Multigas Identification by Temperature-Modulated Operation of a Single Anodic Aluminum Oxide Gas Sensor Platform and Deep Learning Algorithm

Abstract:

Semiconductor metal oxide (SMO) gas sensors are gaining prominence owing to their high sensitivity, rapidresponse, and cost-effectiveness. These sensors detect changes in resistance resulting from oxidation−reduction reactions with targetgases, responding to a variety of gases simultaneously. However, their inherent limitations lie in selectivity. Despite attempts toaddress this through new sensing materials and filters, achieving perfect selectivity remains challenging. This study addresses theselectivity 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 temperaturemodulation. A staircase waveform featuring six temperature conditions was applied to the microheater platform, and gas responsedata were collected for acetone, ammonia, ethanol, and nitrogen dioxide. Leveraging a convolutional neural network (CNN), gaspatterns were trained and used to predict gas types and concentrations. The results demonstrated a high classification accuracy of97.0%, with mean absolute percentage errors (MAPE) for concentration estimation of acetone, ammonia, ethanol, and nitrogendioxide at 13.7, 19.2, 19.8, and 19.4%, respectively. The proposed method effectively classified four spices and accuratelydistinguished similar odors, which are difficult for human olfaction to differentiate. The results highlight that the combination oftemperature modulation and deep learning algorithms proves to be highly effective in precisely determining gas types andconcentrations.