Multigas Selective Identification Based on a Single Chemiresistive Gas Sensor via Dynamic Light-Pulse Modulation and Machine Learning

Metal oxide semiconductor (MOS) gas sensors hold great promise for gas detection due to their low cost and miniaturization. Nevertheless, such sensors are unable to distinguish structurally analogous gas molecules due to mere reliance on one-dimensional resistance signals. The construction of a sensor array offers a feasible strategy to improve gas-sensing selectivity by increasing signal dimensionality, while it inevitably increases system complexity and cost, limiting their practical applicability. In this work, we propose a dynamic light-pulse modulation strategy that can expand the signal dimensions based on a single chemiresistive gas sensor, achieving the selective identification of structurally similar volatile organic compounds (VOCs). Using Ag-modified ZnO nanocomposites as the sensing materials, the fabricated gas sensor exhibits favorable response and recovery characteristics toward formic acid under optimal working conditions, yet it also presents highly analogous response behaviors toward structurally similar ethanol and acetic acid. Employing periodic light pulses to the sensor, multidimensional feature parameters are extracted from the transient resistance in different gas environments. By further integrating both dynamic and steady-state parameters into a multidimensional feature set and applying a support vector machine classifier, highly selective recognition of structurally similar VOCs (including formic acid, ethanol, and acetic acid) can be achieved across a wide concentration range, with an identification accuracy rate reaching as high as 97.8%. The proposed strategy combining dynamic light-pulse modulation and machine learning provides a valuable pathway to overcome the cross-sensitivity of MOS sensors, laying a foundation for the development of compact, intelligent gas recognition systems for practical applications.