Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip
Summary: Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace ald...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-10-01
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| Series: | iScience |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004223018989 |
| _version_ | 1797647801322045440 |
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| author | Haiting Cao Huayi Shi Jie Tang Yanan Xu Yufan Ling Xing Lu Yang Yang Xiaojie Zhang Houyu Wang |
| author_facet | Haiting Cao Huayi Shi Jie Tang Yanan Xu Yufan Ling Xing Lu Yang Yang Xiaojie Zhang Houyu Wang |
| author_sort | Haiting Cao |
| collection | DOAJ |
| description | Summary: Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace aldehydes at the ppt level. For rapid preconcentration, we demonstrate that the nozzle-like microfluidic concentrator allows the enrichment of rare gaseous analytes by five-fold in only 0.01 ms. The enriched gas is subsequently captured and detected by an integrated silicon-based SERS chip, which is made of zeolitic imidazolate framework-8 coated silver nanoparticles grown in situ on a silicon wafer. After SERS measurement, a fully connected deep neural network is built to extract faint features in the spectral dataset and discriminate volatile organic compound classes. We demonstrate that six kinds of gaseous aldehydes at 100 ppt could be detected and classified with an identification accuracy of ∼80.9% by using this platform. |
| first_indexed | 2024-03-11T15:22:51Z |
| format | Article |
| id | doaj.art-16f9967895524c96970d890fa832f2be |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-03-11T15:22:51Z |
| publishDate | 2023-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-16f9967895524c96970d890fa832f2be2023-10-28T05:08:38ZengElsevieriScience2589-00422023-10-012610107821Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chipHaiting Cao0Huayi Shi1Jie Tang2Yanan Xu3Yufan Ling4Xing Lu5Yang Yang6Xiaojie Zhang7Houyu Wang8Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, ChinaSuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, ChinaSuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, ChinaSuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, ChinaState Key Laboratory of Radiation Medicine and Protection, School of Radiation Medicine and Protection, Collaborative Innovation Center of Radiological Medicine of Jiangsu Higher Education Institutions, Soochow University, 199 Renai Road, Suzhou 215123, ChinaSuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, ChinaDepartment of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China; Corresponding authorDepartment of Experimental Center, Medical College of Soochow University, Suzhou, Jiangsu 215123, China; Corresponding authorSuzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China; Corresponding authorSummary: Current gaseous sensors hardly discriminate trace volatile organic compounds at the ppt level. Herein, we present an integrated platform for simultaneously enabling rapid preconcentration, reliable surface-enhanced Raman scattering, (SERS) detection and automatic identification of trace aldehydes at the ppt level. For rapid preconcentration, we demonstrate that the nozzle-like microfluidic concentrator allows the enrichment of rare gaseous analytes by five-fold in only 0.01 ms. The enriched gas is subsequently captured and detected by an integrated silicon-based SERS chip, which is made of zeolitic imidazolate framework-8 coated silver nanoparticles grown in situ on a silicon wafer. After SERS measurement, a fully connected deep neural network is built to extract faint features in the spectral dataset and discriminate volatile organic compound classes. We demonstrate that six kinds of gaseous aldehydes at 100 ppt could be detected and classified with an identification accuracy of ∼80.9% by using this platform.http://www.sciencedirect.com/science/article/pii/S2589004223018989FluidicsNanoscienceNanotechnology |
| spellingShingle | Haiting Cao Huayi Shi Jie Tang Yanan Xu Yufan Ling Xing Lu Yang Yang Xiaojie Zhang Houyu Wang Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip iScience Fluidics Nanoscience Nanotechnology |
| title | Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip |
| title_full | Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip |
| title_fullStr | Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip |
| title_full_unstemmed | Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip |
| title_short | Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip |
| title_sort | ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon sers artificial intelligence chip |
| topic | Fluidics Nanoscience Nanotechnology |
| url | http://www.sciencedirect.com/science/article/pii/S2589004223018989 |
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