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

The detection of nitrogen dioxide (NO 2 ) is one of great significance in agricultural development and health care monitoring. It is urgent to develop a new type of NO 2 sensor with enhanced sensitivity, improved selectivity at low temperature. The search for advanced gas sensing materials to efficiently detect NO 2 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, SnO 2 nanoneural network material composed of ultrasmall SnO 2 nanoparticles (3 nm) was successfully obtained and has high specific surface area (96.69 m 2 ·g −1 ). The results of gas sensing test showed that the SnO 2 nanoneural network sensor exhibited excellent properties to NO 2 gas at 50 °C, including high response (72.6–10 ppm NO 2 ), good reproducibility and selectivity. In addition, the SnO 2 nanoneural network sensor has perfect moisture resistance and 12 weeks long-term stability for NO 2 detection. It proves that construction of SnO 2 nanoneural network by ultrasmall particles is an effective strategy to improve the sensing performance of NO 2 sensors, and its unique opening hierarchical structure provides a broader idea for improving the performance of gas sensors.