|
ИСТИНА |
Войти в систему Регистрация |
ИСТИНА ИНХС РАН |
||
Quantum of selectivity testing: detection of isomers and close homologs by AZO based E-nose without a prior trainingстатья
Статья опубликована в высокорейтинговом журнале
Статья опубликована в журнале из списка Web of Science и/или Scopus- Авторы: Goikhman Boris, Fedorov Fedor, Simonenko Nikolay P., Simonenko Tatiana L., Fisenko Nikita, Dubinina Tatiana, Ovchinnikov Georgii, Lantsberg Anna, Lipatov Alexey, Simonenko Elizaveta P., Nasibulin Albert G.
- Журнал: Journal of Materials Chemistry A
- Год издания: 2022
- Издательство: Royal Society of Chemistry
- Местоположение издательства: United Kingdom
- DOI: 10.1039/d1ta10589b
- Аннотация: Tracing of the chemical composition of the surrounding environment appeals to the design of highly sensitive and selective gas sensors. Primarily driven by IoT, miniaturized multisensor systems, like e-noses, are considered to address both selectivity and sensitivity issues. Although e-noses might enable discrimination between close homologs and isomers, they are required to be “trained”, i.e. to project analyte-related signals into artificial space, prior to their in-field applications. In this study, using programmed co-precipitation method, we synthesized aluminum-doped zinc oxide (AZO) and employed it as a sensing material in an e-nose to examine the sensing performance towards close C1-C5 alcohol homologs and isomers, e.g. 1-propanol and 2-propanol, 1-butanol and isobutanol in the frame of the multisensor paradigm. For the first time we demonstrated selective recognition of the alcohol vapors without prior training of the e-nose. This was realized by matching projections of the known analytes’ “fingerprints”, used to build a chemical space, with the projections of analyte-related signals acquired by e-nose in artificial space under machine learning algorithms. Moreover, the AZO based e-nose demonstrates a remarkable, up to 0.87, chemoresistive response, 0.9 ppm, in the air at 300 °C with a detection limit down to sub-ppb level. This opens a new avenue for development of self-learning gas analytical systems, which might recognize new analytes with which they have not been dealt with and those profile is not yet stored in their library of the sensed analytes.
- Добавил в систему: Фисенко Никита Александрович