Quantum of selectivity testing: detection of isomers and close homologs by AZO based E-nose without a prior training: detection of isomers and close homologs using an AZO based e-nose without a prior training

Boris V. Goikhman, Fedor S. Fedorov, Nikolay P. Simonenko, Tatiana L. Simonenko, Nikita A. Fisenko, Tatiana S. Dubinina, George Ovchinnikov, Anna V. Lantsberg, Alexey Lipatov, Elizaveta P. Simonenko, Albert G. Nasibulin

Результат исследований: Вклад в журналСтатьярецензирование

1 Цитирования (Scopus)

Аннотация

Tracing 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 the 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 using the e-nose in artificial space under machine learning algorithms. Moreover, the AZO based e-nose demonstrates a remarkable, up to 0.87, chemoresistive response to alcohol vapors, 0.9 ppm, in the mixture with air at 300 °C with a detection limit down to sub-ppb level. This opens a new avenue for the development of self-learning gas analytical systems, which might recognize new analytes whose profiles are not yet stored in their library.

Язык оригиналаАнглийский
Страницы (с-по)8413-8423
Число страниц11
ЖурналJournal of Materials Chemistry A
Том10
Номер выпуска15
DOI
СостояниеОпубликовано - 2022

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Подробные сведения о темах исследования «Quantum of selectivity testing: detection of isomers and close homologs by AZO based E-nose without a prior training: detection of isomers and close homologs using an AZO based e-nose without a prior training». Вместе они формируют уникальный семантический отпечаток (fingerprint).

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