Chem. C 2008, 112, 18916–18922. [Google Scholar] [CrossRef]
  35. Xu, W.; Lan, R.; Du, D.; Humphreys, J.; Walker, M.; Wu, Z.; Wang, H.; Tao, S. Directly growing hierarchical nickel-copper hydroxide nanowires on carbon fibre cloth for efficient electrooxidation of ammonia. Appl. Catal. B Environ. 2017, 218, 470–479. [Google Scholar] [CrossRef]
  36. Rostami, H.; Rostami, A.A.; Omrani, A. An electrochemical method to prepare of Pd/Cu2O/MWCNT nanostructure as an anode electrocatalyst for alkaline direct ethanol fuel cells. Electrochim. Acta 2016, 194, 431–440. [Google Scholar] [CrossRef]
  37. Anantharaj, S.; Sugime, H.; Noda, S. Ultrafast growth of a Cu(OH)2–CuO nanoneedle array on Cu foil for methanol oxidation electrocatalysis. ACS Appl. Mater. Interfaces 2020, 12, 27327–27338. [Google Scholar] [CrossRef] [PubMed]
  38. Hefnawy, M.A.; Fadlallah, S.A.; El-Sherif, R.M.; Medany, S.S. Synergistic effect of Cu-doped NiO for enhancing urea electrooxidation: Comparative electrochemical and DFT studies. J. Alloys Compd. 2022, 896, 162857. [Google Scholar] [CrossRef]
  39. Parveen, M.; Tahira, A.; Mahar, I.A.; Bhatti, M.A.; Dawi, E.; Nafady, A.; Alshammari, R.H.; Vigolo, B.; Qi, K.; Ibupoto, Z.H. Green structure orienting and reducing agents of wheat peel extract induced abundant surface oxygen vacancies and transformed the nanoflake morphology of NiO into a plate-like shape with enhanced non-enzymatic urea sensing application. RSC Adv. 2023, 13, 34122–34135. [Google Scholar] [CrossRef]
  40. Amin, S.; Tahira, A.; Solangi, A.; Beni, V.; Morante, J.R.; Liu, X.; Falhman, M.; Mazzaro, R.; Ibupoto, Z.H.; Vomiero, A. A practical non-enzymatic urea sensor based on NiCo2O4 nanoneedles. RSC Adv. 2019, 9, 14443–14451. [Google Scholar] [CrossRef]
  41. Mangrio, S.; Tahira, A.; Chang, A.S.; Mahar, I.A.; Markhand, M.; Shah, A.A.; Medany, S.S.; Nafady, A.; Dawi, E.A.; Saleem, L.M. Advanced Urea Precursors Driven NiCo2O4 Nanostructures Based Non-Enzymatic Urea Sensor for Milk and Urine Real Sample Applications. Biosensors 2023, 13, 444. [Google Scholar] [CrossRef]
  42. Naz, I.; Tahira, A.; Shah, A.A.; Bhatti, M.A.; Mahar, I.A.; Markhand, M.P.; Mastoi, G.M.; Nafady, A.; Medany, S.S.; Dawi, E.A. Green Synthesis of NiO Nanoflakes Using Bitter Gourd Peel, and Their Electrochemical Urea Sensing Application. Micromachines 2023, 14, 677. [Google Scholar] [CrossRef] [PubMed]
  43. Farithkhan, A.; John, S.A. Three-Dimensional Coral-Like NiFe-Layered Double Hydroxides on Biomass-Derived Nitrogen-Doped Carbonized Wood as a Sensitive Probe for Nonenzymatic Urea Determination. ACS Sustain. Chem. Eng. 2022, 10, 6952–6962. [Google Scholar] [CrossRef]
  44. Salarizadeh, N.; Habibi-Rezaei, M.; Zargar, S.J. NiO–MoO3 nanocomposite: A sensitive non-enzymatic sensor for glucose and urea monitoring. Mater. Chem. Phys. 2022, 281, 125870. [Google Scholar] [CrossRef]
  45. Mangrio, S.; Tahira, A.; Mahar, I.A.; Parveen, M.; Hullio, A.A.; Solangi, D.A.; Khawaja, A.; Bhatti, M.A.; Ibupoto, Z.A.; Mallah, A.B. Electrochemical non-enzymatic urea sensing using polyvinylpyrrolidine derived highly electrocatalytic NiCo2O4 nanowires. J. Nanopart. Res. 2023, 25, 195. [Google Scholar] [CrossRef]
  46. Anil, A.G.; Naik, T.S.K.; Subramanian, S.; Ramamurthy, P.C. A novel non-enzymatic urea sensor based on the nickel complex of a benzimidazoyl pyridine derivative. J. Electroanal. Chem. 2021, 883, 115062. [Google Scholar] [CrossRef]
  47. Mishra, S.R.; Gadore, V.; Verma, R.; Singh, K.R.; Singh, J.; Ahmaruzzaman, M. In2S3 incorporated into CO32−@ Ni/Fe/Zn trimetallic LDH as a bi-functional novel nanomaterial for enzymatic urea sensing and removal of sulfur-containing pharmaceutical from aqueous streams. Chem. Eng. J. 2023, 475, 146207. [Google Scholar] [CrossRef]
