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.
CITATION STYLE
Zhang, N., Liu, Z., & Wang, J. (2022). Machine-Learning-Enabled Design and Manipulation of a Microfluidic Concentration Gradient Generator. Micromachines, 13(11). https://doi.org/10.3390/mi13111810
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