Ionogels Based on a Single Ionic Liquid for Electronic Nose Application
| Autor: | Susana I. C. J. Palma, Gonçalo Santos, Evelyn P. Cervantes, Wellington B. Gonçalves, Ana C. A. Roque, Jonas Gruber, Rosamaria Wu Chia Li, Ana Carolina Pádua |
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| Přispěvatelé: | UCIBIO - Applied Molecular Biosciences Unit, DQ - Departamento de Química |
| Rok vydání: | 2022 |
| Předmět: |
electronic nose
volatile organic compound Materials science food.ingredient Composite QD415-436 02 engineering and technology Ionic liquid 010402 general chemistry Biochemistry 01 natural sciences Gelatin Electronic nose gas sensor Analytical Chemistry Matrix (chemical analysis) chemistry.chemical_compound food COMPOSTOS ORGÂNICOS Volatile organic compound composite Ionogel Physical and Theoretical Chemistry ionic liquid chemistry.chemical_classification Doping 021001 nanoscience & nanotechnology 0104 chemical sciences ionogel chemistry Chemical engineering Gas sensor 0210 nano-technology Selectivity Iron oxide nanoparticles |
| Zdroj: | Repositório Institucional da USP (Biblioteca Digital da Produção Intelectual) Universidade de São Paulo (USP) instacron:USP Chemosensors Volume 9 Issue 8 Chemosensors, Vol 9, Iss 201, p 201 (2021) |
| ISSN: | 2227-9040 |
| Popis: | 165186/2015-1 307501/2019-1 424027/2018-6 grant reference SCENT-ERC-2014-STG-639123 (2015-2020) UIDB/04378/2020 POCI-01-0145-FEDER-007728 Ionogel are versatile materials, as they present the electrical properties of ionic liquids and also dimensional stability, since they are trapped in a solid matrix, allowing application in electronic devices such as gas sensors and electronic noses. In this work, ionogels were designed to act as a sensitive layer for the detection of volatiles in a custom-made electronic nose. Ionogels composed of gelatin and a single imidazolium ionic liquid were doped with bare and functionalized iron oxide nanoparticles, producing ionogels with adjustable target selectivity. After exposing an array of four ionogels to 12 distinct volatile organic compounds, the collected signals were analyzed by principal component analysis (PCA) and by several supervised classification methods, in order to assess the ability of the electronic nose to distinguish different volatiles, which showed accuracy above 98%. publishersversion published |
| Databáze: | OpenAIRE |
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