Unraveling the promoted nitrogen dioxide detection performance of N-doped SnO2 microspheres at low temperature
Introduction
Nitrogen dioxide, as one of the components of automobile exhausts and fossil fuel combustion emissions, causes serious air pollution and is regarded as the main source of photochemical smog and acid rain [1,2]. It threatens the natural environment and injures the human health [3]. Therefore, it is highly significant and essential to develop NO2 sensors with superior gas-sensing performances for the environment monitoring. Many metal oxide semiconductors, including SnO2 [[4], [5], [6]], In2O3 [7,8], ZnO [9], and TiO2 [10], have been reported in the gas sensor due to their high sensitivity, low cost and simplicity in fabrication. Among them, SnO2 is a common n-type semiconductor with a wide band gap (3.6 eV) and have been regarded as a candidate for NO2 detection because of its low cost, nontoxic nature, good stability and superior sensing performances [[11], [12], [13]]. However, the application of SnO2-based gas sensors for NO2 detection is still faced with some challenges such as low sensitivity, high operating temperature, long response/recovery time to low concentration of NO2 gas [[14], [15], [16]]. The high operating temperature not only increases the wasteful power consumption, but also induces the thermal growth effect of grain leading to the decrease in its service life [17]. Hence, the development of SnO2-based sensing materials with superior NO2 gas-sensing performances at low operating temperature is highly desirable.
The sensing mechanism of SnO2-based sensing materials is related to the variation in the resistance of themselves in tested gas, which is caused by the electron exchange between the detected gas and the sensing material [16]. In general, the sensing process of SnO2-based sensing materials includes three processes, which are the tested gas diffusion, gas adsorption and the sensing reaction, respectively. Therefore, the gas-sensing performance of SnO2-based sensing materials are determined by three main factors, namely the geometrical effect, electronic effect and chemical effect [18]. For the geometrical effect, the porous nanostructure with high specific surface area exhibits rich diffusion passageways and active sites, which favor the diffusion and adsorption, improving the response/recovery speed and sensitivity of sensing materials [4,[19], [20], [21]]. For the electronic effect, the bended band, narrow band gap, and efficient charge carrier separation of sensing materials caused by the formation of hetero-junction structure, metal-doping, and light or ion-beam irradiation can result in the more free-electrons, promoted charge carriers transfer, and effective resistance change, which are favorable to the enhancement on the sensitivity of sensing materials [[22], [23], [24], [25], [26]]. For chemical effect, the decreased activation energy and targeted catalytic activity caused by element doping and the modification of catalysts are benefit for the adsorption of targeted gases and the sensing reaction, supporting the enhancement on the sensitivity and selectivity of sensing materials [[27], [28], [29], [30]]. Based on the above analysis, many methods have been used to improve the NO2 sensing performances of SnO2-based sensors, including the preparation of SnO2 porous nanostructures, the modification of the catalytic, constructing the p-n hetero-junction and doping. Among these methods, the doping can effectively improve the sensing performances of sensing materials via the electronic effect and chemical effect. Therefore, the element doped SnO2 porous nanostructures will be an available route to enhance NO2 sensing performances of SnO2-based sensing materials.
Recently, N-doped metal oxides have been found in various applications, such as catalytic supporting materials [[31], [32], [33]], Lithium-ion batteries [34], and gas sensors [[35], [36], [37]]. The N doping can rich the free-electrons and narrow the band gap of metal oxides, which will increase the amount of adsorbed gas molecules and promote effective electron transfer in the sensing reaction [38,39]. In addition, N doping can introduce more surface defects, which result in more active sites for gas molecules [37,40]. Therefore, the nitrogen doping has become a facile strategy for the improvement of the gas-sensing property of metal oxide sensors. However, up to now, NO2 gas-sensing performances of the N–SnO2 is rarely reported and the understanding of mechanisms on the gas-sensing performance enhancement induced by N doping is still quite limited.
