Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator

Microfluidics concentration gradient generators have been widely applied in chemical and biological fields. However, the current gradient generators still have some limitations. In this work, we presented a microfluidic concentration gradient generator with its corresponding manipulation process to...

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Vydáno v:Micromachines (Basel) Ročník 13; číslo 11; s. 1810
Hlavní autoři: Zhang, Naiyin, Liu, Zhenya, Wang, Junchao
Médium: Journal Article
Jazyk:angličtina
Vydáno: Basel MDPI AG 24.10.2022
MDPI
Témata:
Algorithms
Artificial neural networks
CAD
Cancer
Computer aided design
Concentration gradient
Datasets
Design
Design and construction
design automation
Finite element method
Generators
Interpolation
interpolation algorithm
Machine learning
Microfluidics
Nanoparticles
Neural networks
Physics
China
ISSN:2072-666X, 2072-666X
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Shrnutí:Microfluidics concentration gradient generators have been widely applied in chemical and biological fields. However, the current gradient generators still have some limitations. In this work, we presented a microfluidic concentration gradient generator with its corresponding manipulation process to generate an arbitrary concentration gradient. Machine-learning techniques and interpolation algorithms were implemented to help researchers instantly analyze the current concentration profile of the gradient generator with different inlet configurations. The proposed method has a 93.71% accuracy rate with a 300× acceleration effect compared to the conventional finite element analysis. In addition, our method shows the potential application of the design automation and computer-aided design of microfluidics by leveraging both artificial neural networks and computer science algorithms.
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These authors contributed equally to this work.
ISSN:2072-666X
2072-666X
DOI:10.3390/mi13111810