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iScience

Title:		Ultrasensitive discrimination of volatile organic compounds using a microfluidic silicon SERS artificial intelligence chip
Author(s):		Cao H; Shi H; Tang J; Xu Y; Ling Y; Lu X; Yang Y; Zhang X; Wang H;
Address:		"Suzhou Key Laboratory of Nanotechnology and Biomedicine, Institute of Functional Nano & Soft Materials (FUNSOM), Soochow University, Suzhou, Jiangsu 215123, China. State 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, China. Department of Thoracic Surgery, Shanghai Pulmonary Hospital, School of Medicine, Tongji University, Shanghai 200433, China. Department of Experimental Center, Medical College of Soochow University, Suzhou, Jiangsu 215123, China"
Journal Title:		iScience
Year:		2023
Volume:		20230902
Issue:		10
Page Number:		107821 -
DOI:		10.1016/j.isci.2023.107821
ISSN/ISBN:		2589-0042 (Electronic) 2589-0042 (Linking)
Abstract:		"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 approximately 80.9% by using this platform"
Keywords:		Fluidics Nanoscience Nanotechnology;
Notes:		"PubMed-not-MEDLINECao, Haiting Shi, Huayi Tang, Jie Xu, Yanan Ling, Yufan Lu, Xing Yang, Yang Zhang, Xiaojie Wang, Houyu eng 2023/09/21 iScience. 2023 Sep 2; 26(10):107821. doi: 10.1016/j.isci.2023.107821. eCollection 2023 Oct 20"