Next Article in Journal
Selective Label-Free Electrochemical Aptasensor Based on Carbon Nanotubes for Carbendazim Detection
Next Article in Special Issue
Study on Denoising Method of Photoionization Detector Based on Wavelet Packet Transform
Previous Article in Journal
Pyrene-Based Fluorescent Probe for “Off-on-Off” Sequential Detection of Cu2+ and CN− with HeLa Cells Imaging
Previous Article in Special Issue
A Novel Gas Recognition Algorithm for Gas Sensor Array Combining Savitzky–Golay Smooth and Image Conversion Route
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Chemosensors
Volume 11
Issue 2
10.3390/chemosensors11020116
chemosensors-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

Cross-Linked SnO2 Nanosheets Modified by Ag Nanoparticles for Formaldehyde Vapor Detection

by
Huaipeng Weng
1,
Xumeng Dong
1,
Yufeng Sun
1,*,
Haibo Ren
1,
Jiarui Huang
2 and
Sang Woo Joo
3,*
1
Key Lab for High Performance Nonferrous Metals of Anhui Province, Anhui Polytechnic University, Wuhu 241000, China
2
Key Laboratory of Functional Molecular Solids of the Ministry of Education, Anhui Laboratory of Molecule-Based Materials, College of Chemistry and Materials Science, Anhui Normal University, Wuhu 241002, China
3
School of Mechanical Engineering, Yeungnam University, Gyeongsan 712749, Gyeoungbuk, Republic of Korea
*
Authors to whom correspondence should be addressed.
Chemosensors 2023, 11(2), 116; https://doi.org/10.3390/chemosensors11020116
Submission received: 11 January 2023 / Revised: 29 January 2023 / Accepted: 1 February 2023 / Published: 4 February 2023
(This article belongs to the Special Issue Chemical Sensors for Volatile Organic Compound Detection)
Browse Figures
Versions Notes

Abstract

:
Ag@SnO2 nanosheets were prepared through a hydrothermal method followed by heat treatment and a liquid reduction process. Many Ag nanoparticles (Ag NPs) were dispersed uniformly over the surface of the SnO2 nanosheets. The thickness of the SnO2 nanosheets was approximately 10 nm. After decoration with Ag NPs, the Ag@SnO2 nanosheet sensors exhibited improved gas-sensing behaviors compared to the pure SnO2 nanosheet sensor. The response of cross-linked SnO2 nanosheets decorated by Ag NP sensors for 100 ppm formaldehyde vapor was up to 101.4, which was double that (45.5) of the pure SnO2 nanosheet sensor. The response and recovery times of the Ag@SnO2 sensor were 21 s and 23 s, respectively. The Ag@SnO2 nanosheet sensors showed reasonable cycling stability, as demonstrated by testing with 100 ppm formaldehyde 10 times. The superior gas-sensing behaviors of the Ag@SnO2 sensor were due to the large specific surface area, cross-linked nanostructure, and synergistic effect of the Ag NPs with huge sensitizing active sites and numerous SnO2 nanosheets.

1. Introduction

Formaldehyde is an important chemical raw material often used in the chemical, textile, and wood industries [1,2,3]. However, formaldehyde is toxic, flammable, and volatile. Repeated exposure to even low formaldehyde concentrations (e.g., 0.08 ppm) can harm the nervous and immune systems of humans [4,5]. Therefore, a gas sensor with high sensitivity and a low detection limit is needed to detect formaldehyde gas.
In recent years, researchers reported that gas sensors based on metal oxide semiconductors (MOS) have higher sensitivity, better selectivity, and lower manufacturing cost. Common gas-sensing materials include ZnO [6], SnO2 [7], MoO3 [8], NiO [9], and In2O3 [10]. Among these MOSs, SnO2 is an n-type semiconductor with a bandgap of 3.6 eV, a rapid electron transmission rate (160 cm2/V s), and desirable physicochemical properties [11,12,13].
Controlling the morphology of the SnO2 to improve the sensor response is a viable strategy. Based on the sensing mechanism, the sensing property is also related to its morphology and the size of the nanocrystals. SnO2 materials with uniform morphology and adequate size would improve the performance of gas sensors. Therefore, SnO2 materials with different morphologies, such as nanosphere [14], nanowire [15], nanorod [16], nanotube [17], nanosheet [12], and micro/nanoflower [18], were constructed to enhance their sensing properties. Li et al. constructed a sensor using SnO2 nanospheres prepared through a hydrothermal method [19]. The sensor showed a strong response (38.3) for 100 ppm formaldehyde at 200 °C, and the response/recovery times were 17 s/25 s, respectively. The exceptional sensing performance was ascribed to the favorable porous nanosphere structure providing numerous surface-active sites and convenient channels for the easy adsorption and diffusion of gas molecules. Hu et al. utilized flower-like SnO2 as a sensing material [20]. The sensor response to 100 ppm formaldehyde reached 34.6 with response/recovery times of 64 s/10 s. The flower-like structure can supply many active sites for accelerating the diffusion and transport of gas molecules and electrons. Among these morphologies, 2D nanosheets have a large specific surface area, suitable surface energy, and stable structure, which can significantly improve the response, shorten the response/recovery times, and reduce the detection limit [21]. Xu et al. prepared SnO2 nanosheets using a hydrothermal method [12]. The nanosheet sensor displayed a strong response to 79.5 to 100 ppm formaldehyde at 200 °C, with response and recovery times of 42 s and 112 s, respectively. Yu et al. synthesized SnO2 nanosheets using a solvothermal method [22]. In addition, Meng et al. used the stepwise recognition method of qualitative classification and quantitative regression to improve the recognition rate, the resolution, and the generalization performance of the SnO2 sensor, which is a new way of on-line rapid sensor detection for drug-producing chemicals [23]. The SnO2 sensor exhibited a response of 7 to 100 ppm formaldehyde at 200 °C, with short response/recovery times (1 s/6 s). Many researchers have made progress in improving the sensor performance by synthesizing various morphologies of tin dioxide. Nevertheless, it is still necessary to synthesize hierarchical SnO2 with a large specific surface area to further enhance the sensor performance.
The following methods are often used to improve the practicality of SnO2-based gas sensors by reducing the working temperature and shortening the response/recovery times: element doping [24], construction of heterojunctions [25,26], and noble metal modification [27]. Among them, the decoration of noble metals on sensing material is an effective strategy to promote the sensing properties. The commonly used noble metals include Pd [28], Au [29], Pt [30], and Ag [31]. For example, Li et al. modified SnO2 nanosheets with Pd/Au nanoparticles [32]. The Pd NPs-modified SnO2 sensor exhibited a strong response of 125 and a response time of 68 s toward 100 ppm formaldehyde at 110 °C. Tang et al. synthesized Pt-modified SnO2 nanospheres with a mean diameter of 30–50 nm [14]. As sensing materials, it achieved a response of 70.1 toward 100 ppm formaldehyde with an extremely short response time (5 s) at 200 °C. The enhanced sensing property was mainly ascribed to two reasons. First, the electron transfer speed can be accelerated because of the electron sensitization effect. Second, the synergistic effect of noble metals and SnO2 promotes the redox reactions on the surface of the materials [33]. Ag is relatively inexpensive with stable chemical properties compared with other noble metals. Therefore, Ag is a promising alternative to modify sensing materials and improve the sensing performance. For example, Liu et al. [34] attached Ag NPs to the surface of SnO2 using a liquid reduction method. The resulting sensor exhibited a strong response to volatile organic compounds (VOCs). The response of the Ag@SnO2 sensor was 14.4 for 10 ppm formaldehyde at the optimal operating temperature (150 °C), while the response of pure SnO2 was only 3.2 at the same working temperature. Many strategies to decorate SnO2 with noble metals have been developed. Despite many achievements in the preparation of Ag@SnO2 sensing materials, there is a considerable focus on SnO2 hierarchical structures modified by Ag NPs with improved sensing performance for VOCs.
In this study, numerous cross-linked SnO2 nanosheets loaded with uniform Ag NPs were prepared via a hydrothermal method combined with a heat treatment and liquid reduction process. Compared with pure SnO2 nanosheets, Ag@SnO2 nanosheets showed enhanced sensing properties towards VOCs. This paper discusses the effect of Ag NPs on the sensing performance, including the sensing mechanism of the Ag@SnO2 nanosheets.

