Cargando…
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...
| Autores principales: | , , , , , , , , , , |
|---|---|
| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
MDPI
2019
|
| Materias: | |
| 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 |