  48. Ashakirin, S.N.; Haniff, M.A.S.M.; Zaid, M.H.M.; Razipwee, M.F.M.; Mahmoudi, E. Urease immobilized electrodeposited silver reduce graphene oxide modified screen-printed carbon electrode for highly urea detection. Measurement 2022, 196, 111058. [Google Scholar] [CrossRef]
  49. Wahab, R.; Khan, F.; Alam, M.; Ahmad, J.; Al-Khedhairy, A.A. Aluminum oxide quantum dots (Al2O3): An immediate sensing aptitude for the detection of urea. Inorg. Chem. Commun. 2023, 147, 110238. [Google Scholar] [CrossRef]
Catalysts 14 00669 g001
Figure 1. FTIR spectrum of composite materials made with varying concentrations of fruit banana juice, CuO/NiO sample 1, and CuO/NiO sample 2 compared with pure NiO and pure CuO.
Figure 1. FTIR spectrum of composite materials made with varying concentrations of fruit banana juice, CuO/NiO sample 1, and CuO/NiO sample 2 compared with pure NiO and pure CuO.
Catalysts 14 00669 g001
Catalysts 14 00669 g002
Figure 2. XRD patterns of diffraction for pure NiO and pure CuO nanostructured materials (a,b) without banana juice and (c,d), with 15 mL and 25 mL of banana fruit extract (sample 1 and sample 2), respectively. Blue circles are indicated reflections peaks of pure NiO and red circles are defined the pure CuO in sample 1 and sample 2.
Figure 2. XRD patterns of diffraction for pure NiO and pure CuO nanostructured materials (a,b) without banana juice and (c,d), with 15 mL and 25 mL of banana fruit extract (sample 1 and sample 2), respectively. Blue circles are indicated reflections peaks of pure NiO and red circles are defined the pure CuO in sample 1 and sample 2.
Catalysts 14 00669 g002
Catalysts 14 00669 g003
Figure 3. Images from SEM of pure NiO, pure CuO nano-structured materials made (a–d) without banana fruit juice with different levels of magnification and with (e,f) 15 mL and (g,h) 25 mL of banana fruit extract (sample 1 and sample 2), respectively.
Figure 3. Images from SEM of pure NiO, pure CuO nano-structured materials made (a–d) without banana fruit juice with different levels of magnification and with (e,f) 15 mL and (g,h) 25 mL of banana fruit extract (sample 1 and sample 2), respectively.
Catalysts 14 00669 g003
Catalysts 14 00669 g004
Figure 4. (a) EDS spectrum of sample 1 of NiO/CuO composite synthesized with 15 mL of banana fruit juice, (b–e) corresponding elemental mapping of O, Ni, and Cu.
Figure 4. (a) EDS spectrum of sample 1 of NiO/CuO composite synthesized with 15 mL of banana fruit juice, (b–e) corresponding elemental mapping of O, Ni, and Cu.
Catalysts 14 00669 g004
Catalysts 14 00669 g005
Figure 5. (a) EDS spectrum of sample 2 of NiO/CuO composite synthesized with 25 mL of banana fruit juice, (b–e) corresponding elemental mapping of O, Ni, and Cu, and (f) SEM image of sample 2 after stability test during electrochemical measurements.
Figure 5. (a) EDS spectrum of sample 2 of NiO/CuO composite synthesized with 25 mL of banana fruit juice, (b–e) corresponding elemental mapping of O, Ni, and Cu, and (f) SEM image of sample 2 after stability test during electrochemical measurements.
Catalysts 14 00669 g005
Catalysts 14 00669 g006
Figure 6. (a) Bare glassy carbon electrode (BGCE) CV curves of pure NiO, pure CuO nanostructured materials, and CuO/NiO composite materials utilizing various volumes of banana fruit juice, including 15 and 25 mL and sample 1 and sample 2 at 50 mV/s in 0.1 M of NaOH with and without urea. (b) CV curves were measured at 50 mV/s in 0.1 mM of urea for pure NiO, pure CuO, sample 1, and sample 2. (c) Pure NiO, pure CuO, and sample 2 are shown in 0.1 mM of urea separately. (d) Sample 2 CV curves measured in 0.1 mM of urea at various scan rates. (e) Anodic and cathodic peak currents are linearly plotted simultaneously versus the scan rate’s square root.
