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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.

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Summary
This summary is machine-generated.

This study introduces a new method using Bayes

Keywords:
Bayes’ ruledeterministic lateral displacementmicrofluidicssorting

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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.