Enhanced ammonia sensing at room temperature with reduced graphene oxide/tin oxide hybrid films

Abstract

Sensitive and selective detection of ammonia at room temperature is required for proper environmental monitoring and also to avoid any health hazards in the industrial areas. The excellent electrical properties of reduced graphene oxide (RGO) and sensing capabilities of SnO₂ were combined to achieve enhanced ammonia sensitivity. RGO–SnO₂ films were synthesized hydrothermally as well as prepared by mixing different amounts of hydrothermally synthesized SnO₂ nanoparticles with graphene oxide (GO). It was observed that the response of the hybrid sensing layer was considerably better than intrinsic RGO or SnO₂. However, the best performance was observed in the 10 : 8 (RGO–SnO₂) sample. The sample was exposed to nine different concentrations of ammonia in the presence of 20% RH at room temperature. The response of the sensor varied from 1.4 times (25 ppm) to 22 times (2800 ppm) with quick recovery after purging with air. The composite formation was verified by characterizing the samples using field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS) and high resolution transmission electron microscopy (HRTEM). The results and their significance have been discussed in detail.

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1. Introduction

Ammonia is a toxic pollutant, which occurs naturally in the environment through human wastes and industries.¹ It has a sharp and pungent odor but we can smell it only if the concentration of ammonia is more than 50 ppm (parts per million).² Therefore, it is quite natural to get exposed to lower levels of ammonia in our day-to-day lives without even knowing. Ammonia can cause severe effects on human body like irritation of the eyes, throat, skin and respiratory system when exposed to concentrations greater than 35 ppm for even just 15 minutes.³ Therefore, it is necessary to develop highly sensitive and selective ammonia sensors that can detect low concentrations of ammonia.

Tin dioxide (SnO₂) is an n-type semiconducting material. It is highly sensitive towards different chemical analytes.^4–6 Different morphologies of SnO₂ nanostructures and its composites have already been employed as ammonia sensors in the past few years.⁷ However, similar to other metal oxides, SnO₂ also suffers from two major drawbacks. The first is high temperature operability, i.e. it can sense gases only at elevated temperature (200–500 °C), which increases the power consumption as well as limits its feasibility as a sensor in conditions where high temperature operations are not allowed.⁸ The second drawback is poor selectivity, i.e. tin oxide shows a similar response towards different gases and thereby demonstrates no specificity towards any particular gas.⁹

In these regards, graphene and reduced graphene oxide (RGO) gain advantage. Graphene is a 2D carbon nanomaterial with a high aspect ratio and excellent electronic properties, which facilitate sensing gases at room temperature.¹⁰ However, graphene in its pure form is not ideal for gas detection, because it is devoid of any functional groups and defect sites, which play a vital role in gas sensing. In addition, graphene is usually synthesized using sophisticated and expensive techniques such as chemical vapor deposition (CVD) and epitaxial methods,¹¹ and one of the cheapest techniques to synthesize graphene is through mechanical exfoliation but that too suffers with scalability issues.¹² RGO on the other hand can be synthesized chemically, which is usually a low cost technique, and also RGO contains functional groups and defect sites, which act as active regions for the gas molecules to get attached.¹³ However, the response of RGO towards gases is not as high as that of metal oxides.

Inspired by the outstanding properties of RGO and SnO₂ (RGO can sense gases at room temperature and SnO₂ gives a large response), here we have developed RGO–SnO₂ hybrid samples for ammonia sensing. RGO, intrinsically a p-type material, was synthesized by reducing graphene oxide (GO) thermally. The n-type SnO₂ nanoparticles were synthesized using a hydrothermal technique. The RGO–SnO₂ hybrid material was prepared in two ways, which have been discussed in the Experimental section. The sensor featured a very large response (larger than RGO or SnO₂ response towards ammonia), good selectivity, fast response and recovery, even at room temperature. The enhanced sensing behavior of the hybrid film is due to the combined nature of the p-type and n-type sensing materials. The response of the sensor was measured in the presence of ammonia (25–2800 ppm) and different VOCs. The sensing results and their mechanism have been explained in detail.

2. Experimental section

2.1. Material synthesis

2.1.1 Chemicals. Fine graphite powder was purchased from Loba Chemie Pvt. Ltd., India, and stannous chloride hydrate [SnCl₂·2H₂O] was purchased from Sisco Research Laboratories (SRL), India. All the other chemicals were purchased from Merck, India. All the above mentioned reagents were of analytical grade and used without further purification.

