Porous SnO₂ nanosheets for room temperature ammonia sensing in extreme humidity

Gas sensors based on tin dioxide (SnO₂) for the detection of ammonia (NH₃) have become commercially available for environmental monitoring due to their reactive qualities when exposed to different gaseous pollutants. Nevertheless, their implementation in the medical field has been hindered by certain inherent drawbacks, such as needing to operate at high temperatures, lack of selectivity, unreliable operation under high-humidity conditions, and a lower detection limit. To counter these issues, this study created 2D nanosheets of SnO₂ through an optimized solvothermal method. It was found that tuning the precursor solution's pH to either neutral or 14 led to aggregated or distributed, uniform-size nanosheets with a higher crystallinity, respectively. Remarkably, the SnO₂ nanosheet sensor (SNS-14) displayed a much lower response to water molecules and specific reactivity to ammonia even when subjected to reducing and oxidizing agents at 25 °C due to the micropores and chemisorbed oxygen on the nanosheets. Furthermore, the SNS-14 was seen to have the highest sensitivity to ammonia at 100 ppm, with rapid response (8 s) and recovery times (55 s) even at a high relative humidity of 70%. Its theoretical detection limit was recorded to be 64 ppt, better than any of the earlier SnO₂-based chemiresistive sensors. Its exceptional sensing abilities were credited to its optimal crystallinity, specific surface area, defects, chemisorbed oxygen, and porous structure. NH₃-TPD measurements and computational simulations were employed to understand the ammonia interaction with atomistic details on the SnO₂ nanosheet surface. A real time breath sensing experiment was simulated to test the efficacy of the sensor. Reaching this advancement is an achievement in bypassing past boundaries of SnO₂-centered sensors, making it feasible to detect ammonia with enhanced precision, discrimination, dependability, and velocity for probable usages in medical diagnostics and ecological surveillance.