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Visualization of the Gas Spatial Distribution of the Odor Source by SERS Gas Sensor
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- Lin Chen
- Department of Electronics, Graduate School of Information Science and Electrical Engineering Kyushu University, 744, Motooka, Nishi‐ku Fukuoka 819‐0395 Japan
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- Hao Guo
- Department of Electronics, Graduate School of Information Science and Electrical Engineering Kyushu University, 744, Motooka, Nishi‐ku Fukuoka 819‐0395 Japan
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- Takuya Matsuo
- Department of Electronics, Graduate School of Information Science and Electrical Engineering Kyushu University, 744, Motooka, Nishi‐ku Fukuoka 819‐0395 Japan
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- Fumihiro Sassa
- Department of Electronics, Graduate School of Information Science and Electrical Engineering Kyushu University, 744, Motooka, Nishi‐ku Fukuoka 819‐0395 Japan
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- Kenshi Hayashi
- Department of Electronics, Graduate School of Information Science and Electrical Engineering Kyushu University, 744, Motooka, Nishi‐ku Fukuoka 819‐0395 Japan
Bibliographic Information
- Published
- 2024-01-03
- Resource Type
- journal article
- Rights Information
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- http://onlinelibrary.wiley.com/termsAndConditions#vor
- DOI
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- 10.1002/tee.23988
- Publisher
- Wiley
Search this article
Description
<jats:p>Surface‐Enhanced Raman Scattering (SERS) technique is used to fabricate the gas sensor, which is developed for the visualization of the spatial distribution of gas evaporating from odor source. The 3 × 3 SERS sensor array is put on the odor source and then scanned by the home‐made detection system to obtain the Raman spectra of the odor. The intensities of the characteristic peaks from the collected spectra are used to draw a heatmap graph. After the heatmap graph is processed by the noise reduction, the localization of the odor source can be clearly identified through the resulting graph. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.</jats:p>
Journal
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- IEEJ Transactions on Electrical and Electronic Engineering
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IEEJ Transactions on Electrical and Electronic Engineering 19 (5), 876-881, 2024-01-03
Wiley
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Details 詳細情報について
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- CRID
- 1360865816782784640
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- ISSN
- 19314981
- 19314973
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- Article Type
- journal article
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- Data Source
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- Crossref
- KAKEN
- OpenAIRE
