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Using Bayes’ Rule for Analysis of Microfluidic Particle and Cluster Sorting

Akbari, Elham LU ; Yilmaz, Esra LU orcid ; Prinz, Christelle N. LU ; Beech, Jason P. LU and Tegenfeldt, Jonas O. LU orcid (2026) In Micromachines 17(4).
Abstract

Deterministic lateral displacement (DLD) and related microfluidic sorting devices are typically evaluated based on the size distributions of particles collected at each outlet, even though the more relevant measure of performance is the probability that a particle of a given size ends up in a specific outlet. Here, we use Bayes’ rule to infer these size-dependent routing probabilities from experimentally accessible measurements of outlet size distributions, inlet size distributions, and outlet subpopulations. Using a DLD array designed to separate microspheres and microsphere clusters, we determine the probabilities that particles of different sizes are directed to each outlet and define a probabilistic critical size ((Formula... (More)

Deterministic lateral displacement (DLD) and related microfluidic sorting devices are typically evaluated based on the size distributions of particles collected at each outlet, even though the more relevant measure of performance is the probability that a particle of a given size ends up in a specific outlet. Here, we use Bayes’ rule to infer these size-dependent routing probabilities from experimentally accessible measurements of outlet size distributions, inlet size distributions, and outlet subpopulations. Using a DLD array designed to separate microspheres and microsphere clusters, we determine the probabilities that particles of different sizes are directed to each outlet and define a probabilistic critical size ((Formula presented.)) at which particles are equally likely to follow a zigzag and a displacement trajectory. Based on this, we calculate key performance metrics, purity, and yield. Our results demonstrate high-quality separations and show that routing probabilities provide a general and robust framework for benchmarking microfluidic sorting devices beyond traditional outlet-based analyses.

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Please use this url to cite or link to this publication:
author
; ; ; and
organization
publishing date
type
Contribution to journal
publication status
published
subject
keywords
Bayes’ rule, deterministic lateral displacement, microfluidics, sorting
in
Micromachines
volume
17
issue
4
article number
396
publisher
MDPI AG
external identifiers
  • scopus:105037060632
  • pmid:42076173
ISSN
2072-666X
DOI
10.3390/mi17040396
language
English
LU publication?
yes
id
3beaedf5-5709-434f-ab4d-180361878052
date added to LUP
2026-06-01 14:38:52
date last changed
2026-06-02 03:00:06
@article{3beaedf5-5709-434f-ab4d-180361878052,
  abstract     = {{<p>Deterministic lateral displacement (DLD) and related microfluidic sorting devices are typically evaluated based on the size distributions of particles collected at each outlet, even though the more relevant measure of performance is the probability that a particle of a given size ends up in a specific outlet. Here, we use Bayes’ rule to infer these size-dependent routing probabilities from experimentally accessible measurements of outlet size distributions, inlet size distributions, and outlet subpopulations. Using a DLD array designed to separate microspheres and microsphere clusters, we determine the probabilities that particles of different sizes are directed to each outlet and define a probabilistic critical size ((Formula presented.)) at which particles are equally likely to follow a zigzag and a displacement trajectory. Based on this, we calculate key performance metrics, purity, and yield. Our results demonstrate high-quality separations and show that routing probabilities provide a general and robust framework for benchmarking microfluidic sorting devices beyond traditional outlet-based analyses.</p>}},
  author       = {{Akbari, Elham and Yilmaz, Esra and Prinz, Christelle N. and Beech, Jason P. and Tegenfeldt, Jonas O.}},
  issn         = {{2072-666X}},
  keywords     = {{Bayes’ rule; deterministic lateral displacement; microfluidics; sorting}},
  language     = {{eng}},
  number       = {{4}},
  publisher    = {{MDPI AG}},
  series       = {{Micromachines}},
  title        = {{Using Bayes’ Rule for Analysis of Microfluidic Particle and Cluster Sorting}},
  url          = {{http://dx.doi.org/10.3390/mi17040396}},
  doi          = {{10.3390/mi17040396}},
  volume       = {{17}},
  year         = {{2026}},
}