Pocket Electronic Nose Integrating an Ultra-Compact Sensor Array Chip and Spatiotemporal Network Enables Highly Selective Gas Sensing
Xingguo Wang, Xin Kang, Xinyi Chen, Yuhao Xu, Peng Ye, Jin Cui, Bin Ai
Abstract
Accurately distinguishing gases with nearly identical molecular structures─such as nitric oxide (NO) and nitrogen dioxide (NO 2 )─remains challenging for conventional sensors. We report a palm-sized (5 cm × 5 cm) electronic nose that integrates an ultralow-power microelectro-mechanical systems (MEMS) sensor array with a spatiotemporal deep-learning model (STNet), for trace-level detection and quantification of NO and NO 2 . The array contains nine carbon-based nanocomposite sensors monolithically fabricated on a 3 mm × 3 mm chip; each sensor operates at room temperature, consumes <2 mW, and achieves detection limits below 0.5 ppm for both gases. STNet combines an enhanced Transformer encoder with a temporal convolutional network, simultaneously capturing intersensor correlations and long-range temporal dependencies. Evaluated on laboratory-generated data sets, the system reduces misclassification rates by up to 50% and improves concentration-prediction accuracy by 25% relative to state-of-the-art CNN and LSTM baselines. Powered and controlled by a smartphone running the embedded STNet model, the device delivers on-site analysis with subsecond latency. By uniting highly selective sensing hardware with efficient edge-level inference, this platform overcomes long-standing limitations in selectivity, portability, and power consumption, offering a scalable solution for environmental monitoring, industrial process control, and medical diagnostics.