Figure 6. (a) Bare glassy carbon electrode (BGCE) CV curves of pure NiO, pure CuO nanostructured materials, and CuO/NiO composite materials utilizing various volumes of banana fruit juice, including 15 and 25 mL and sample 1 and sample 2 at 50 mV/s in 0.1 M of NaOH with and without urea. (b) CV curves were measured at 50 mV/s in 0.1 mM of urea for pure NiO, pure CuO, sample 1, and sample 2. (c) Pure NiO, pure CuO, and sample 2 are shown in 0.1 mM of urea separately. (d) Sample 2 CV curves measured in 0.1 mM of urea at various scan rates. (e) Anodic and cathodic peak currents are linearly plotted simultaneously versus the scan rate’s square root.
Catalysts 14 00669 g006
Catalysts 14 00669 g007
Figure 7. (a) CV curves of sample 2 at 50 mV/sec for different concentrations of urea made in NaOH (0.1 M). (b) Anodic peak current plotted linearly vs. several urea concentrations. (c) Chronoamperometric behavior of sample 2 to various urea concentrations. (d) Anodic peak current plotted linearly against various concentrations of urea.
Figure 7. (a) CV curves of sample 2 at 50 mV/sec for different concentrations of urea made in NaOH (0.1 M). (b) Anodic peak current plotted linearly vs. several urea concentrations. (c) Chronoamperometric behavior of sample 2 to various urea concentrations. (d) Anodic peak current plotted linearly against various concentrations of urea.
Catalysts 14 00669 g007
Catalysts 14 00669 g008
Figure 8. (a) LSV curves of sample 2 at 50 mV/s in 0.1 M NaOH; (b) the oxidation peak current plotted linearly against various concentrations of urea; (c) the reproducibility of various modified electrodes of sample 2 (0.5 mM urea); (d) the reproducibility bar graph of sample 2.
Figure 8. (a) LSV curves of sample 2 at 50 mV/s in 0.1 M NaOH; (b) the oxidation peak current plotted linearly against various concentrations of urea; (c) the reproducibility of various modified electrodes of sample 2 (0.5 mM urea); (d) the reproducibility bar graph of sample 2.
Catalysts 14 00669 g008
Catalysts 14 00669 g009aCatalysts 14 00669 g009b
Figure 9. (a) CV curves at 50 mV/s showing stability of sample 2 (0.5 mM urea); (b) variation in peak current through bar graph of each electrode. (c) Selectivity measured utilizing CV curves at 50 mV/s of sample 2 in alternative environment that interferes. (d) EIS Nyquist plots of pure NiO, pure CuO, sample 1, and sample 2 in 0.5 mM of urea.
Figure 9. (a) CV curves at 50 mV/s showing stability of sample 2 (0.5 mM urea); (b) variation in peak current through bar graph of each electrode. (c) Selectivity measured utilizing CV curves at 50 mV/s of sample 2 in alternative environment that interferes. (d) EIS Nyquist plots of pure NiO, pure CuO, sample 1, and sample 2 in 0.5 mM of urea.
Catalysts 14 00669 g009aCatalysts 14 00669 g009b
Catalysts 14 00669 g010
Figure 10. (a–d) CV curves of non-faradaic for pure NiO, pure CuO, sample 1, and sample 2 at different scan rates in urea (0.5 mM); (e) linear plot for the quantification of ECSA showing the difference between the current density on the anodic and cathodic sides against the scan rate.
Figure 10. (a–d) CV curves of non-faradaic for pure NiO, pure CuO, sample 1, and sample 2 at different scan rates in urea (0.5 mM); (e) linear plot for the quantification of ECSA showing the difference between the current density on the anodic and cathodic sides against the scan rate.
Catalysts 14 00669 g010
Catalysts 14 00669 sch001
Scheme 1. Generalizing demonstration of non-enzymatic urea sensor using CuO/NiO composites.
Scheme 1. Generalizing demonstration of non-enzymatic urea sensor using CuO/NiO composites.
Catalysts 14 00669 sch001
Table 1. Real sample of lotus root extract: lotus root (50 g) + (100 mL) DI water was put on hotplate for 3 h at 75 °C; 1 mL of extract was used in 50 mL of NaOH (0.1 M).