2.1.2 GO/RGO synthesis. GO was synthesized using the modified Hummers method as has been reported earlier.¹⁴ Briefly, graphite powder was exfoliated using NaNO₃, H₂SO₄ and KMnO₄. After synthesizing GO, the solution was centrifuged at a speed of 5000 rpm to eliminate the visible particles. Then, the purified GO was reduced thermally by heating at 160 °C in air for 30 minutes to obtain RGO.

2.1.3 SnO₂ nanoparticle synthesis. SnO₂ nanoparticles were synthesized by the following procedure. First, 0.54 g stannous chloride hydrate (0.06 M) was dissolved in 40 mL distilled water and the solution was stirred for 5 minutes. Then, 1 mL of concentrated HCl was added to the abovementioned solution and stirred vigorously for 30 minutes prior to transferring it to a 50 mL Teflon-lined stainless steel autoclave. The autoclave was heated at 200 °C for 12 h and cooled naturally to room temperature. The powder product was collected by centrifuging. Finally, the as-synthesized product was washed several times by water and ethanol and then calcined at 400 °C for 2 h.

2.1.4 RGO–SnO₂ hybrid film preparation. The hybrid sensing layer was prepared in two ways. (i) First, 40 mL of aqueous GO dispersed solution (2 mg mL⁻¹) was prepared, to which 0.54 g stannous chloride hydrate was added and then 1 mL HCl was added and stirred before transferring to a stainless steel autoclave. Further steps were same as that of SnO₂ nanoparticle synthesis. The GO got reduced during the synthesis only when the solution was heated at 200 °C. (ii) The second method of hybrid film preparation was performed by mixing GO and SnO₂ in different weight percentages. In this method, at first 100 mg GO was dispersed in 4 mL ethanol. The SnO₂ nanoparticles were then mixed with GO dispersed in ethanol by maintaining different weight ratios. Five such samples were prepared in which the wt% of GO and SnO₂ was varied as 10 : 3, 10 : 4, 10 : 5, 10 : 8 and 1 : 1. The GO–SnO₂ mixed samples were ultrasonicated for 30 minutes, so as to obtain uniform dispersion and also for proper mixing of GO and SnO₂ nanoparticles. The samples were then drop casted on Pt based interdigitated electrodes (Synkera Technologies, length and width of the electrode fingers being 2.54 mm and 100 μm, respectively, and having 100 μm space between two adjacent fingers), dried in air and then heated at 160 °C for 30 minutes to reduce the GO present in the hybrid sensing layer.

2.2. Material characterizations

The RGO–SnO₂ hybrid sensing layers were characterized using a Zeiss Auriga Compact field emission scanning electron microscope (FESEM) to observe their morphologies as well as their composition. FEI TECNAI G2 high resolution transmission electron microscope (HRTEM) was employed to observe the detailed microstructures of the hybrid sensing layers. The X-ray diffraction (XRD) pattern was observed using a Panalytical X'Pert Pro diffractometer with a conventional X-ray tube (Cu Kα radiation). Moreover, in order to ensure proper reduction of GO to RGO, X-ray photoelectron spectroscopy (XPS) was performed using a PHI 5000 Versa Probe II.

2.3. Gas sensing

The gas sensing set-up was assembled in-house. It consists of three mass flow controllers (MFCs) to control the flow rates of the gases, each MFC can allow a maximum of 100 standard cubic centimeters per minute (sccm) of gas to flow through it, an airtight stainless steel chamber to probe the samples, and an Agilent 34972A LXI data acquisition (DAQ) card (which can record the resistance of the samples after every 5 s) to interface the complete system with a computer.

3. Results and discussion

3.1. Material characterizations

The SEM images of RGO, SnO₂ nanoparticles and the hybrid films are shown in Fig. 1.

Fig. 1 FESEM images of (a) thermally reduced GO, (b and c) the RGO–SnO₂ hybrid film prepared by mixing GO and SnO₂ and (d) SnO₂ nanoparticles.