In this study, the N-doped SnO2 microspheres with different nitrogen contents were synthesized by annealing the SnO2 microspheres in NH3 atmosphere at different temperatures. Texture characterizations exhibit that the N-doped SnO2 microspheres with a diameter of about 300–500 nm possess porous surfaces. The characterization results indicate that the SnO2 microspheres after NH3 treatment show the formation of N-doping and oxygen vacancy on the surface of the material, rich free-electrons and narrow energy band. To investigate the effect of N doping on the NO2 gas-sensing property of SnO2 microspheres, the sensing property tests of N-doped SnO2 microspheres were performed. The results show that the N–SnO2-200 microspheres exhibit superior selectivity, higher response (S = 155) and low operating temperature toward 5 ppm NO2 in comparison with the pristine and other SnO2-based sensors. However, excessive N impurity results in the poor electron mobility of the material, which suppress the response of the SnO2 to NO2. The sensing mechanism was further studied by VASP Density Functional Theory (DFT) calculations. It indicated that the high response and superior selectivity are attributed to the formation of the N-doping and oxygen vacancies as nitrogen-containing active sites, the increase in the number of free-electrons and narrowed energy band, which favor the adsorption of NO2 gas molecules and promote electron transfer between the sensing material and the tested gas, enhancing NO2 gas-sensing performances of sensing materials. Therefore, this work not only illustrates that N doping is an effective approach to develop metal oxide sensor with improved NO2 gas-sensing performance, but also provides significant guidance for constructing high-performance nitrogen dioxide sensors.
Section snippets
Materials and synthesis
All chemicals with analytical grade were purchased from Sinopharm Chemical Reagent Co., Ltd and used without further purification. The SnO2 microspheres were prepared according to our previous report [41]. The N–SnO2 microspheres with different concentrations of N doping were obtained by calcining the SnO2 microspheres under NH3 atmosphere at different temperatures (100 °C, 200 °C, 300 °C, and 400 °C) with a heating rate of 2 °C/min for 2 h. The prepared N-doped SnO2 microspheres were denoted
Characterization of N-doped SnO2 porous microspheres
The crystal structures of SnO2 and N-doped SnO2 microspheres were characterized via X-ray diffraction (XRD). All diffraction peaks of samples match well with the tetragonal phased SnO2 (JCPDS 41-1445) (Fig. 1a). With the NH3 treatment temperature rising, the diffraction peak intensity of samples firstly increases and then decreases, and the N–SnO2-400 microspheres display the weakest diffraction pattern (Fig. 1b). It is corresponding to the formation of nitrogen impurity on the surface of N–SnO2
Conclusion
In this work, the N-doped SnO2 microspheres with a diameter of ca. 300–500 nm were synthesized via calcining the parent SnO2 in NH3 atmosphere. The effect of the N doping on the NO2 gas-sensing performance of the SnO2 was systematically investigated. The experimental results indicate that the N–SnO2 microspheres with the optimized nitrogen content (N–SnO2-200) exhibit superior selectivity and improved response (155–5 ppm NO2) in comparison with the pristine and other N-doped SnO2 at low working
CRediT authorship contribution statement
Wenjing Du: Data curation, Formal analysis, Writing - original draft. Wenxu Si: Data curation, Formal analysis, Investigation. Wenzheng Du: Methodology, Software, Validation. Tianhong Ouyang: Methodology, Software, Validation. Fenglong Wang: Methodology, Writing - review & editing. Mengjiao Gao: Formal analysis, Data curation. Lili Wu: Resources, Formal analysis. Jiurong Liu: Funding acquisition, Project administration, Formal analysis, Writing - review & editing. Zhao Qian: Software,
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors acknowledge the financial support from the National Natural Science Foundation of China (No. 51572157), the Qilu Young Scholar Scheme of Shandong University (No. 31370088963043), the Fundamental Research Funds for the Central Universities (2018JC036, 2018JC046) and the Young Scholars Program of Shandong University.
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