2. Experimental Details

2.1. Preparation of Cross-Linked SnO2 Nanosheets

First, 1.0 g of SnCl2·2H2O was dissolved in 20 mL deionized water with stirring, followed by adding 10 mL NaOH (1.5 mol l−1) solution. Subsequently, 0.3 g cetyltrimethyl ammonium bromide (CTAB) was placed into a mixed solution. After stirring for 30 min, 0.8 mL hydrazine hydrate was added to the mixture. Subsequently, the mixture was maintained at 150 °C for 24 h. The precursor was obtained after drying at 70 °C for 10 h. Finally, the cross-linked SnO2 nanosheets were achieved by annealing the precursor at 700 °C for 30 min at a constant heating rate (5 °C min−1).

2.2. Preparation of Cross-Linked Ag@SnO2 Nanosheets

The prepared SnO2 powder (0.5 g) was dispersed in 15 mL of deionized water with constant stirring. Subsequently, 1 mL of a 20 mM AgNO3 solution and 2 mL of a 10 mM ascorbic acid solution were added to the mixture solution with stirring for another 60 min. Finally, the product was collected after washing three times and dried at 70 °C for 5 h.

2.3. Characterization

The related information is shown in the Supporting Information (SI).

3. Results and Discussion

3.1. Characterizations

Figure 1a exhibits the X-ray diffraction (XRD) pattern of the precursor and annealed product. Before calcination, all the XRD peaks of the precursor were assigned to SnO2 (JCPDS No. 06-0395) and Sn3O4 (JCPDS No. 16-0737). After calcining the precursor at 700 °C, all the peaks at 26.1°, 33.9°, 37.9°, 51.8°, 54.7°, 61.8°, 64.7°, 65.9°, and 71.2° 2θ were indexed to the (110), (101), (200), (211), (220), (310), (112), (301), and (202) lattice planes of tetragonal rutile SnO2 (JCPDS No. 41-1445), respectively. No impurity peaks were detected. Moreover, the high crystallinity of the final product was obtained by observing the sharp and strong XRD peaks. The XRD pattern of Ag@SnO2 exhibited the peaks for SnO2 and peaks at 38.1°, 44.2°, 64.4°, and 77.5° 2θ corresponding to the (111), (200), (220), and (311) planes of Ag (JCPDS Card No. 04-0783), respectively, indicating that the product contained Ag and SnO2. Figure 1b exhibits energy-dispersive X-ray spectroscopy (EDS) analysis, confirming the existence of Sn, O, and Ag in the final product.
The morphologies of the samples were studied by scanning electron microscopy (SEM). Figure 2a,b shows the sheet-linked morphology of the precursor. The nanosheets had a smooth surface. Many cross-linked nanosheets formed a hierarchical structure by self-assembly. Some pores formed among the cross-linked nanosheets. Synthesis of cross-linked SnO2 nanosheets from an aqueous solution containing Sn2+ ions may be explained via the following mechanism:
4SnCl2·2H2O + 6OH− = Sn4(OH)6Cl2↓ + 8H2O + 6Cl−
Sn4(OH)6Cl2 + O2 = 2Sn2O3 + 2H2O + 2HCl
2Sn2O3 + O2 = 4SnO2
The reaction (1) could be easily observed when adding NaOH to the Sn2+ solution, resulting in some Sn4(OH)6Cl2 microplates. After that, the Sn4(OH)6Cl2 microplates were gradually oxidized, forming some Sn2O3 nuclei on the surface of the microplates. Upon increasing the time further, Sn2O3 nuclei formed from the oxidation of Sn4(OH)6Cl2, followed by crystal growth. In liquid medium, the growth habit of Sn2O3 crystal is mainly determined by its intrinsic structure. Therefore, the cross-linked nanosheet-like precursor was obtained. Finally, the cross-linked SnO2 nanosheets were obtained via calcination of the precursor in air. Figure 2c,d presents SEM images of the precursors after high-temperature calcination. The morphology maintains the structure of cross-linked nanosheets. The thickness of the nanosheets was ca. 10 nm, as shown in Figure S3. As displayed in Figure 2e,f, after being decorated by Ag NPs, abundant Ag NPs were attached uniformly to the surface of each nanosheet, and no obvious aggregation of Ag NPs occurred. Figure S4 presents the size of the Ag NPs with 80 nm. The loaded Ag NPs can supply many active sites for gas adsorption and accelerate the transfer of electrons. Figure 2g presents the mapping images of the Ag@SnO2 nanosheets, confirming the presence of Ag, Sn, and O. The Ag NPs were modified uniformly on the nanosheet surface.
The morphology of the composites was explored further by transmission electron microscopy (TEM). Figure 3a,b shows TEM images (low/high magnification, respectively) of the SnO2 nanosheets modified by Ag NPs (Ag@SnO2). The sample maintained a cross-linked nanosheet morphology. Abundant Ag NPs were scattered uniformly over the SnO2 nanosheet surface, with no serious aggregation of Ag NPs. As shown in Figure 3c, the lattice spacing of 0.176 nm matches the crystal plane (211) of rutile SnO2 in the TEM image with high-resolution TEM (HRTEM). From Figure 3b,c, the crystallographic orientation of the SnO2 nanosheet is the (211) plane. The nanosheet grows along the (211) direction, but the basal plane cannot be determined. Moreover, the lattice spacing (0.236 nm) corresponded to the crystal plane (111) of Ag [35]. From the SAED pattern of Ag@SnO2 (Figure 3d), five distinct diffraction rings corresponded to the (100), (101), (200), (211), and (301) crystal planes of the tetragonal SnO2, indicating that the nanosheet was polycrystalline [36]. The diffraction ring could be attributed to the crystal plane (311) of Ag [35].
Figure 4a presents the nitrogen adsorption/desorption isotherms of the Ag@SnO2 nanosheets. The specific surface area of the nanocomposites is 11.9 m2 g−1. The pore size ranged from 0.86 nm to 24.65 nm, with an average pore size of 12.86 nm. By comparison, the specific surface area of pure SnO2 nanosheets was only 8.1 m2 g−1. The pore sizes were between 0.89 and 28.12 nm, averaging 11.78 nm. The cross-linked nanosheets contributed to the formation of numerous pores. The Ag@SnO2 nanosheets had a larger specific surface area than the pure SnO2 nanosheets. The higher specific surface area was favorable for providing more available active sites for the gas molecules. The porous structure helped improve the diffusion efficiency of gas molecules by shortening the diffusion distance.
Figure 5a presents the UV-vis spectra of cross-linked Ag@SnO2 and pure cross-linked SnO2 nanosheets. The maximum absorption peak of the as-prepared samples occurred around 300 nm. The absorption band (350–280 nm) of Ag-SnO2 composite is red-shifted compared with pristine SnO2, which can be attributed to the charge transition between Ag and SnO2, leading to the enhancement of gas-sensing performance [34]. Figure 5b presents the calculated bandgap of Ag@SnO2 and the SnO2 using the Tucker model. The calculated bandgaps for SnO2 and Ag@SnO2 were 2.81 eV and 2.66 eV, respectively. The bandgap of Ag@SnO2 was smaller than that of SnO2, possibly due to Ag NPs accelerating the electron transfer rate, improving the surface electron concentration of Ag@SnO2, so that more electrons were injected into the conduction band of Ag@SnO2, inducing the Burstein–Moss effect [37,38].
The element components and valences of the Ag@SnO2 nanosheets were examined by X-ray photoelectron spectroscopy (XPS). Figure 6a exhibits the survey spectrum of the product, demonstrating the existence of Sn, O, and Ag. As shown in Figure 6b, there were two peaks at ca. 486.1 eV and 494.5 eV in the Sn 3d spectrum, which could be ascribed to Sn 3d5/2 and Sn 3d3/2 [39], respectively. This result suggests the formation of Sn4+ in SnO2. Figure 6c exhibits the Ag 3d spectrum, where two peaks at 367.1 eV and 373.1 eV are ascribed to Ag 3d5/2 and Ag 3d3/2, respectively [22,40]. From the O 1s spectrum in Figure 6d, three peaks at ca. 529.8 eV, 530.6 eV, and 531.9 eV were ascribed to lattice oxygen (OL. 53.4%), vacancy oxygen (OV. 27.8%), and chemisorbed oxygen (OC. 18.8%), respectively. Abundant chemisorbed oxygen formed in the composites, which improved the sensing performance. Figure S5 shows the C 1s spectrum of cross-linked Ag@SnO2 nanosheets, which may come from the adsorbed CTAB on the sample. Furthermore, the weight percentage of Ag among the Ag@SnO2 composites was calculated to be 9.7 wt % through the XPS results.

