Using Bayes’ Rule for Analysis of Microfluidic Particle and Cluster Sorting
(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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- author
- Akbari, Elham
LU
; Yilmaz, Esra
LU
; Prinz, Christelle N.
LU
; Beech, Jason P.
LU
and Tegenfeldt, Jonas O.
LU
- organization
- publishing date
- 2026-04
- 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}},
}