Construction of SnO2 nanoneural network by ultrasmall particles for highly selective NO2 detection at low temperature

The detection of nitrogen dioxide (NO2) is one of great significance in agricultural development and health care monitoring. It is urgent to develop a new type of NO2 sensor with enhanced sensitivity, improved selectivity at low temperature. The search for advanced gas sensing materials to efficiently detect NO2 gas at low temperature has much attracted the attention of researchers. Sensing materials with ultrasmall particle size is crucial to enhance response of gas sensor, and porous structure with large specific surface area is better for gas performance. In this study, SnO2 nanoneural network material composed of ultrasmall SnO2 nanoparticles (3 nm) was successfully obtained and has high specific surface area (96.69 m2·g−1). The results of gas sensing test showed that the SnO2 nanoneural network sensor exhibited excellent properties to NO2 gas at 50 °C, including high response (72.6–10 ppm NO2), good reproducibility and selectivity. In addition, the SnO2 nanoneural network sensor has perfect moisture resistance and 12 weeks long-term stability for NO2 detection. It proves that construction of SnO2 nanoneural network by ultrasmall particles is an effective strategy to improve the sensing performance of NO2 sensors, and its unique opening hierarchical structure provides a broader idea for improving the performance of gas sensors.