3.2. Sensing Performance of Ag@SnO2 Nanosheet Sensors

The working temperatures are always considered an important factor of a gas sensor for wide applications. At different working temperatures, the responses of cross-linked Ag@SnO2 and pure cross-linked SnO2 nanosheet sensors exposed to 100 ppm formaldehyde vapor were recorded, as shown in Figure 7a. In the temperature range of 80–200 °C, the responses of the composites increased as the working temperature was increased. As for reductive gases, the response was defined as Ra/Rg, where Ra is sensor resistance in dry air, and Rg is sensor resistance in dry air containing test gas. At 140 °C, the response reached the maximum value of 101.4, with a decrease at higher temperatures. As a result, 140 °C is the optimal working temperature of the Ag@SnO2 sensor. For the pure SnO2 sensor, the maximum response was 45.5 at temperatures up to 180 °C. Thus, 180 °C is considered the optimal working temperature of the SnO2 sensor, which is higher than that of the Ag@SnO2 sensor. Due to the introduction of Ag NPs in the Ag@SnO2, the strong catalytic effect enables formaldehyde molecules to be oxidized at a lower temperature. As shown in Figure 7b, formaldehyde, ethanol, isopropanol, acetone, methanol, toluene, ammonia, and benzene vapors are detected based on the Ag@SnO2 and pure SnO2 nanosheets sensors at 140 °C. The responses of the Ag@SnO2 sensor to these seven vapors were 101.4, 66.8, 60.5, 42.8, 17.2, 12.2, 7.5, and 5.1, respectively. The corresponding responses of the pure SnO2 sensor were 45.5, 42.2, 35.2, 29.3, 15.7, 9.8, 6.5, and 4.8, respectively. The results suggest that the sensing performance of the cross-linked Ag@SnO2 nanosheet sensor is superior to that of the pure cross-linked SnO2 nanosheet sensor. The high response of formaldehyde is mainly because different gases have different dissociation energies. The dissociation energy of formaldehyde (364 kJ mol−1) is lower than that of ethanol (436 kJ mol−1), methanol (439 kJ mol−1), benzene (431 kJ mol−1), acetone (393 kJ mol−1), and toluene (368 kJ mol−1). The low dissociation energy of formaldehyde is conducive to the reaction with the absorbed oxygen on the sensing material, thus showing strong response and high selectivity.
The gas sensors fabricated by the cross-linked Ag@SnO2 and pure cross-linked SnO2 nanosheets were applied to detect some VOCs at an optimal working temperature of 140 °C. Figure 8a exhibits the real-time response curve of the Ag@SnO2 sensor and the pure SnO2 sensor after exposure to formaldehyde vapor at different concentrations (1–400 ppm). The output current increased rapidly when the gas sensor was exposed to the formaldehyde vapor and maintained a stable condition. When the formaldehyde vapor broke away from the gas sensor, the output current quickly returned to the original value and maintained a stable current. Thus, the Ag@SnO2 sensor possesses a distinct gas-sensing response and recovery. In Figure 8, the Ag@SnO2 sensor exhibited an overshoot at a high concentration, which is the sensor response just after exposure to the target gas was highest, followed by a signal decay up to the end of the pulse. Particularly, the overshoot is drastic in ethanol and isopropanol detection. This was mainly caused by uneven gas diffusion concentration after injecting the target gas. The local concentration of the target gas was high at the beginning of injection, and then diffused to the surrounding area. The Ag@SnO2 sensor exhibits a fast strong response due to the catalytic effect of the Ag nanoparticles, resulting in an overshoot at high concentrations and signal decay, which cannot be obviously observed in the slow, weak response of the pure SnO2 sensor. From the response curve of the sensor (insets), the response increased gradually as the formaldehyde concentration was increased. The calculated responses at a very low (1 ppm) and high concentrations (100 ppm) were 6.4 and 101.4, respectively. Figure 8b–d shows the real-time response curves of the Ag@SnO2 sensor for ethanol, isopropanol, and acetone vapors, respectively. The inserts present the respective response curves. Exposure to a fixed concentration of vapors (100 ppm) revealed responses to ethanol, isopropanol, and acetone vapors of 66.8, 60.5, and 42.8, respectively. Furthermore, for the pure cross-linked SnO2 nanosheet sensor, the corresponding responses for formaldehyde, ethanol, isopropanol, and acetone vapors were 45.5, 42.2, 35.2, and 29.3. In addition, after exposure to 100 ppm formaldehyde, the response and recovery times of Ag@SnO2 sensor were 21 s and 23 s, respectively, as shown in Figure S6. The response and recovery times of the SnO2 nanosheet sensor were 28 s and 26 s, respectively, which are longer than those of the Ag@SnO2 sensor. Compared with the SnO2 nanoparticles [13], SnO2 nanoflowers [20], flower-like SnO2 microspheres [41], Pt-SnO2 nanospheres [14], Ag-SnO2 nanoparticles [34], hollow PdO/ZnO/SnO2 nanospheres [42], and flower-like Bi-SnO2 nanostructures [43], the cross-linked Ag@SnO2 nanosheets showed exceptional sensing performance, including a higher response, quicker response, and short recovery times, as shown in Table S1. Two additional sensors were fabricated with the cross-linked Ag@SnO2 nanosheets under the same process. Similar gas-sensing properties were obtained, which suggests the reproducibility of the sensors.
The repeatability of the Ag@SnO2 sensor was assessed by conducting real-time response tests to 100 ppm formaldehyde ten times at the optimal working temperature, as shown in Figure 9a. The output current increased sharply when the sensor was exposed to the formaldehyde vapor. The output current quickly returned to the initial state when the formaldehyde vapor was away from the sensor. Over ten cycles, the sensor maintained a similar current to the first, indicating reasonable repeatability of the Ag@SnO2 sensor for formaldehyde vapor detection. Figure S7 shows the long-term stability of the Ag@SnO2 sensor towards 100 ppm formaldehyde vapor at the working temperature of 140 °C. The response of the sensor remained relatively stable within 14 days, and the sensor performance remained unchanged throughout the long-term testing process, indicating its stability. Figure 9b shows the response curves for different formaldehyde concentrations. The linear regression coefficients for Ag@SnO2 and SnO2 were 0.9642 and 0.9884, respectively.

3.3. Gas-Sensing Mechanism

The sensor based on SnO2 abides by a gas-sensing mechanism with a typical n-type semiconductor material. Figure 10 presents a schematic illustration of the sensing mechanism of the Ag@SnO2 nanosheet sensor. The internal resistance of the sensing material will change due to large differences in resistance when the sensors are exposed to air and reducing gases [44]. When the SnO2 sensor is exposed to air, oxygen molecules will be adsorbed on the SnO2 surface. At the working temperature, oxygen molecules transform to oxygen species (O2− and O−) with strong oxidation by capturing electrons from the conduction band (CB) of SnO2. An electron-depleted layer (EDL) will form because of the loss of electrons in the CB of SnO2, which leads to a rapid increase in sensor resistance [45,46]. When the sensor is exposed to formaldehyde vapor, the oxygen species on the material surface will quickly react with formaldehyde vapor to produce COx and H2O(g). At the same time, the electrons captured by the oxygen anions are released back to the CB of the SnO2, narrowing the EDL thickness. This contributes to a rapid decrease in sensor resistance. The reaction process is shown in the following formulae [47].
O2(gas) → O2(ads),
O2(gas) + e− → O2−(ads),
O2−(ads) + e− → 2O−(ads),
CH2O(ads) + 2O−(ads) → CO2(g) + H2O(g) + 2e−.
The mechanism for the enhanced sensing performance of the Ag@SnO2 sensor can be described from the following aspects. After Ag nanoparticles are attached to the SnO2 surface, the spillover and catalytic effects will show [34]. Ag NPs will increase the chemisorbed oxygen on the SnO2 surface, which provides more reactive sites for redox reactions. Moreover, the barrier variation of the SnO2-based sensors is greatly influenced by the amount and type of oxygen adsorbed on the material surface. The Ag@SnO2 composites have more chemisorbed oxygen than the pure SnO2, which is due to the spillover effect between Ag nanoparticles and the material surface, accelerating the decomposition rate of the oxygen. The faster oxygen decomposition rate provides more oxygen species on the material surface. Hence, the reaction rate between oxygen species and formaldehyde molecules is accelerated, and the resistance transition rate inside the sensor is also increased.
Furthermore, the work function of Ag (5.1 eV) is higher than that of SnO2 (4.9 eV). When Ag NPs are modified on the surface of the SnO2, electrons flow from the surface of the SnO2 to the surface of the Ag NPs, and the Schottky junction is formed between them [48,49]. When the Ag@SnO2 sensor is exposed to air, the electrons accumulating on the surface of the Ag nanoparticles react quickly with oxygen, which promotes the ionization of absorbed oxygen. In addition, the amount of oxygen species converging on the surface of the Ag NPs will overflow to the material surface, resulting in an increasing number of oxygen species on the surface [50]. Finally, the electron depletion layer of Ag@SnO2 is thicker than that of SnO2, which further increases the resistance of sensing material in air. When the Ag@SnO2-based sensor is exposed to formaldehyde gas, the large number of oxygen species adsorbed on the sensing material quickly react with formaldehyde molecules with the help of Ag catalysis. As the reaction progresses, many free electrons return, decreasing the thickness of the electron depletion layer and increasing the current of the Ag@SnO2 sensor.

