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Microsystem Technologies

, Volume 25, Issue 1, pp 115–119 | Cite as

Support vector regression and neural networks analytical models for gas sensor based on molybdenum disulfide

  • Azar Alizadeh
  • Fatemeh Mosalanezhad
  • Abdolkarim Afroozeh
  • Elnaz AkbariEmail author
  • Zolkafle Buntat
Technical Paper

Abstract

In this study, MoS2 gas sensor based on field effect transistor has been proposed and the adsorption of NO2 molecules on the channel surface can lead to significant changes on its electronic and transport properties. The analytical models have been developed for the NO2 gas sensors by making an initial assumption that the gate voltage is directly proportional to the gas concentration. The performance of this sensor, is predicted and investigated by support vector regression (SVR) and artificial neural network (ANN) algorithms. The MoS2 gas sensor displays current changes upon exposure to very low concentrations of NO2. The comparison between analytical model, ANN and SVR with the empirical data shows the successful model construction. However, ANN outperforms the SVR approach and gives more accurate results.

Notes

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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Azar Alizadeh
    • 1
  • Fatemeh Mosalanezhad
    • 2
  • Abdolkarim Afroozeh
    • 3
  • Elnaz Akbari
    • 4
    • 5
    Email author
  • Zolkafle Buntat
    • 6
  1. 1.School of EngineeringUniversity of CaliforniaMercedUSA
  2. 2.Department of ChemistryJahrom Branch, Islamic Azad UniversityJahromIran
  3. 3.Department of EngineeringUniversity of LarestanLarIran
  4. 4.Department for Management of Science and Technology DevelopmentTon Duc Thang UniversityHo Chi Minh CityVietnam
  5. 5.Faculty of Electrical and Electronics EngineeringTon Duc Thang UniversityHo Chi Minh CityVietnam
  6. 6.Institute of High Voltage and High Current, Faculty of Electrical EngineeringUniversiti Teknologi MalaysiaJohor BahruMalaysia

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