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.