4. Conclusions

Numerous cross-linked SnO2 nanosheets decorated with Ag NPs were prepared using a hydrothermal method, followed by a heat treatment and liquid reduction process. Many Ag NPs were dispersed uniformly over the surface of the cross-linked SnO2 nanosheets with a thickness of ca. 10 nm. In contrast to the pure cross-linked SnO2 nanosheet sensor, the Ag-decorated SnO2 nanosheet sensor showed an enhanced sensing performance for VOC detection. For the 100 ppm formaldehyde vapor, the Ag@SnO2 sensors exhibited a stronger response than the pure SnO2 sensor at a low optimal working temperature. The exceptional sensing behaviors of the Ag@SnO2 sensors were attributed to the favorable cross-linked nanosheet structure, large specific surface area, and the synergistic effect of Ag NPs and cross-linked SnO2 nanosheets.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/chemosensors11020116/s1, Figure S1: Experimental setup; Figure S2: Photograph of the sensor; Figure S3: High-magnification SEM image of cross-linked SnO2 nanosheets; Figure S4: High-magnification SEM image of cross-linked Ag@SnO2 nanosheets; Figure S5: C 1s spectrum of cross-linked Ag@SnO2 nanosheets; Figure S6: Response and recovery characteristics of cross-linked Ag@SnO2 nanosheets and pure cross-linked SnO2 nanosheet sensors to 100 ppm formaldehyde vapor; Figure S7: Long-term stability of the Ag@SnO2 sensor towards 100 ppm formaldehyde vapor at the working temperature of 140 °C; Table S1: Responses of SnO2-based gas sensors to different concentrations of formaldehyde vapor.

