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

Author

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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)

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.

Suggested Citation

  • Chi-Yo Huang & Pei-Han Chung & Joseph Z. Shyu & Yao-Hua Ho & Chao-Hsin Wu & Ming-Che Lee & Ming-Jenn Wu, 2018. "Evaluation and Selection of Materials for Particulate Matter MEMS Sensors by Using Hybrid MCDM Methods," Sustainability, MDPI, vol. 10(10), pages 1-35, September.
    • Handle: RePEc:gam:jsusta:v:10:y:2018:i:10:p:3451-:d:172426
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    References listed on IDEAS

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    Cited by:

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