The RGO film used to carry out ammonia sensing tests was multilayered as there are visible wrinkles in the SEM images of the RGO film, as shown in Fig. 1(a). Fig. 1(b) shows the FESEM image of the RGO–SnO₂ (10 : 3) hybrid film. The SnO₂ nanoparticles attached with RGO flakes can be seen in the image. In addition, it was observed that SnO₂ nanoparticles got agglomerated over the RGO flakes. Owing to their high surface energy, the SnO₂ nanoparticles probably got agglomerated while ultrasonicating the GO and metal oxide mixture in the ethanol medium.¹ The amount of SnO₂ particles was observed to increase when the proportion of SnO₂ was increased (ratio of RGO to SnO₂ was 10 : 8), as shown in Fig. 1(c). The FESEM image of 10 : 4, 10 : 5 and 1 : 1 RGO-SnO₂ samples is shown in Fig. S1 of the ESI.† The FESEM of SnO₂ nanoparticles is shown in Fig. 1(d).

In order to ensure the composition of the hybrid samples synthesized both ways, energy dispersive X-ray spectroscopy (EDS) was carried out for all the samples. The EDS results of the samples are shown in Fig. 2.

Fig. 2 EDS results of (a) 10 : 4 (RGO : SnO₂) hybrid samples and (b) hydrothermally synthesized RGO–SnO₂ hybrid samples.

The compositional analysis revealed the proportion of C, Sn and O present in the hybrid samples. Fig. 2(a) shows the composition of RGO-SnO₂ hybrid samples prepared by mixing already synthesized GO and SnO₂, followed by thermal reduction. The EDS result of the hydrothermally synthesized hybrid samples is shown in Fig. 2(b).

TEM was performed to observe the crystalline nature and microstructural properties of the hybrid sensing materials prepared hydrothermally as well as by mixing GO and SnO₂ in different proportions. The TEM and HRTEM images of the samples are shown in Fig. 3.

Fig. 3 TEM images of (a) thermally reduced GO, (b) the 10 : 3 (RGO : SnO₂) hybrid sample (inset shows the SAED pattern of RGO), (c) the 10 : 8 (RGO : SnO₂) hybrid sample (inset shows the SAED pattern of RGO), (d) the hydrothermally synthesized RGO–SnO₂ sample (inset shows the folded and multilayered RGO), (e) SnO₂ nanoparticles and (f) HRTEM image of 10 : 8 (RGO : SnO₂) (inset shows the SAED pattern of SnO₂ nanoparticles).

The TEM image (Fig. 3(a)) of thermally reduced GO appears to be transparent and few layered. Fig. 3(b) shows the imprints of uniformly distributed SnO₂ nanoparticles (diameter = ∼10 nm) over RGO flakes in the 10 : 3 (RGO : SnO₂) hybrid sample. The inset shows the hexagonal rings of the RGO. Fig. 3(c) demonstrates the presence of an increased amount of SnO₂ nanoparticles in the 10 : 8 (RGO : SnO₂) hybrid sample. The agglomeration of nanoparticles is clearly visible in the TEM image and also the same was observed in the FESEM image. The inset of Fig. 3(c) also shows the hexagonal ring of the C-atoms. Fig. 3(d) shows uniformly distributed SnO₂ nanoparticles over multilayered RGO flakes in the hydrothermally synthesized RGO–SnO₂ hybrid sample. RGO in the hydrothermal sample attained a different folded structure due to high temperature treatment. One such hollow tubular RGO structure found in the hydrothermally synthesized sample is shown in the inset of Fig. 3(d). The particle size of the SnO₂ nanoparticles was around 10 nm as can be seen in Fig. 3(e). Fig. 3(f) shows the HRTEM image of the 10 : 8 (RGO : SnO₂) sample. It shows the presence of (101) and (110) SnO₂ planes in the hybrid sample, which are perpendicular to each other. The result agrees well with the XRD data of SnO₂ nanoparticles, as shown in Fig. 4. Moreover, the SAED pattern of the SnO₂ nanoparticles was found to be polycrystalline, as is shown in the inset of Fig. 3(f).

Fig. 4 XRD results of thermally reduced GO, SnO₂, GO–SnO₂, and RGO–SnO₂.

A broad peak at around 15° was observed in the 30 minutes thermally reduced GO sample, as is shown in Fig. 4. For SnO₂ nanoparticles, sharp peaks were observed, which signify its highly crystalline nature. Our observed SnO₂ XRD result matched well with the tetragonal structure of SnO₂ with lattice constants of a = 4.7552, b = 4.7552, and c = 3.1992 (JCPDS no. 01-077-0452). For the hybrid samples, the XRD of 10 : 8 (RGO : SnO₂) before and after reducing it thermally were carried out. Although no significant peak shifting corresponding to the reduction of GO was observed, peaks of both RGO and SnO₂ nanoparticles are clearly visible in GO–SnO₂ and the RGO–SnO₂ samples.