Author Contributions

Conceptualization, H.W.; methodology, H.W., X.D. and H.R.; validation, H.R.; investigation, H.W. and X.D.; resources, Y.S. and S.W.J.; writing—original draft preparation, H.W.; writing—review and editing, Y.S., J.H. and S.W.J.; supervision, Y.S.; funding acquisition, S.W.J. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Research Foundation of Korea (NRF-2019R1A5A8080290).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Hui, C.; Sun, L.; Li, G.; Zou, X. Well-tuned surface oxygen chemistry of cation off-stoichiometric spinel oxides for highly selective and sensitive formaldehyde detection. Chem. Mater. 2018, 30, 2018–2027. [Google Scholar]
  2. Wang, D.; Wan, K.; Zhang, M.; Li, H.; Wang, P.; Wang, X.; Yang, J. Constructing hierarchical SnO2 nanofiber/nanosheets for efficient formaldehyde detection. Sens. Actuators B Chem. 2019, 283, 714–723. [Google Scholar] [CrossRef]
  3. Zhang, D.; Cao, Y.; Yang, Z.; Wu, J. Nanoheterostructure construction and DFT study of Ni-doped In2O3 nanocubes/WS2 hexagon nanosheets for formaldehyde sensing at room temperature. ACS Appl. Mater. Interfaces 2020, 12, 11979–11989. [Google Scholar] [CrossRef]
  4. Li, N.; Fan, Y.; Shi, Y.; Xiang, Q.; Wang, X.; Xu, J. A low temperature formaldehyde gas sensor based on hierarchical SnO/SnO2 nano-flowers assembled from ultrathin nanosheets: Synthesis, sensing performance and mechanism. Sens. Actuators B Chem. 2019, 294, 106–115. [Google Scholar] [CrossRef]
  5. Ji, H.; Zeng, W.; Li, Y. Gas sensing mechanisms of metal oxide semiconductors: A focus review. Nanoscale 2019, 11, 22664–22684. [Google Scholar] [CrossRef]
  6. Huang, B.; Zeng, W.; Li, Y. Synthesis of ZIF-8 coating on ZnO nanorods for enhanced gas-sensing performance. Chemosensors 2022, 10, 297. [Google Scholar] [CrossRef]
  7. Deng, Z.; Zhang, Y.; Xu, D.; Zi, B.; Zeng, J.; Lu, Q.; Xiong, K.; Zhang, J.; Zhao, J.; Liu, Q. Ultrasensitive formaldehyde sensor based on SnO2 with rich adsorbed oxygen derived from a metal organic framework. ACS Sens. 2022, 7, 2577–2588. [Google Scholar] [CrossRef]
  8. Meng, F.; Qi, T.; Zhang, J.; Zhu, H.; Yuan, Z.; Liu, C.; Qin, W.; Ding, M. MoS2-templated porous hollow MoO3 microspheres for highly selective ammonia sensing via a Lewis acid-base interaction. IEEE Trans. Ind. Electron. 2022, 69, 960–970. [Google Scholar] [CrossRef]
  9. Li, Q.; Zeng, W.; Zhou, Q.; Wang, Z. Highly sensitive ethanol sensing using NiO hollow spheres synthesized via hydrothermal method. Chemosensors 2022, 10, 341. [Google Scholar] [CrossRef]
  10. Ou, Y.; Zhu, G.; Liu, P.; Jia, Y.; Zhu, L.; Nie, J.; Zhang, S.; Zhang, W.; Gao, J.; Lu, H.; et al. Anchoring platinum clusters onto oxygen vacancy-modified In2O3 for ultraefficient, low-temperature, highly sensitive, and stable detection of formaldehyde. ACS Sens. 2022, 7, 1201–1212. [Google Scholar] [CrossRef] [PubMed]
  11. Liu, H.; Liu, B.; Li, P.; Kang, W.; Zhang, Y. High sensitivity and anti-humidity gas sensor for nitrogen dioxide based on Ce/SnO2 nanomaterials. Sensor Actuat. A-Phys. 2022, 344, 113717. [Google Scholar]
  12. Xu, R.; Zhang, L.; Li, M.; Yin, Y.; Yin, J.; Zhu, M.; Chen, J.; Wang, Y.; Bie, L. Ultrathin SnO2 nanosheets with dominant high-energy {001} facets for low temperature formaldehyde gas sensor. Sens. Actuators B Chem. 2019, 289, 186–194. [Google Scholar] [CrossRef]
  13. Yang, T.; Zhu, M.; Gu, K.; Zhai, C.; Zhao, Q.; Yang, X.; Zhang, M. Facile synthesis of SnO2 nanoparticles for improved formaldehyde detection. New J. Chem. 2018, 16, 3185–14014. [Google Scholar]
  14. Tang, Y.; Han, Z.; Qi, Y.; Yang, Z.; Han, H.; Jiang, Y.; Zhang, X.; Wu, L.; Wang, Z.; Liu, J.; et al. Enhanced ppb-level formaldehyde sensing performance over Pt deposited SnO2 nanospheres. J. Alloy. Compd. 2022, 899, 163230. [Google Scholar] [CrossRef]
  15. Tonezzer, M. Selective gas sensor based on one single SnO2 nanowire. Sens. Actuators B Chem. 2019, 288, 53–59. [Google Scholar] [CrossRef]
  16. Sharma, A.P.; Dhakal, P.; Pradhan, D.K.; Behera, M.K.; Xiao, B.; Bahoura, M. Fabrication and characterization of SnO2 nanorods for room temperature gas sensors. Aip Adv. 2018, 8, 095219. [Google Scholar] [CrossRef]
  17. Su, P.; Li, W.; Zhang, J.; Xie, X. Chemiresistive gas sensor based on electrospun hollow SnO2 nanotubes for detecting NO at the ppb level. Vacuum 2022, 199, 110961. [Google Scholar] [CrossRef]
