Smart SO2 Sensor Based on Cs3Cu2I5@Fe2O3 Nanocrystals for Eliminating Humidity Interference

• Published in: IEEE sensors journal 2023-12, Vol.23 (24), p.30504-30515
• Main Authors: Zhuang, Yuyan, Zhu, Jintuo, Gao, Danhong, Gao, Shasha, Yang, Zijiang, Huang, Sheng, He, Xinjian
• Format: Article
• Language: English
• Subjects: Alexnet network model; Algorithms; Density functional theory; Electron orbitals; Ferric oxide; films of Cs3Cu2I5@Fe2O3; Gas detectors; Gas sensors; Gramian angular field (GAF); High temperature; Humidity; Interference; Machine learning; Metal oxides; Nanocrystals; Perovskites; Recognition; Recovery time; Respiration; Respiratory system; Room temperature; room-temperature SO2 sensor; self-calibrating SO2 sensors; Sensors; Sulfur; Sulfur compounds; Sulfur dioxide; Temperature sensors; Water stability
• ISSN: 1530-437X; 1558-1748
• DOI: 10.1109/JSEN.2023.3329497

Sulfur dioxide is a colorless gas with strong pungent odor, and prolonged inhalation can damage the respiratory system and heart. Precise measurement of sulfur dioxide is very important for the human health. While the current sulfur dioxide sensor works at a high temperature and is easily interfered by humidity. Therefore, in this work, we used a solution method to coat perovskite nanocrystals Cs3Cu2I5 with metal oxides Fe2O3 and obtained the room-temperature sulfur dioxide gas-sensitive materials Cs3Cu2I5@Fe2O3 nanocrystals. Meanwhile, combined with machine learning, the sensor has the ability of self-calibrating under different humidity environment, realizing the high precision and anti-interference measurement of sulfur dioxide. In addition, in this structure, the coating Fe2O3 not only helps to improve water stability but also transfers the interaction of sulfur dioxide to the inner Cs3Cu2I5 and lowers the gas-sensitive temperature to room temperature. The response/recovery time of the sulfur dioxide sensor based on Cs3Cu2I5@Fe2O3 is 31/816 s, and the sensitivity is 0.19 at 10 ppm. Then, intelligent classification algorithm was used for recognition, and the accurate recognition rate was up to 95.0%. Furthermore, density functional theory (DFT) was implemented to reveal the gas-sensitive mechanism that sulfur dioxide was adsorbed by Cs3Cu2I5@Fe2O3 nanocrystals, the good response was attributed to the band structure changes significantly, and the hybridization of electron orbitals was appeared between gas and nanocrystals. We believe that the sensor will have potential in sulfur dioxide, and the idea of using machine learning to intelligent eliminate humidity interference can also be extended to other gas sensor.