Enhancing Accuracy of Nanocomposite Hydrogen Sensors in Various Environmental Situations through Machine Learning

• Published in: Journal of semiconductor technology and science Vol. 24; no. 5; pp. 393 - 398
• Main Authors: Cho, U-Jin, Jeon, Youhyeong, Park, Sung-Wook, Kwon, Min-Woo
• Format: Journal Article
• Language: English
• Published: 대한전자공학회 01.10.2024
• Subjects: 전기공학; Random forest model; palladium; molybdenum disulfide; hydrogen; nanocomposite sensor
• ISSN: 1598-1657; 2233-4866; 2233-4866; 1598-1657
• DOI: 10.5573/JSTS.2024.24.5.393

This paper presents a proof of concept that combines a nano-composite hydrogen detecting sensor and machine-learning technique to achieve accurate and fast detection of hydrogen leakage. The nano-composite hydrogen detecting sensor is fabricated by depositing MoS2 on a SiO2/Si wafer using chemical vapor deposition, followed by forming discrete Pd nanoparticles through DC (Direct current) sputtering. This sensor shows high sensitivity of 2.77 and fast response time of 4 to 5 seconds at room temparature, but has a significant dependency on environmental factors such as temperature, and humidity. A machine learning technique, i.e. random forest, was incorporated to filter out the environmental factors. Experimental results show that the combination, i. e. MiCS-2714 sensor not only retains sensitivity, response time of the nano-composite but also attains R2 score of 0.994, MSE 0.0506, and the state classification accuracy of 0.979. KCI Citation Count: 0