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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Detalles Bibliográficos
Autores principales: Gradišek, Anton, van Midden, Marion, Koterle, Matija, Prezelj, Vid, Strle, Drago, Štefane, Bogdan, Brodnik, Helena, Trifkovič, Mario, Kvasić, Ivan, Zupanič, Erik, Muševič, Igor
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928873/
https://www.ncbi.nlm.nih.gov/pubmed/31783711
http://dx.doi.org/10.3390/s19235207

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6928873/
https://www.ncbi.nlm.nih.gov/pubmed/31783711
http://dx.doi.org/10.3390/s19235207

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