Evaluation and Selection of Materials for Particulate Matter MEMS Sensors by Using Hybrid MCDM Methods

Chi-Yo Huang, Pei-Han Chung, Joseph Z. Shyu, Yao-Hua Ho, Chao-Hsin Wu, Ming-Che Lee and Ming-Jenn Wu
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Chi-Yo Huang: Department of Industrial Education, National Taiwan Normal University, Taipei 106, Taiwan
Pei-Han Chung: Institute of Management of Technology, National Chiao-Tung University, 1001 University Road, Hsinchu 300, Taiwan
Joseph Z. Shyu: Institute of Management of Technology, National Chiao-Tung University, 1001 University Road, Hsinchu 300, Taiwan
Yao-Hua Ho: Department of Computer Science and Information Engineering, National Taiwan Normal University, Taipei 106, Taiwan
Chao-Hsin Wu: Department of Electrical Engineering, National Taiwan University, Taipei 106, Taiwan
Ming-Che Lee: Department of Mechanical Engineering, National Taipei University of Technology, Taipei 106, Taiwan
Ming-Jenn Wu: Department of Industrial Education, National Taiwan Normal University, Taipei 106, Taiwan

Sustainability, 2018, vol. 10, issue 10, 1-35

Abstract: Air pollution poses serious problems as global industrialization continues to thrive. Since air pollution has grave impacts on human health, industry experts are starting to fathom how to integrate particulate matter (PM) sensors into portable devices; however, traditional micro-electro-mechanical systems (MEMS) gas sensors are too large. To overcome this challenge, experts from industry and academia have recently begun to investigate replacing the traditional etching techniques used on MEMS with semiconductor-based manufacturing processes and materials, such as gallium nitride (GaN), gallium arsenide (GaAs), and silicon. However, studies showing how to systematically evaluate and select suitable materials are rare in the literature. Therefore, this study aims to propose an analytic framework based on multiple criteria decision making (MCDM) to evaluate and select the most suitable materials for fabricating PM sensors. An empirical study based on recent research was conducted to demonstrate the feasibility of our analytic framework. The results provide an invaluable future reference for research institutes and providers.

Keywords: particulate matter (PM); PM2.5; sensors; micro electro mechanic systems (MEMS); multiple criteria decision making (MCDM) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

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