The elemental and compositional information of the hybrid samples were further investigated using XPS. The XPS results of the RGO–SnO₂ samples are shown in Fig. 5.

Fig. 5 XPS results of (a) comparative survey scan of RGO and RGO–SnO₂, (b) Sn 3d spectra in the RGO–SnO₂ hybrid sample, (c) C 1s peak of GO and (d) C 1s peak of the RGO–SnO₂ hybrid sample.

The surface spectrum of the RGO–SnO₂ sample (Fig. 5(a)) clearly shows the presence of carbon, oxygen and tin. No other peaks corresponding to the precursors of the samples (GO or SnO₂) were observed, hence signifying that the samples were highly pure. Two Sn 3d peaks were observed at 486.4 and 494.8 eV, which correspond to Sn 3d_5/2 and Sn 3d_3/2, respectively, as shown in Fig. 5(b).¹⁵ These peaks are attributed to the +4 oxidation state of Sn in RGO–SnO₂ hybrid samples. The C 1s peak of GO can be de-convoluted into three peaks, 284.5, 286.6 and 288.3 eV, corresponding to C–C, C–O and C=O.¹⁶ The peak intensities corresponding to C–O and C=O got reduced significantly after thermally reducing the RGO–SnO₂ sample, as is evident from Fig. 5(d), thereby signifying that the GO got properly reduced. One interesting observation that was made during the XPS characterization was that the intensities of the peaks corresponding to C–O and C=O in GO–SnO₂ were significantly reduced and were almost similar to that of the 30 minutes reduced GO–SnO₂ sample. This signifies that the SnO₂ nanoparticles assisted in the reduction of GO. Similar results were observed in XRD pattern, where no considerable peak shift was observed, as shown in Fig. 4. However, no measurable resistance could be measured at room temperature in the GO–SnO₂ samples unless they had been thermally reduced for 30 minutes.

3.1.1 Gas sensing results. The sensors were developed on ceramic substrates. The substrates contain platinum interdigitated electrodes (IDEs) for measuring the resistance of the sensing layer. The hybrid samples were drop coated on these IDEs. The conductivities of the RGO, RGO–SnO₂ and SnO₂ samples are reported in the ESI.† The optical image of the sensor device is shown in Fig. 6(a). Fig. 6(b) shows the schematic of the RGO–SnO₂ based sensor device.

Fig. 6 (a) Image of the Pt-based interdigitated electrode before and after coating with the RGO–SnO₂ sensing layer and (b) schematic (not to scale) of RGO–SnO₂ coated Pt-based interdigitated electrodes on a ceramic substrate.

The samples coated on IDEs were probed inside the stainless steel chamber and purged with dry air for 20 minutes to stabilize the baseline resistances of the samples at room temperature. After that, the target gas, ammonia, in the presence of 20% relative humidity (RH), was allowed into the chamber for 5 minutes followed by dry air purging. The response of all the samples was calculated as follows:

The response of thermally reduced GO towards ammonia at room temperature is shown in Fig. 7(a). The resistance of the RGO sensor increased when exposed to ammonia as RGO is inherently a p-type material and ammonia is a donor molecule, as already been discussed in our previously reported work.¹⁷ SnO₂ is a semiconducting material, which senses analytes at higher temperature. Thus, the SnO₂ nanoparticle based sensor was exposed to 1200 ppm ammonia at different temperatures (150–300 °C) to find out the temperature of its optimum response. The temperature profile and the response of the SnO₂ based sensor towards different concentrations of ammonia at its optimum temperature (200 °C) are shown in Fig. 7(b) and (c), respectively.

Fig. 7 Response of (a) thermally reduced GO towards 1200 and 2000 ppm ammonia at room temperature, (b) the SnO₂ sensor towards 1200 ppm ammonia at four different temperatures (150–300 °C) and (c) the SnO₂ sensor towards four different concentrations of ammonia at 200 °C.

The basic mechanism of sensing gases using a metal oxide (e.g. tin oxide) has already been discussed in the literature.¹⁸ Briefly, when air comes into contact with metal oxide, oxygen molecules trap electrons from the metal oxide surface, thereby increasing the sensing layer resistance. These adsorbed oxygen species act as reaction sites when gas molecules (e.g. NH₃) come in contact with them, thus releasing electrons back to the conduction band of tin oxide.

