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Yang N, Song W, Xiao Y, Xia M, Xiao L, Li T, Zhang Z, Yu N, Zhang X. Minimum Minutes Machine-Learning Microfluidic Microbe Monitoring Method (M7). ACS NANO 2024; 18:4862-4870. [PMID: 38231040 DOI: 10.1021/acsnano.3c09733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/18/2024]
Abstract
Frequent outbreaks of viral diseases have brought substantial negative impacts on society and the economy, and they are very difficult to detect, as the concentration of viral aerosols in the air is low and the composition is complex. The traditional detection method is manually collection and re-detection, being cumbersome and time-consuming. Here we propose a virus aerosol detection method based on microfluidic inertial separation and spectroscopic analysis technology to rapidly and accurately detect aerosol particles in the air. The microfluidic chip is designed based on the principles of inertial separation and laminar flow characteristics, resulting in an average separation efficiency of 95.99% for 2 μm particles. We build a microfluidic chip composite spectrometer detection platform to capture the spectral information on aerosol particles dynamically. By employing machine-learning techniques, we can accurately classify different types of aerosol particles. The entire experiment took less than 30 min as compared with hours by PCR detection. Furthermore, our model achieves an accuracy of 97.87% in identifying virus aerosols, which is comparable to the results obtained from PCR detection.
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Affiliation(s)
- Ning Yang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Wei Song
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Yi Xiao
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Muming Xia
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Lizhi Xiao
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
| | - Tongge Li
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Zhaoyuan Zhang
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Ni Yu
- School of Electrical and Information Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
| | - Xingcai Zhang
- School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States
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