• Enhancing Microparticle Separa...

Treffer: Enhancing Microparticle Separation Efficiency in Acoustofluidic Chips via Machine Learning and Numerical Modeling.

Title:
Enhancing Microparticle Separation Efficiency in Acoustofluidic Chips via Machine Learning and Numerical Modeling.
Authors:
Klymkovych T; Department of Semiconductor and Optoelectronic Devices, Lodz University of Technology, 116 Zeromskiego, 90-924 Łódź, Poland.; Department of Computer Design Systems, Lviv Polytechnic National University, 5 Mytropolyta Andreia, 79000 Lviv, Ukraine., Bokla N; Department of Semiconductor and Optoelectronic Devices, Lodz University of Technology, 116 Zeromskiego, 90-924 Łódź, Poland.; Department of Computer Design Systems, Lviv Polytechnic National University, 5 Mytropolyta Andreia, 79000 Lviv, Ukraine., Zabierowski W; Department of Microelectronic and Computer Science, Lodz University of Technology, 221 Wólczańska, 90-924 Łódź, Poland., Klymkovych D; Department of Computer Design Systems, Lviv Polytechnic National University, 5 Mytropolyta Andreia, 79000 Lviv, Ukraine.
Source:
Sensors (Basel, Switzerland) [Sensors (Basel)] 2025 Oct 17; Vol. 25 (20). Date of Electronic Publication: 2025 Oct 17.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: MDPI Country of Publication: Switzerland NLM ID: 101204366 Publication Model: Electronic Cited Medium: Internet ISSN: 1424-8220 (Electronic) Linking ISSN: 14248220 NLM ISO Abbreviation: Sensors (Basel) Subsets: PubMed not MEDLINE; MEDLINE
Imprint Name(s):
Original Publication: Basel, Switzerland : MDPI, c2000-
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Contributed Indexing:
Keywords: COMSOL Multiphysics; Livelink API; acoustofluidics; lab-on-a-chip; machine learning; microfluidic simulation; microparticle separation; neural networks; parameter optimization; reinforcement learning
Entry Date(s):
Date Created: 20251029 Latest Revision: 20251101
Update Code:
20251101
PubMed Central ID:
PMC12567905
DOI:
10.3390/s25206427
PMID:
41157481
Database:
MEDLINE

Weitere Informationen

An integrated approach for enhancing microparticle separation efficiency in acoustofluidic lab-on-a-chip systems is presented, combining numerical modeling in COMSOL 6.2 Multiphysics <sup>®</sup> with reinforcement learning techniques implemented in Python 3.10.14. The proposed method addresses the limitations of traditional parameter tuning, which is time-consuming and computationally intensive. A simulation framework based on LiveLink™ for COMSOL-Python integration enables the automatic generation, execution, and evaluation of particle separation scenarios. Reinforcement learning algorithms, trained on both successful and failed experiments, are employed to optimize control parameters such as flow velocity and acoustic frequency. Experimental data from over 100 numerical simulations were used to train a neural network, which demonstrated the ability to accurately predict and improve sorting efficiency. The results confirm that incorporating failed outcomes into the reward structure significantly improves learning convergence and model accuracy. This work contributes to the development of intelligent microfluidic systems capable of autonomous adaptation and optimization for biomedical and analytical applications, such as label-free separation of microplastics from biological fluids, selective sorting of soot and ash particles for environmental monitoring, and high-precision manipulation of cells or extracellular vesicles for diagnostic assays.