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Using Bayes' Rule for Analysis of Microfluidic Particle and Cluster Sorting
Elham Akbari1,2, Esra Yilmaz1,2, Christelle N Prinz1,2,3
1Department of Physics, Division of Solid State Physics, Lund University, P.O. Box 118, SE-221 00 Lund, Sweden.
Micromachines
|May 4, 2026
Summary
This study introduces a new method using Bayes
Area of Science:
- Microfluidics
- Biotechnology
- Particle Separation
Background:
- Deterministic lateral displacement (DLD) devices are crucial for microfluidic particle sorting.
- Current evaluation methods based on outlet size distributions are insufficient.
- A more accurate performance measure is the probability of particle routing to specific outlets.
Purpose of the Study:
- To develop a probabilistic framework for evaluating microfluidic sorting devices.
- To infer size-dependent routing probabilities using Bayes' rule.
- To establish a robust method for benchmarking DLD and related devices.
Main Methods:
- Applied Bayes' rule to infer routing probabilities from experimental data.
- Measured inlet and outlet particle size distributions and outlet subpopulations.
- Utilized a DLD array for separating microspheres and microsphere clusters.
Main Results:
- Determined size-dependent probabilities for particle routing to each outlet.
- Defined a probabilistic critical size (DC) for trajectory determination.
- Calculated key performance metrics including purity and yield, demonstrating high-quality separations.
Conclusions:
- Probabilistic routing analysis offers a superior method for evaluating microfluidic sorters.
- This framework moves beyond traditional outlet-based analyses for more accurate device benchmarking.
- The approach is general and robust for various microfluidic sorting applications.
