Neural-Network-Assisted Impedance Analysis for Humidity and Ammonia Detection Using MXene and PtSnO ₂ Sensors with Cross-Interference Suppression

High Resolution Image Download MS PowerPoint Slide Nonlinearity, baseline drift, humidity interference, and selectivity are among the primary challenges associated with direct current measurements in semiconducting gas sensors today. This study demonstrates that integrating PtSnO 2 and MXene-based sensors enables high-performance gas sensing by effectively mitigating humidity-induced interference. Impedance measurements across multiple frequencies were employed, yielding sensors with tunable gas responses, low noise, extended dynamic range, enhanced baseline stability, and minimal humidity cross-sensitivity. PtSnO 2 was optimized for simultaneous NH 3 and relative humidity (RH) detection, while MXene served as a dedicated RH sensor. A multilayer perceptron was trained on the impedance dataset to deconvolute and accurately predict RH and NH 3 concentrations. The proposed sensor system and analytical framework were benchmarked against commercial DHT22 (humidity) and DFrobot (NH 3 ) sensors, demonstrating superior performance and sensitivity. This methodology is extendable to other material systems, including metal oxides and transition metal dichalcogenides, for advanced gas-sensing applications. These findings advance gas-sensing technology by improving detection accuracy and robustness in industrial safety, environmental monitoring, and biomedical diagnostics, including early disease detection.