Designing optimized ternary catalytic alloy electrode for efficiency improvement of semiconductor gas sensors using a machine learning approach

  • Suman Ghosal Ghosal Department of Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology, West Bengal, India
  • Swati Dey Department of Aerospace Engineering and Applied Mechanics, Indian Institute of Engineering Science and Technology, West Bengal, India
  • Partha Pratim Chattopadhyay National Institute of Foundry and Forge Technology, Hatia, India
  • Shubhabrata Datta Department of Mechanical Engineering, SRM Institute of Science and Technology, Chennai India
  • Partha Bhattacharyya Department of Electronics and Telecommunication Engineering, Indian Institute of Engineering Science and Technology, West Bengal, India
Keywords: Oxide based gas sensor, Ternary alloy catalyst design, Sensing parameters, Artificial neural network, Genetic algorithm, Multi-objective optimization

Abstract

Catalytic noble metal (s) or its alloy (s) has long been used as the electrode material to enhance the sensing performance of the semiconducting oxide based gas sensors. In the present paper, design of optimized ternary metal alloy electrode, while the database is in pure or binary alloy compositions, using a machine learning methodology is reported for detection of CH4 gas as a test case. Pure noble metals or their binary alloys as the electrode on the semiconducting ZnO sensing layer were investigated by the earlier researchers to enhance the sensitivity towards CH4. Based on those research findings, an artificial neural network (ANN) models were developed considering the three main features of the gas sensor devices, viz. response magnitude, response time and recovery time as a function of ZnO particle size and the composition of the catalytic alloy. A novel methodology was introduced by using ANN models considered for optimized ternary alloy with enriched presentation through the multi-objective genetic algorithm (GA) wherever the generated pareto front was used. The prescriptive data analytics methodology seems to offer more or less convinced evidences for future experimental studies.

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Published
2021-06-13
How to Cite
Ghosal, S. G., Dey, S., Chattopadhyay, P. P., Datta, S., & Bhattacharyya, P. (2021). Designing optimized ternary catalytic alloy electrode for efficiency improvement of semiconductor gas sensors using a machine learning approach. Decision Making: Applications in Management and Engineering, 4(2), 126-139. https://doi.org/10.31181/dmame210402126g