Understanding Enhanced Aniline Sensing with Cu-Doped SnO2 via GC-MS Analysis.

The rational design of high-performance metal oxide gas sensors is often limited by an insufficient understanding of the gas-solid interface interaction mechanisms. This study employs headspace gas chromatography-mass spectrometry (GC-MS) to dynamically investigate surface reaction between aniline vapor and Cu-doped SnO2 nanoparticles. The GC-MS results suggest that Cu ion doping facilitates the oxidation of aniline to produce azobenzene, which is not detected on pristine SnO2 under identical conditions. This special catalytic reaction is likely associated with the enhanced selectivity and response value of Cu-SnO2 toward aniline vapor. Crucially, a positive correlation is identified between the aniline oxidation rate, quantified by GC-MS, and the sensor's response value. This mechanistic insight is leveraged to rationally optimize the Cu doping concentration, yielding a Cu-doped SnO2 sensing material with an optimal composition. The resulting sensor exhibits an outstanding aniline sensing performance of high response (4.5@10 ppm), low detection limit (80 ppb), rapid response time (∼25s), and good selectivity. This work not only demonstrates a viable approach for metal-catalyzed selective detection of volatile organic compounds (VOCs) but also provides a general methodology for quantitatively correlating gas-solid reaction processes with gas sensing performance, providing critical theoretical and technical support for the rational design of advanced gas sensors.