MXene/WO3 Sensor Array with Improved SNN Algorithm for Accurate Identification of Toxic Gases

• Published in: ACS applied materials & interfaces Vol. 16; no. 45; pp. 62421 - 62428
• Main Authors: Guo, Liangchao, Wang, Junke, Han, Haoran, Wang, Peng, Lu, Yunxiang, Yuan, Qilong, Du, Chunyu, Yin, Shuo, Zhou, Ye, Zhang, Chao
• Format: Journal Article
• Language: English
• Published: American Chemical Society 13.11.2024
• Subjects: algorithms; ambient temperature; electronic nose; food safety; Functional Inorganic Materials and Devices; nanocomposites; nanoparticles; toxicity; V2CT x /WO3; Electronic nose systems; Spiking neural network; Artificial synapses; Machine learning
• ISSN: 1944-8244; 1944-8252; 1944-8252
• DOI: 10.1021/acsami.4c14793

Gas sensing is pivotal in critical areas such as industrial production and food safety. This study explores the gas classification capabilities of MXene-based gas sensors. Pure V2CT x MXene and an MXene/WO3 nanocomposite were synthesized, and MXene-based gas sensors were integrated into a 2 × 2 rudimentary electronic nose array. The tests on gas sensitivity revealed that the inclusion of WO3 nanoparticles (NPs) boosted the sensor’s response to 10 ppm of NO2 from 2.82 to 3.45 at room temperature. Moreover, the sensor showcased a rapid response/recovery duration of 74.5/149.0 s, excellent environmental stability, and long-term reliable sensing performance. Furthermore, we have improved the method of accurately identifying four toxic gases detected by an MXene-based sensor array using a spiking neural network (SNN) based on the memristive system. Also, the performance of this identification method revealed that the method achieved 95.83% accuracy in the identification of the four gases. Notably, the improved SNN demonstrated approximately 5% higher accuracy than the other gas recognition algorithm. These results highlight the potential of SNN as a powerful tool to accurately and reliably identify toxic gases based on the gas sensor array.