  18. Xiang, C.; Chen, T.; Zhao, Y.; Sun, J.; Jiang, K.; Li, Y.; Zhu, X.; Zhang, X.; Zhang, N.; Guo, R. Facile hydrothermal synthesis of SnO2 nanoflowers for low-Concentration formaldehyde detection. Nanomaterials 2022, 12, 2133. [Google Scholar] [CrossRef]
  19. Li, Y.; Chen, N.; Deng, D.; Xing, X.; Xiao, X.; Wang, Y. Formaldehyde detection: SnO2 microspheres for formaldehyde gas sensor with high sensitivity, fast response/recovery and good selectivity. Sens. Actuators B Chem. 2017, 238, 264–273. [Google Scholar] [CrossRef]
  20. Hu, J.; Wang, H.; Chen, M.; Zhang, Y.; Zhao, X.; Zhang, D.; Lu, Q.; Zhang, J.; Liu, Q. Constructing hierarchical SnO2 nanoflowers for enhanced formaldehyde sensing performances. Mater. Lett. 2020, 263, 126843. [Google Scholar] [CrossRef]
  21. Zhang, J.; Zhang, B.; Yao, S.; Li, H.; Chen, C.; Bala, H.; Zhang, Z. Improved triethylamine sensing properties of fish-scale-like porous SnO2 nanosheets by decorating with Ag nanoparticles. J. Materiomics 2022, 8, 518–525. [Google Scholar] [CrossRef]
  22. Yu, H.; Yang, T.; Zhao, R.; Xiao, B.; Li, Z.; Zhang, M. Fast formaldehyde gas sensing response properties of ultrathin SnO2 nanosheets. RSC Adv. 2015, 126, 104574–104581. [Google Scholar] [CrossRef]
  23. Han, J.; Zheng, Y.; Fan, M. Qualitative and quantitative recognition method of drug-producing chemicals based on SnO2 gas Sensor with dynamic measurement and PCA weak separation. Sens. Actuators B Chem. 2021, 348, 130698. [Google Scholar]
  24. Zhao, R.; Zhang, X.; Peng, S.; Hong, P.; Zou, T.; Wang, Z.; Xing, X.; Yang, Y.; Wang, Y. Shaddock peels as bio-templates synthesis of Cd-doped SnO2 nanofibers: A high performance formaldehyde sensing material. J. Alloys Compd. 2020, 813, 152170. [Google Scholar] [CrossRef]
  25. Li, X.; Liu, W.; Huang, B.; Liu, H.; Li, X. Layered SnSe2 microflakes and SnSe2/SnO2 heterojunctions for low-temperature chemiresistive-type gas sensing. J. Mater. Chem. C 2020, 8, 15804–15815. [Google Scholar] [CrossRef]
  26. Meng, D.; Liu, D.; Wang, G.; Shen, Y.; San, X.; Li, M.; Meng, F. Low-temperature formaldehyde gas sensors based on NiO-SnO2 heterojunction microflowers assembled by thin porous nanosheets. Sens. Actuators B Chem. 2018, 273, 418–428. [Google Scholar] [CrossRef]
  27. Kou, X.; Meng, F.; Chen, K.; Wang, T.; Sun, P.; Liu, F.; Yan, X.; Sun, Y.; Liu, F.; Shimanoe, K.; et al. High-performance acetone gas sensor based on Ru-doped SnO2 nanofibers. Sens. Actuators B Chem. 2020, 320, 128292. [Google Scholar] [CrossRef]
  28. Wang, F.; Hu, K.; Liu, H.; Zhao, Q.; Wang, K.; Zhang, Y. Low temperature and fast response hydrogen gas sensor with Pd coated SnO2 nanofiber rods. Int. J. Hydrogen Energ. 2020, 45, 7234–7242. [Google Scholar] [CrossRef]
  29. Meng, F.; Zheng, H.; Chang, Y.; Zhao, Y.; Li, M.; Wang, C.; Sun, Y.; Liu, J. One-step synthesis of Au/SnO2/RGO nanocomposites and their VOC sensing properties. IEEE Trans. Nanotechnol. 2018, 172, 212–219. [Google Scholar] [CrossRef]
  30. Duy, N.V.; Thai, N.X.; Ngoc, T.M.; Le, D.T.T.; Hung, C.M.; Nguyen, H.; Tonezzer, M.; Hieu, N.V.; Hoa, N.D. Design and fabrication of effective gradient temperature sensor array based on bilayer SnO2/Pt for gas classification. Sens. Actuators B Chem. 2022, 351, 130979. [Google Scholar]
  31. Yuan, X.; Sun, L.; Wang, J.; Liao, D.; Sun, J. Metal organic frameworks derived SnO2 microsphere doped Ag for monitoring low concentration ethanol. Mater. Sci. Semicond. Proc. 2021, 136, 106110. [Google Scholar] [CrossRef]
  32. Li, G.; Cheng, Z.; Xiang, Q.; Yan, L.; Wang, X.; Xu, J. Bimetal PdAu decorated SnO2 nanosheets based gas sensor with temperature-dependent dual selectivity for detecting formaldehyde and acetone. Sens. Actuators B Chem. 2019, 283, 590–601. [Google Scholar] [CrossRef]
  33. Peng, S.; Hong, P.; Li, Y.; Xing, X.; Yang, Y.; Wang, Z.; Zou, T.; Wang, Y. Pt decorated SnO2 nanoparticles for high response CO gas sensor under the low operating temperature. J. Mater. Sci. Mater. Electron. 2019, 30, 3921–3932. [Google Scholar] [CrossRef]
  34. Liu, D.; Pan, J.; Tang, J.; Liu, W.; Bai, S.; Luo, R. Ag decorated SnO2 nanoparticles to enhance formaldehyde sensing properties. J. Phys. Chem. Solids 2019, 124, 36–43. [Google Scholar] [CrossRef]
  35. Ovsianytskyi, O.; Nam, Y.; Tsymbalenko, O.; Lan, P.; Moon, M.; Lee, K. Highly sensitive chemiresistive H2S gas sensor based on graphene decorated with Ag nanoparticles and charged impurities. Sens. Actuators B Chem. 2018, 257, 278–285. [Google Scholar] [CrossRef]
