Enhance the Discrimination Precision of Graphene Gas Sensors with a Hidden Markov Model.

Anal Chem

Department of Electrical and Computer Engineering , George Mason University, Fairfax , Virginia 22030 , United States.

Published: November 2018

Sensors are the key element to enable smart electronics and will play an important role in the emerging big data era. In this work, we reported an experimental study and a data-analytical characterization method to enhance the precision of discriminating chemically and structurally similar gases. Graphene sensors were fabricated by conventional photolithography and measured with feature analysis against different chemicals. A new hidden Markov model assisted with frequency spectral analysis, and the Gaussian mixture model (K-GMM-HMM) is developed to discriminate similar gases. The results indicated that the new method achieved a high prediction accuracy of 94%, 27% higher than the maximum value obtained by the conventional methods or other feature transient analysis methods. This study indicated that graphene gas sensors with the new K-GMM-HMM analysis are very attractive for chemical discrimination used in future smart electronics.

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http://dx.doi.org/10.1021/acs.analchem.8b04386DOI Listing

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