Advances in Integration, Wearable Applications, and Artificial Intelligence of Biomedical Microfluidics Systems
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Published:2023-04-29
Issue:5
Volume:14
Page:972
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ISSN:2072-666X
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Container-title:Micromachines
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language:en
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Short-container-title:Micromachines
Author:
Ma Xingfeng12, Guo Gang2, Wu Xuanye23, Wu Qiang4, Liu Fangfang3, Zhang Hua4, Shi Nan35, Guan Yimin24
Affiliation:
1. School of Communication and Information Engineering, Shanghai University, Shanghai 200000, China 2. Department of Microelectronics, Shanghai University, Shanghai 200000, China 3. Shanghai Industrial μTechnology Research Institute, Shanghai 200000, China 4. Shanghai Aure Technology Limited Company, Shanghai 200000, China 5. Institute of Translational Medicine, Shanghai University, Shanghai 200000, China
Abstract
Microfluidics attracts much attention due to its multiple advantages such as high throughput, rapid analysis, low sample volume, and high sensitivity. Microfluidics has profoundly influenced many fields including chemistry, biology, medicine, information technology, and other disciplines. However, some stumbling stones (miniaturization, integration, and intelligence) strain the development of industrialization and commercialization of microchips. The miniaturization of microfluidics means fewer samples and reagents, shorter times to results, and less footprint space consumption, enabling a high throughput and parallelism of sample analysis. Additionally, micro-size channels tend to produce laminar flow, which probably permits some creative applications that are not accessible to traditional fluid-processing platforms. The reasonable integration of biomedical/physical biosensors, semiconductor microelectronics, communications, and other cutting-edge technologies should greatly expand the applications of current microfluidic devices and help develop the next generation of lab-on-a-chip (LOC). At the same time, the evolution of artificial intelligence also gives another strong impetus to the rapid development of microfluidics. Biomedical applications based on microfluidics normally bring a large amount of complex data, so it is a big challenge for researchers and technicians to analyze those huge and complicated data accurately and quickly. To address this problem, machine learning is viewed as an indispensable and powerful tool in processing the data collected from micro-devices. In this review, we mainly focus on discussing the integration, miniaturization, portability, and intelligence of microfluidics technology.
Funder
National Natural Science Foundation of the People’s Republic of China Ministry of Science and Technology of the People’s Republic of China Ministry of Industry and Information Technology of the People’s Republic of China
Subject
Electrical and Electronic Engineering,Mechanical Engineering,Control and Systems Engineering
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