Microchip Employing Short Period Thermal Modulation for the Detection of H₂S and SO₂ Mixtures

Jifeng Chu, Qiongyuan Wang, Yuyang Liu, Jianbin Pan, Huan Yuan, Aijun Yang, Xiaohua Wang, Mingzhe Rong

Abstract: Most of the previous works used to improve the performance of single gas sensors by forming the array. However, more discrete sensors would bring higher costs, larger volumes, and power consumption. With the technique of micro-electro-mechanical systems (MEMS), a microchip with three sensitive materials (ZIF8-WO3, ZIF8-In2O3, and ZIF8-SnO2) has been fabricated to detect the mixtures of H2S and SO2. By combining the short-period thermal (SPT) modulation with the periodic sampling, the response curve could be split into several sub-curves, and each sub-curve was regarded as the virtual device operated at a constant temperature (CT). It can reduce power consumption without increasing the physical volume. Based on the fluctuation and distinction of the descriptors, the optimal 24 features were extracted from the initial 144 features. A stacked denoising autoencoder (SDAE) was employed to enhance the generalization ability of Bagging neural networks. Toward the unknown 30 mixtures of H2S and SO2, the recognition model under SPT, (78.67%) had better performance than that under CT, (62.67%). SPT (92.63%) has a smaller non-overlapping area of feature buffers than CT (50.45%), which might be the physics behind the performance improvement. Certainly, this work has broad application prospects in the future.