Enhance the Discrimination Precision of Graphene Gas Sensors with a Hidden Markov Model
| Autor: | Songlin Feng, Chen Shi, Huixian Ye, Jinxia Xu, Kai Jiang, Qiliang Li, Eric C. Nallon, Vincent P. Schnee, Hui Wang |
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| Rok vydání: | 2018 |
| Předmět: |
Graphene
Chemistry 010401 analytical chemistry 02 engineering and technology 021001 nanoscience & nanotechnology Mixture model 01 natural sciences 0104 chemical sciences Analytical Chemistry law.invention Characterization (materials science) Feature (computer vision) law Key (cryptography) Electronic engineering Electronics Photolithography 0210 nano-technology Hidden Markov model |
| Zdroj: | Analytical chemistry. 90(22) |
| ISSN: | 1520-6882 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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