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Updated: Feb 3, 2026

Simultaneous Synthesis of Single-walled Carbon Nanotubes and Graphene in a Magnetically-enhanced Arc Plasma
Published on: February 2, 2012
Enhance the Discrimination Precision of Graphene Gas Sensors with a Hidden Markov Model
Huixian Ye1,2,3, Eric C Nallon3,4, Vincent P Schnee4
1Shanghai Advanced Research Institute , Chinese Academy of Sciences , Shanghai 201210 , China.
Abstract:
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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