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dc.contributor.author Kim, Sohee -
dc.contributor.author Jang, Hyun Woo -
dc.contributor.author Kwon, Hyeokjin -
dc.contributor.author Heo, Su Jin -
dc.contributor.author Pyo, Goeun -
dc.contributor.author Kim, Dong Su -
dc.contributor.author Chae, Ji Won -
dc.contributor.author Jang, Jae Eun -
dc.date.accessioned 2023-12-26T18:43:03Z -
dc.date.available 2023-12-26T18:43:03Z -
dc.date.created 2022-02-15 -
dc.date.issued 2021-11-03 -
dc.identifier.isbn 9781728195018 -
dc.identifier.issn 1930-0395 -
dc.identifier.uri http://hdl.handle.net/20.500.11750/46888 -
dc.description.abstract High selectivity of the sensor is one of the most important parameters for the electronic nose (E-nose) or gas sensor. However, many sensors have shown poor selectivity, even though some had high sensitivity. To solve this issue, we suggested a multi-sensor array concept, which consisted of thin-film transistors (TFTs) with various polymer selectors. By using TFT as a base sensor structure, sensitivity was significantly enhanced, and by adopting a polymer as the second dielectric layer, there was a remarkable improvement in selectivity. Some polymers showed unique selectivity to a specific odorant, whereas others had a moderate reaction to various odorants. Instead of common one-to-one matching between a sensor and an odorant, we used multi-output analysis using a dimensional bar chart. Eight different polymers TFTs made a specific chart pattern for four different odorants. Therefore, this sensor array and signal process concept can apply to the e-nose system, which can classify many odorants like a human simulator. © 2021 IEEE. -
dc.language English -
dc.publisher IEEE Sensors Council -
dc.title Detection of Odorant using TFT multi-array with Various Polymers -
dc.type Conference Paper -
dc.identifier.doi 10.1109/SENSORS47087.2021.9639635 -
dc.identifier.scopusid 2-s2.0-85123610319 -
dc.identifier.bibliographicCitation IEEE SENSORS 2021, pp.1 - 4 -
dc.identifier.url https://2021.ieee-sensorsconference.org/wp-content/uploads/sites/16/2021/10/sensors-2021_program_v10-1-2.pdf -
dc.citation.conferencePlace AT -
dc.citation.conferencePlace Sydney -
dc.citation.endPage 4 -
dc.citation.startPage 1 -
dc.citation.title IEEE SENSORS 2021 -
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Department of Electrical Engineering and Computer Science Advanced Electronic Devices Research Group(AEDRG) - Jang Lab. 2. Conference Papers

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