Intelligent Recognition of Sulfur-Containing VOCs Using a Au/Pd-Modified RGO-SnO2 Gas Sensor Array

To address the challenge of accurately distinguishing structurally similar sulfur-containing volatile organic compounds (VOCs) using a single gas sensor because of their similar response characteristics, this study developed a gas sensor array based on Au/Pd-modified rGO/SnO2 devices for the detection and recognition of 1-butanethiol (BT), dimethyl sulfide (DMS), and allyl methyl sulfide (AMS). rGO and SnO2 sensing layers were sequentially fabricated on Si/SiO2 substrates, followed by the deposition of Au or Pd and subsequent annealing, resulting in three sensor types: pristine rGO/SnO2 Au-modified rGO/SnO2, and Pd-modified rGO/SnO2. SEM, Raman, and XPS characterizations revealed the nanoscale dispersion of Au and Pd on the rGO/SnO2 surface while confirming the carbon framework of rGO and the characteristic vibrational and chemical-state features of SnO2. Gas-sensing measurements showed that all three sensor types exhibited clear responses to BT, DMS, and AMS, with the overall response increasing as the gas concentration increased from 1 ppm to 10 ppm. Distinct differences were observed among the three sensor types in response amplitude, response and recovery dynamics, and concentration dependence, providing complementary temporal information for subsequent array-based recognition. To enable reliable signal acquisition, an STM32F103-based signal acquisition and processing circuit was further developed, incorporating constant-voltage excitation, current sensing, signal amplification, and signal conditioning. Based on the acquired dynamic response curves, a three-branch multi-scale one-dimensional residual network was employed for gas classification. Multi-scale convolution, residual connections, and squeeze-and-excitation (SE) channel attention were integrated to extract discriminative temporal features from the gas response process. Across three random seeds, the model achieved mean test-set accuracy, precision, recall, and F1-score values of 95.42% ± 0.34%, 95.46% ± 0.32%, 95.45% ± 0.36%, and 95.39% ± 0.35%, respectively. These results demonstrate that Au/Pd modification enhances the complementary and differential responses of the rGO/SnO2 sensor array toward sulfur-containing VOCs, while the integration of the sensor array with deep learning enables effective recognition of the three target gases. This work provides an experimental basis for the integrated implementation of gas sensing, signal acquisition, and intelligent recognition.