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Machine Learning-Driven Optimization of Viscoelastic Microfluidic Particle Separation
Qing Lu1,2,3, Zhuoran Zhao3, Zhinan Zhang1,2
1School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Analytical Chemistry
|April 21, 2026
Summary
Machine learning accelerates optimization for viscoelastic microfluidics (VEM) particle isolation. This data-driven approach streamlines complex dynamics, enhancing VEM
Area of Science:
- Biophysics
- Microfluidics
- Machine Learning
Background:
- Viscoelastic microfluidics (VEM) enables advanced biological particle isolation.
- Complex fluid dynamics in VEM require extensive empirical optimization, limiting its application.
Purpose of the Study:
- To develop a machine learning (ML)-driven strategy for rapid optimization of VEM operating conditions.
- To transition VEM from an experience-dependent to a data-driven process for enhanced scalability.
Main Methods:
- An experimentally calibrated theoretical model generated particle motion data.
- A Random Forest algorithm was trained on this data for parameter mapping.
- Pareto optimization identified trade-offs for tailored parameter determination.
Main Results:
- Established a robust bidirectional mapping between dynamic parameters and input variables.
- Successfully determined optimal VEM parameters for specific separation needs.
- Demonstrated a data-driven paradigm for VEM optimization.
Conclusions:
- The ML-driven strategy significantly accelerates VEM optimization.
- This approach enhances the scalability and clinical utility of VEM-based isolation.
- The framework enables efficient, tailored particle separation in microfluidic devices.
