Abstract
Semiconductor metal oxide (SMO) gas sensors are gaining prominence owing to their high sensitivity, rapid response, and cost-effectiveness. These sensors detect changes in resistance resulting from oxidation-reduction reactions with target gases, responding to a variety of gases simultaneously. However, their inherent limitations lie in selectivity. Despite attempts to address this through new sensing materials and filters, achieving perfect selectivity remains challenging. This study addresses the selectivity issue by implementing temperature-modulated operation of a single SMO gas sensor utilizing an anodic aluminum oxide (AAO) microheater platform. The AAO-based sensor ensures a high thermal and mechanical stability during prolonged temperature modulation. A staircase waveform featuring six temperature conditions was applied to the microheater platform, and gas response data were collected for acetone, ammonia, ethanol, and nitrogen dioxide. Leveraging a convolutional neural network (CNN), gas patterns were trained and used to predict gas types and concentrations. The results demonstrated a high classification accuracy of 97.0%, with mean absolute percentage errors (MAPE) for concentration estimation of acetone, ammonia, ethanol, and nitrogen dioxide at 13.7, 19.2, 19.8, and 19.4%, respectively. The proposed method effectively classified four spices and accurately distinguished similar odors, which are difficult for human olfaction to differentiate. The results highlight that the combination of temperature modulation and deep learning algorithms proves to be highly effective in precisely determining gas types and concentrations.
Published Version
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