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Improving the Chemical Selectivity of an Electronic Nose to TNT, DNT and RDX Using Machine Learning
We used a 16-channel e-nose demonstrator based on micro-capacitive sensors with functionalized surfaces to measure the response of 30 different sensors to the vapours from 11 different substances, including the explosives 1,3,5-trinitro-1,3,5-triazinane (RDX), 1-methyl-2,4-dinitrobenzene (DNT) and 2...
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| Veröffentlicht in: | Sensors (Basel) |
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| Hauptverfasser: | , , , , , , , , , , |
| Format: | Artigo |
| Sprache: | Inglês |
| Veröffentlicht: |
MDPI
2019
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| Schlagworte: | |
| Online Zugang: | https://ncbi.nlm.nih.gov/pmc/articles/PMC6928873/ https://ncbi.nlm.nih.gov/pubmed/31783711 https://ncbi.nlm.nih.govhttp://dx.doi.org/10.3390/s19235207 |
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