High performance bimetal decorated PtNix-WO3 sensors and the cross-sensitivity investigation

• Published in: Sensors and actuators. B, Chemical Vol. 386; p. 133754
• Main Authors: Gu, Yuefeng, Chen, Danfeng, Wu, Tiancheng, Hao, Junyang, Zhang, Zhigang, Zhu, Zhicheng, Xue, Fangfang, He, Shan, Li, Qiuhong
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.07.2023
• Subjects: Cross-sensitivity; Gas sensor; Machine learning; Metal oxide semiconductor; PtNi; PtNi; Gas sensor; Metal oxide semiconductor; Cross-sensitivity; Machine learning
• ISSN: 0925-4005; 1873-3077
• DOI: 10.1016/j.snb.2023.133754

Gas sensors play an essential bridge in connecting humans and health by helping people monitor the gas quality in their living spaces. In this work, PtNi3-WO3 gas sensors were fabricated and demonstrated superior selectivity, sensitivity, and stability to formic acid, which was sufficient for indoor air gas monitoring. The response to 100 ppm formic acid was as high as 591, with a response time of only 3 s, and detection range down to the ppb level. The bimetal PtNi3 decorated sensors showed a 30 times improvement in formic acid response compared to pure WO3 sensors. The cross-sensitivity was investigated, and we observed that the response initially decreased and then increased in a mixed gas with a constant concentration of carboxylic acid and increasing gradually concentration of formaldehyde. The adsorption and desorption mechanisms were studied, which implied that the decomposition and adsorption behavior of the gas could change and produce different intermediate products under different mixing environments. The results of the energy surface testing and density functional theory supported the proposed sensing mechanism. A machine-learning framework was developed for gas identification and concentration prediction, which achieved ultra-high identification accuracy. In addition, Bluetooth-based portable gas sensors were demonstrated, showing promising practical applications. [Display omitted] •The bimetal decorated PtNix-WO3 sensors presented superior sensitivity, selectivity, and stability in detecting formic acid.•The mixed gas adsorption was strongly related to the dispersive and polar sites on the sensing material surfaces.•The machine learning model had 100% accuracy in identifying formic acid and 90.8% accuracy in predicting its concentration.•The Bluetooth-based portable gas sensor demonstrated stable performance for 2 months without significant variation.