  36. Ma, X.; Gao, R.; Zhang, T.; Sun, X.; Li, T.; Gao, S.; Zhang, X.; Xu, Y.; Cheng, X.; Huo, L. Mesoporous SnO2 nanospheres sensor for fast detection of HCHO and its application in safety detection of aquatic products. Sens. Actuators B Chem. 2023, 374, 132844. [Google Scholar] [CrossRef]
  37. Luque, P.A.; Garrafa-Galvez, H.E.; Nava, O.; Olivas, A.; Martínez-Rosas, M.E.; Vilchis-Nestor, A.R.; Villegas-Fuentes, A.; Chinchillas-Chinchillas, M.J. Efficient sunlight and UV photocatalytic degradation of methyl orange, methylene blue and rhodamine B, using Citrus×paradisi synthesized SnO2 semiconductor nanoparticles. Ceram. Int. 2021, 47, 23861–23874. [Google Scholar] [CrossRef]
  38. Suriya, P.; Prabhu, M.; Kumar, E.; Jagannathan, K. Effect of Ag doping on structural, optical, complex impedance and photovoltaic properties of SnO2 nanoparticles prepared by co-precipitation method for dye sensitized solar cell application. Optik 2022, 260, 168971. [Google Scholar] [CrossRef]
  39. Bhangare, B.; Ramgir, N.S.; Jagtap, S.; Debnathb, A.K.; Muthe, K.P.; Terashima, C.; Aswal, D.K.; Gosavi, S.W.; Fujishima, A. XPS and Kelvin probe studies of SnO2/RGO nanohybrids based NO2 sensors. Appl. Surf. Sci. 2019, 487, 918–929. [Google Scholar] [CrossRef]
  40. Shi, C.; Wang, X.; Wang, Y.; Wang, T.; Li, H.; Yi, G.; Sun, G.; Zhang, Z. Synthesis, characterization, and gas-sensing properties of macroporous Ag/SnO2 composite by a template method. Mater. Sci. Semicond. Process. 2022, 138, 106256. [Google Scholar] [CrossRef]
  41. Re, P.; Qi, L.; You, K.; Shi, Q. Hydrothermal synthesis of hierarchical SnO2 nanostructures for improved formaldehyde gas sensing. Nanomaterials 2022, 12, 228. [Google Scholar]
  42. Jiao, S.; Xue, W.; Zhang, C.; Li, F.; Meng, B.; Zhan, Z. Improving the formaldehyde gas sensing performance of the ZnO/SnO2 nanoparticles by PdO decoration. J. Mater. Sci.-Mater. Electron. 2020, 31, 684–692. [Google Scholar]
  43. Zhu, K.M.; Ma, S.Y. Preparations of Bi-doped SnO2 hierarchical flower-shaped nanostructures with highly sensitive HCHO sensing properties. Mater. Lett. 2019, 607, 357–366. [Google Scholar] [CrossRef]
  44. Kim, S.; Singh, G.; Oh, M.; Lee, K. An analysis of a highly sensitive and selective hydrogen gas sensor based on a 3D Cu-doped SnO2 sensing material by efficient electronic sensor interface. ACS Sens. 2021, 6, 4145–4155. [Google Scholar] [CrossRef] [PubMed]
  45. Manikandan, V.; Petrila, L.; Vigneselvan, S.; Mane, R.S.; Vasile, B.; Dharmavarapu, R.; Lundgaard, S.; Juodkazis, S.; Chandrasekaran, J. A reliable chemiresistive sensor of nickel-doped tin oxide (Ni-SnO2) for sensing carbon dioxide gas and humidity. RSC Adv. 2020, 10, 3796. [Google Scholar] [CrossRef]
  46. Chen, Y.; Cao, Y. Ultrasensitive and low detection limit of acetone gas sensor based on ZnO/SnO2 thick films. RSC Adv. 2020, 10, 35958. [Google Scholar] [CrossRef]
  47. Zhou, S.; Wang, H.; Hu, J.; Lv, T.; Rong, Q.; Zhang, Y.; Zi, B.; Chen, M.; Zhang, D.; Wei, J.; et al. Formaldehyde gas sensor with extremely high response employing cobalt-doped SnO2 ultrafine nanoparticles. Nanoscale Adv. 2022, 4, 824. [Google Scholar] [CrossRef]
  48. Xu, H.; Li, J.; Fu, Y.; Li, P.; Luo, W.; Tian, Y. Ag/Ag2S Nanoparticle-induced sensitization of recovered sulfurdoped SnO2 nanoparticles for SO2 detection. ACS Appl. Nano Mater. 2020, 3, 8075–8087. [Google Scholar] [CrossRef]
  49. Li, Z.; Li, H.; Wu, Z.; Wang, M.; Luo, J.; Torun, H.; Hu, P.; Yang, C.; Grundmann, M.; Liu, X.; et al. Advances in designs and mechanisms of semiconducting metal oxide nanostructures for high-precision gas sensors operated at room temperature. Mater. Horiz. 2019, 6, 470–506. [Google Scholar] [CrossRef]
  50. Tomer, V.K.; Duhan, S. Ordered mesoporous Ag-doped TiO2/SnO2 nanocomposite based highly sensitive and selective VOC sensors. J. Mater. Chem. A 2016, 4, 1033. [Google Scholar] [CrossRef]
Chemosensors 11 00116 g001 550
Figure 1. (a) XRD pattern; (b) EDS analysis of the cross-linked Ag@SnO2 nanosheets.
Figure 1. (a) XRD pattern; (b) EDS analysis of the cross-linked Ag@SnO2 nanosheets.
Chemosensors 11 00116 g001
Chemosensors 11 00116 g002 550
Figure 2. SEM images of (a) the precursor; (b) the precursor; (c) cross-linked SnO2 nanosheets; (d) cross-linked SnO2 nanosheets; (e) cross-linked Ag@SnO2 nanosheets; (f) cross-linked Ag@SnO2 nanosheets. (g) Elemental mapping images of Ag@SnO2 nanosheets.
Figure 2. SEM images of (a) the precursor; (b) the precursor; (c) cross-linked SnO2 nanosheets; (d) cross-linked SnO2 nanosheets; (e) cross-linked Ag@SnO2 nanosheets; (f) cross-linked Ag@SnO2 nanosheets. (g) Elemental mapping images of Ag@SnO2 nanosheets.
Chemosensors 11 00116 g002
Chemosensors 11 00116 g003 550
Figure 3. (a) TEM image; (b) HRTEM image; (c) lattice-resolved HRTEM image; (d) selected area electron diffraction (SAED) pattern of Ag@SnO2 nanosheets.
Figure 3. (a) TEM image; (b) HRTEM image; (c) lattice-resolved HRTEM image; (d) selected area electron diffraction (SAED) pattern of Ag@SnO2 nanosheets.
Chemosensors 11 00116 g003
Chemosensors 11 00116 g004 550
Figure 4. Nitrogen adsorption/desorption isotherms of (a) Ag@SnO2; (b) pure SnO2 nanosheets. The inset image is the pore size distribution.
Figure 4. Nitrogen adsorption/desorption isotherms of (a) Ag@SnO2; (b) pure SnO2 nanosheets. The inset image is the pore size distribution.
Chemosensors 11 00116 g004
Chemosensors 11 00116 g005 550
Figure 5. (a) UV-vis absorption spectra of pure cross-linked SnO2 and cross-linked Ag@SnO2 nanosheets. (b) Bandgap determinations using (αhv)2 vs. hv of cross-linked SnO2 nanosheets and Ag@SnO2 nanosheets.
Figure 5. (a) UV-vis absorption spectra of pure cross-linked SnO2 and cross-linked Ag@SnO2 nanosheets. (b) Bandgap determinations using (αhv)2 vs. hv of cross-linked SnO2 nanosheets and Ag@SnO2 nanosheets.
Chemosensors 11 00116 g005
Chemosensors 11 00116 g006 550
Figure 6. XPS spectra of cross-linked Ag@SnO2 nanosheets of (a) survey spectrum; (b) Sn 3d; (c) Ag 3d; (d) O 1s spectra.
Figure 6. XPS spectra of cross-linked Ag@SnO2 nanosheets of (a) survey spectrum; (b) Sn 3d; (c) Ag 3d; (d) O 1s spectra.
Chemosensors 11 00116 g006
Chemosensors 11 00116 g007 550
Figure 7. (a) Sensor responses of Ag@SnO2 sensor and SnO2 sensor to 100 ppm formaldehyde at various working temperatures; (b) response curves of two sensors to eight kinds of vapors (100 ppm) at 140 °C.
Figure 7. (a) Sensor responses of Ag@SnO2 sensor and SnO2 sensor to 100 ppm formaldehyde at various working temperatures; (b) response curves of two sensors to eight kinds of vapors (100 ppm) at 140 °C.
Chemosensors 11 00116 g007
Chemosensors 11 00116 g008 550
Figure 8. Real-time response curves of sensor devices upon exposure to different concentrations of (a) formaldehyde; (b) ethanol; (c) isopropanol; (d) acetone at 140 °C. The insets show the corresponding sensor response curves.
Figure 8. Real-time response curves of sensor devices upon exposure to different concentrations of (a) formaldehyde; (b) ethanol; (c) isopropanol; (d) acetone at 140 °C. The insets show the corresponding sensor response curves.
Chemosensors 11 00116 g008
Chemosensors 11 00116 g009 550
Figure 9. (a) Current change curves of Ag@SnO2 sensor towards 100 ppm formaldehyde vapor for 10 tests at 140 °C; (b) the linear fitting curve of cross-linked Ag@SnO2 and SnO2 nanosheet sensors towards formaldehyde in the concentration range of 1–400 ppm.
Figure 9. (a) Current change curves of Ag@SnO2 sensor towards 100 ppm formaldehyde vapor for 10 tests at 140 °C; (b) the linear fitting curve of cross-linked Ag@SnO2 and SnO2 nanosheet sensors towards formaldehyde in the concentration range of 1–400 ppm.
Chemosensors 11 00116 g009
Chemosensors 11 00116 g010 550
Figure 10. Schematic diagram of the sensing mechanism of cross-linked Ag@SnO2 nanosheet sensor.
Figure 10. Schematic diagram of the sensing mechanism of cross-linked Ag@SnO2 nanosheet sensor.
Chemosensors 11 00116 g010
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

Weng, H.; Dong, X.; Sun, Y.; Ren, H.; Huang, J.; Joo, S.W. Cross-Linked SnO2 Nanosheets Modified by Ag Nanoparticles for Formaldehyde Vapor Detection. Chemosensors 2023, 11, 116. https://doi.org/10.3390/chemosensors11020116

AMA Style

Weng H, Dong X, Sun Y, Ren H, Huang J, Joo SW. Cross-Linked SnO2 Nanosheets Modified by Ag Nanoparticles for Formaldehyde Vapor Detection. Chemosensors. 2023; 11(2):116. https://doi.org/10.3390/chemosensors11020116

Chicago/Turabian Style

Weng, Huaipeng, Xumeng Dong, Yufeng Sun, Haibo Ren, Jiarui Huang, and Sang Woo Joo. 2023. "Cross-Linked SnO2 Nanosheets Modified by Ag Nanoparticles for Formaldehyde Vapor Detection" Chemosensors 11, no. 2: 116. https://doi.org/10.3390/chemosensors11020116

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