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

• Published in: Journal of materials chemistry. A, Materials for energy and sustainability Vol. 10; no. 15; pp. 8413 - 8423
• Main Authors: Goikhman, Boris V., Fedorov, Fedor S., Simonenko, Nikolay P., Simonenko, Tatiana L., Fisenko, Nikita A., Dubinina, Tatiana S., Ovchinnikov, George, Lantsberg, Anna V., Lipatov, Alexey, Simonenko, Elizaveta P., Nasibulin, Albert G.
• Format: Journal Article
• Language: English
• Published: Cambridge Royal Society of Chemistry 12.04.2022
• Subjects: 1-propanol; air; Alcohol; Algorithms; Aluminum; Butanol; Chemical composition; chemical species; coprecipitation; detection limit; electronic nose; Electronic noses; Gas sensors; Homology; Isobutanol; Isomers; isopropyl alcohol; Machine learning; Propanol; Selectivity; Training; Vapors; Zinc oxide
• ISSN: 2050-7488; 2050-7496; 2050-7496
• DOI: 10.1039/D1TA10589B

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.