Table 1. Real sample of lotus root extract: lotus root (50 g) + (100 mL) DI water was put on hotplate for 3 h at 75 °C; 1 mL of extract was used in 50 mL of NaOH (0.1 M).
ExperimentVolume of Extract in μLConcentration in (mM)Signal
15 μL101.47 × 10−4
210 μL151.67 × 10−4
315 μL171.73 × 10−4
Table 2. Real sample of spinach extract: spinach (50 g) + (100 mL) DI water was put on hotplate for 3 h at 75 °C; 1 mL of extract was used in 50 mL of NaOH (0.1 M).
Table 2. Real sample of spinach extract: spinach (50 g) + (100 mL) DI water was put on hotplate for 3 h at 75 °C; 1 mL of extract was used in 50 mL of NaOH (0.1 M).
ExperimentVolume of Extract in μLConcentration in (mM)Signal
15 μL0.17.2 × 10−5
210 μL0.57.77 × 10−5
315 μL18.18 × 10−5
Table 3. Real sample of curd solution: 1 mL of curd + 50 mL of NaOH (0.1 M).
Table 3. Real sample of curd solution: 1 mL of curd + 50 mL of NaOH (0.1 M).
ExperimentVolume of Extract in μLConcentration in (mM)Signal
15 μL81.39 × 10−4
27 μL91.43 × 10−4
39 μL111.51 × 10−4
Table 4. Real sample of milk solution: 1 mL of milk + 50 mL of NaOH (0.1 M).
Table 4. Real sample of milk solution: 1 mL of milk + 50 mL of NaOH (0.1 M).
ExperimentVolume of Extract in μLConcentration in (mM)Signal
110 μL0.17.24 × 10−5
215 μL29.19 × 10−5
320 μL3.51.06 × 10−4
Table 5. Comparing non-enzymatic urea performance of sample 2 of CuO/NiO to that of latest urea sensors/biosensors.
Table 5. Comparing non-enzymatic urea performance of sample 2 of CuO/NiO to that of latest urea sensors/biosensors.
Electrode MaterialLinear Range (mM)LODReference
NiO Nanoplates0.1 mM to 13 mM0.003 mM[39]
NiCo2O4 nanoneedles0.01 mM to 5 mM1 μM[40]
NiCo2O40.1 mM to 10 mM0.006 mM[41]
NiO Nanoflakes1 mM to 9 mM0.02 mM[42]
NF-LDH0.5 mM to 8 mM0.114 mM[43]
NiO–MoO31 mM to 10 mM0.86 μM[44]
NiCo2O4 Nanowires1 mM to 16 mM0.01 mM[45]
NiBzimpy/MCPE0.01 mM to 0.1 mM1.5 μM[46]
In2S3/LDH/ITO@urease1 μM to 240 μM0.246 μM[47]
Urease@AgrGO/SPCE0.001 mM to 10 mM0.162 μM[48]
γ-Al2O3QDs3.56 μM to 16.52 μM0.110 μM[49]
CuO/NiO0.1 mM to 17 mM0.00 4 mMPresent Work
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Naz, I.; Tahira, A.; Mallah, A.B.; Mahar, I.A.; Hayat, A.; Shah, A.A.; Dawi, E.; AbdElKader, A.; Saleem, L.; Ibrahim, R.M.; et al. Utilization of Banana Juice Biomass Waste to Activate CuO/NiO Composites for Electrocatalytic Oxidation of Urea in Alkaline Media. Catalysts 2024, 14, 669. https://doi.org/10.3390/catal14100669

AMA Style

Naz I, Tahira A, Mallah AB, Mahar IA, Hayat A, Shah AA, Dawi E, AbdElKader A, Saleem L, Ibrahim RM, et al. Utilization of Banana Juice Biomass Waste to Activate CuO/NiO Composites for Electrocatalytic Oxidation of Urea in Alkaline Media. Catalysts. 2024; 14(10):669. https://doi.org/10.3390/catal14100669

Chicago/Turabian Style

Naz, Irum, Aneela Tahira, Arfana Begum Mallah, Ihsan Ali Mahar, Asma Hayat, Aqeel Ahmed Shah, Elmuez Dawi, Atef AbdElKader, Lama Saleem, Rafat M. Ibrahim, and et al. 2024. "Utilization of Banana Juice Biomass Waste to Activate CuO/NiO Composites for Electrocatalytic Oxidation of Urea in Alkaline Media" Catalysts 14, no. 10: 669. https://doi.org/10.3390/catal14100669

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop