DC Field | Value | Language |
---|---|---|
dc.contributor.author | Lee, Kichul | ko |
dc.contributor.author | Cho, Incheol | ko |
dc.contributor.author | Kang, Mingu | ko |
dc.contributor.author | Jeong, Jaeseok | ko |
dc.contributor.author | Choi, Minho | ko |
dc.contributor.author | Woo, Kie Young | ko |
dc.contributor.author | Yoon, Kuk-Jin | ko |
dc.contributor.author | Cho, Yong-Hoon | ko |
dc.contributor.author | Park, Inkyu | ko |
dc.date.accessioned | 2023-01-28T04:01:34Z | - |
dc.date.available | 2023-01-28T04:01:34Z | - |
dc.date.created | 2023-01-16 | - |
dc.date.created | 2023-01-16 | - |
dc.date.created | 2023-01-16 | - |
dc.date.issued | 2023-01 | - |
dc.identifier.citation | ACS NANO, v.17, no.1, pp.539 - 551 | - |
dc.identifier.issn | 1936-0851 | - |
dc.identifier.uri | http://hdl.handle.net/10203/304775 | - |
dc.description.abstract | As interests in air quality monitoring related to environmental pollution and industrial safety increase, demands for gas sensors are rapidly increasing. Among various gas sensor types, the semiconductor metal oxide (SMO)-type sensor has advantages of high sensitivity, low cost, mass production, and small size but suffers from poor selectivity. To solve this problem, electronic nose (e-nose) systems using a gas sensor array and pattern recognition are widely used. However, as the number of sensors in the e-nose system increases, total power consumption also increases. In this study, an ultra-low-power e-nose system was developed using ultraviolet (UV) micro-LED (mu LED) gas sensors and a convolutional neural network (CNN). A monolithic photoactivated gas sensor was developed by depositing a nanocolumnar In2O3 film coated with plasmonic metal nanoparticles (NPs) directly on the mu LED. The e-nose system consists of two different mu LED sensors with silver and gold NP coating, and the total power consumption was measured as 0.38 mW, which is one-hundredth of the conventional heater-based e-nose system. Responses to various target gases measured by multi-mu LED gas sensors were analyzed by pattern recognition and used as the training data for the CNN algorithm. As a result, a real-time, highly selective e-nose system with a gas classification accuracy of 99.32% and a gas concentration regression error (mean absolute) of 13.82% for five different gases (air, ethanol, NO2, acetone, methanol) was developed. The mu LED-based e-nose system can be stably battery-driven for a long period and is expected to be widely used in environmental internet of things (IoT) applications. | - |
dc.language | English | - |
dc.publisher | AMER CHEMICAL SOC | - |
dc.title | Ultra-Low-Power E-Nose System Based on Multi-Micro-LED-Integrated, Nanostructured Gas Sensors and Deep Learning | - |
dc.type | Article | - |
dc.identifier.wosid | 000903320100001 | - |
dc.identifier.scopusid | 2-s2.0-85144430100 | - |
dc.type.rims | ART | - |
dc.citation.volume | 17 | - |
dc.citation.issue | 1 | - |
dc.citation.beginningpage | 539 | - |
dc.citation.endingpage | 551 | - |
dc.citation.publicationname | ACS NANO | - |
dc.identifier.doi | 10.1021/acsnano.2c09314 | - |
dc.contributor.localauthor | Yoon, Kuk-Jin | - |
dc.contributor.localauthor | Cho, Yong-Hoon | - |
dc.contributor.localauthor | Park, Inkyu | - |
dc.description.isOpenAccess | N | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | micro-LED | - |
dc.subject.keywordAuthor | monolithic photoactivated gas sensor | - |
dc.subject.keywordAuthor | ultra-low-power | - |
dc.subject.keywordAuthor | localized surface plasmon resonance | - |
dc.subject.keywordAuthor | deep learning algorithm | - |
dc.subject.keywordAuthor | electronic nose | - |
dc.subject.keywordPlus | ROOM-TEMPERATURE | - |
dc.subject.keywordPlus | OXIDE | - |
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