4NH₃ + 3O_2(ads)⁻ → 2N₂ + 6H₂O + 3e⁻

Hence, the resistance of the SnO₂ nanoparticle based sensor decreased when exposed to ammonia as can be seen in Fig. 7(c), thereby depicting n-type behavior of the SnO₂ nanoparticle based sensor. The response was found to be 2.4 times in the presence of 1200 ppm NH₃, which is considerably larger than what we obtained from RGO sensors, but the temperature of sensing was higher than room temperature i.e. 200 °C.

In order to integrate the higher sensitivity of the SnO₂ based sensor and the ability of RGO to detect gases at room temperature (which helps in lowering the power dissipation), RGO–SnO₂ hybrid sensing layers were prepared by mixing GO and SnO₂ in different wt%. The sample prepared after mixing GO to SnO₂ in the ratio of 10 : 3 and then thermally reducing it demonstrated enhanced sensitivity, even at room temperature, but the resistance of the hybrid sensing layer increased when exposed to ammonia, thus showing an effective p-type behavior. The response of the hybrid sensor towards four different concentrations of ammonia is shown in Fig. 8(a). The 10 : 3 (RGO : SnO₂) exhibited a response of 0.665 times against 1200 ppm of ammonia. Again, when the amount of SnO₂ was increased to 10 : 4 wt% (RGO : SnO₂), the resistance of the hybrid sensor was found to decrease at room temperature when ammonia was introduced to the test chamber, thus showing an effective n-type behavior. A response of around 3.4 times was observed against 1200 ppm ammonia for the 10 : 4 (RGO : SnO₂) sample, as shown in Fig. 8(b).

Fig. 8 Response of (a) 10 : 3 (RGO : SnO₂), (b) 10 : 4 (RGO : SnO₂) towards four different concentrations of ammonia (400–1200 ppm), (c) comparative response of intrinsic RGO, SnO₂ and the RGO–SnO₂ hybrid sensor prepared hydrothermally and by varying the wt% of GO and SnO₂ nanoparticles towards 1200 ppm ammonia (here RT refers to room temperature) and (d) a comparative plot of recovery times of intrinsic RGO, SnO₂ and hybrid RGO–SnO₂ sensors.

In order to further investigate the role of the amount of SnO₂ in the hybrid film, the wt% of SnO₂ was gradually increased and ammonia tests were carried out. It was observed that with increase in the amount of SnO₂ in the RGO–SnO₂ hybrid film, the response of the sensors got enhanced towards ammonia (at room temperature). However, when RGO and SnO₂ were mixed in 1 : 1 ratio, the samples did not show any measurable resistance up to 290 °C. Such an increment of resistance of the RGO–SnO₂ hybrid sample is associated with the depletion of electrons due to the formation of a p–n junction in the hybrid sample, as reported in the literature.¹⁹ Therefore, the ammonia test was carried out at 300 °C for this 1 : 1 ratio sample, but we observed that the response got reduced. For example, the response of this sample towards 1200 ppm ammonia was found to be ∼1.3 times only. This response from a 1 : 1 (RGO : SnO₂) ratio sample was found to be similar to the response of pristine SnO₂ nanoparticles at 300 °C, as shown in Fig. 7(b). This suggests that SnO₂ nanoparticles were playing the predominant role for ammonia sensing in the 1 : 1 hybrid sample.

The hydrothermally synthesized hybrid sample also did not show any measurable resistance at room temperature. This could be explained as follows: RGO can sense gases at room temperature due to its planar 2D structure. However, when the hybrid sample was synthesized hydrothermally, the RGO flakes got folded and formed multilayered structures, as is evident in Fig. 3(d), due to which the 2D structure no longer existed and thus no resistance could be measured at room temperature. Therefore, the ammonia test was carried out at 120 °C. Moreover, the response of the hydrothermally synthesized sample was found to be poorer than the samples prepared by mixing GO and SnO₂. For example, the response of the hydrothermally synthesized sample towards 1200 ppm ammonia was found to be ∼1.9 times. The 10 : 8 (RGO : SnO₂) hybrid sample showed the maximum response towards (measurements carried out at 1200 ppm) ammonia, as can be seen in Fig. 8(c). Moreover, it was observed that the sensors recovered faster with increase in the amount of SnO₂ nanoparticles in hybrid samples, as shown in Fig. 8(d). The recovery time of the 10 : 8 (RGO : SnO₂) hybrid sensor was found to be the shortest among all the sensors. The recovery times of the RGO based ammonia sensor was not included in Fig. 8(d) because the recovery time is comparatively longer as is evident from the response plot of RGO in Fig. 7(a). The response times of the RGO–SnO₂ hybrid samples were found to be comparable with that of intrinsic SnO₂ nanoparticles but were to be found considerably faster than that of intrinsic RGO. For example, the response time of the 10 : 8 (RGO : SnO₂) sample was found to be around 210 s against 1200 ppm ammonia, whereas the response times of RGO and SnO₂ nanoparticles against 1200 ppm ammonia were 450 and 210 s, respectively. A comparative plot of response times of RGO and SnO₂ hybrid samples for different concentrations of ammonia is shown in Fig. S2 of the ESI.† The response of the best sample (10 : 8 (RGO : SnO₂)) to different concentrations of ammonia is shown in Fig. 9.

Fig. 9 Response of the 10 : 8 (RGO : SnO₂) sensor towards nine different concentrations of ammonia (25–2800 ppm), inset shows the magnified response of the sensor against lower concentrations of ammonia (25–300 ppm).

The 10 : 8 (RGO : SnO₂) sensor was also exposed to nine different concentrations of ammonia (25–2800 ppm) in the presence of 20% RH, in order to ensure its performance as a practical ammonia sensor. The response of the 10 : 8 (RGO : SnO₂) sensor was found to be excellent, as shown in Fig. 9, and varied from 1.4 times against 25 ppm ammonia to 22 times against 2800 ppm ammonia. In addition, the recovery of the sensor was very fast. It took merely 150 seconds to recover to its baseline resistance after exposure to 400 ppm of ammonia (but in the case of RGO, the recovery was found to be more than 650 s, which is very slow). The response of all the other hybrid samples against different concentrations of ammonia is shown in the ESI (Fig. S3–S5†). The responses of the sensors are also highly reproducible and repeatable. The reproducible response of our best sample is shown in Fig. 10(a).

Fig. 10 (a) Reproducible response of the 10 : 8 (RGO : SnO₂) hybrid sensor towards three different concentrations of ammonia (1200–2800 ppm) and (b) comparison between the results recently reported on RGO–SnO₂ based ammonia sensor and our samples.

The 10 : 8 (RGO : SnO₂) hybrid sensor device was prepared twice and the response of both the sensors towards ammonia was found to be similar, as shown in Fig. 10(a). However, it was observed that the recovery of the first sample (Sample 1) was faster than the second sample (Sample 2). The reason is not yet known and needs further investigation.

Recent reports on RGO–SnO₂ based hybrid gas and volatile organic compound (VOC) sensors are available in the literature.^20–23 However, in most of the cases, the detection temperature was high. A comparison of our achieved response with recently reported literature values on RGO–SnO₂ based ammonia sensor is shown in Fig. 10(b) (only the work reported by Ghaddab et al. is on ammonia sensing by a SWNT/SnO₂ hybrid).^24–28 The temperature of sensing for all the reported works have been indicated in the plot itself. For our results, the temperature of sensing is room temperature. It was found that our response is better than reported literature values against respective concentrations of ammonia. Only the response of the sensors reported by Zhang et al. was better than our result, but their working temperature was very high (260 °C). The response of the hybrid ammonia sensor in air ambience reported by Lin et al. was found to be poorer than the response (in N₂ ambience) that has been plotted in Fig. 10(b). They achieved a response of around 7% i.e. 0.9347 times against 50 ppm of ammonia in air ambience, while in our case all the tests were done in air only.

Metal oxide based sensors are supposed to have very poor selectivity. Therefore, in order to ensure the selectivity of our sensor, the 10 : 8 (RGO : SnO₂) hybrid film was exposed to 1000 ppm of different volatile organic compounds (VOCs) and also to 30% RH.

The response of the 10 : 8 (RGO : SnO₂) hybrid sensor towards 1000 ppm of different VOCs was found to be poorer (∼1.25 times) than that of 1000 ppm of ammonia (5.6 times), as is evident from Fig. 11. Moreover, as the ammonia tests were carried out at 20% RH; therefore, the sensor was also exposed to 30% RH to ensure that there was no considerable contribution of RH to the response of the sensors towards ammonia. It was observed that the sensor did not respond well against 30% RH. A mere response of 1.08 times was observed against RH. Hence, the hybrid sensor was found highly selective towards ammonia.

Fig. 11 Comparative response of all the VOCs at 1000 ppm and 30% RH.

3.2. Sensing mechanism

The basic sensing mechanisms of RGO (predominantly p type due to an abundant amount of holes) and tin oxide (adsorbed oxygen species from air act as reaction sites at the metal oxide surface) are already discussed in the earlier section. However, the hybrid samples exhibited enhanced response towards ammonia compared to that of intrinsic RGO and SnO₂. This is because of the formation of hetero and homo junctions at the interface of the materials, (i) RGO–SnO₂ and (ii) SnO₂–SnO₂, as is shown in Fig. 12. In the case of hetero junctions (p-type RGO and n-type SnO₂), SnO₂ donates electrons to RGO, which recombine with the holes of RGO, thereby shifting the Fermi level and thus a depletion region forms. This depletion region is an additional site for the target gases and attracts electrons from the donor molecules (here ammonia) and results in an increase in conductivity. Along with the RGO–SnO₂ depletion zone, SnO₂–SnO₂ depletion region and also the functional groups attached with RGO are active sites for the analytes to react and get attached.

Fig. 12 Schematic (not to scale) representation of sensing mechanism in (a) the 10 : 3 (RGO : SnO₂) sample showing p-type behavior and (b) the 10 : 8 (RGO : SnO₂) sample showing n-type behavior.

In addition to superior response, our sensors also showed both p-type and n-type behavior, so the sensing mechanism was dominated by the material proportion present in the hybrid film. For example, the 10 : 3 (RGO : SnO₂) sample demonstrated a p-type behavior towards ammonia, which is similar to that shown by intrinsic RGO, but an enhanced response was observed in the hybrid sample. This means the sensing was dominated by RGO being available in abundance, but the enhancement of response was observed due to the presence of SnO₂ nanoparticles, which gives rise to different potential barriers at the junctions as mentioned above. The sensing mechanism of the 10 : 3 (RGO : SnO₂) sample is shown in Fig. 12(a).

On the other hand, the sensing phenomenon of the 10 : 8 (RGO : SnO₂) sample is different than that of the 10 : 3 (RGO : SnO₂) hybrid sample. Here RGO, acted primarily as a conducting network, which resulted in a measurable conductivity of the hybrid sample at room temperature, even though there is a large proportion of tin oxide particles, as is also evident from the FESEM image. Here the response of the hybrid sample is predominantly due to the SnO₂–SnO₂ depletion region and the oxide sites presence at the surface of the tin oxide particles, as can be seen in Fig. 12(b). This is also evident from the effective n-type behavior of the hybrid sensing layer. In addition, there is a contribution of potential barrier presence at the RGO–SnO₂ hetero junction. Thus, the response of the hybrid sample for ammonia was found to be considerably higher than that observed in intrinsic SnO₂ nanoparticles and RGO.

The sensing phenomena of all the n-type behaving samples (10 : 4, 10 : 5, 1 : 1 and hydrothermal) is similar to that of 10 : 8 samples as explained above. The increase in sensitivity towards ammonia with the amount of SnO₂ is due to an increase in SnO₂–SnO₂ homojunctions in the sensing layers.

4. Conclusions

RGO–SnO₂ hybrid ammonia sensors were fabricated. The sensing layers were synthesized by varying the concentration of SnO₂ in RGO and their performances as ammonia detectors at room temperature were observed. Not only an enhanced response over intrinsic RGO and SnO₂ was achieved, but also the sensors recovered faster with high selectivity towards ammonia. The sensing performance of in situ hydrothermally synthesized RGO–SnO₂ hybrid sample was found to be poorer than that of the samples prepared by mixing SnO₂ nanoparticles and GO (synthesized separately). Moreover, the hybrid sensor could sense analytes at room temperature, thereby reducing the power consumption of the sensors.

Acknowledgements

S. Santra acknowledges Department of Science and Technology (DST), India for supporting the work partially (project no SR/S2/RJN-104/2011). D. Pradhan and P. K. Guha acknowledge SRIC, IIT Kharagpur for the ISIRD grant to partially support this work. The authors acknowledge DST-FIST Lab (in Department of Physics, IIT Kharagpur) and Central Research Facility, IIT Kharagpur to let us use the XPS facility and FESEM, HRTEM and XRD respectively.

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Footnote

† Electronic supplementary information (ESI) available. See DOI: 10.1039/c5ra06696d

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