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Related Experiment Video

Updated: Oct 6, 2025

Label-free Isolation and Enrichment of Cells Through Contactless Dielectrophoresis

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Published on: September 3, 2013

Deep-Learning Based Estimation of Dielectrophoretic Force.

Sunday Ajala¹, Harikrishnan Muraleedharan Jalajamony¹, Renny Edwin Fernandez¹

¹Department of Engineering, Norfolk State University, Norfolk, VA 23504, USA.

|January 21, 2022

Summary

This study introduces a novel deep learning approach using textile electrodes to estimate dielectrophoretic (DEP) forces on microparticles. The method accurately quantifies DEP forces, showing robustness in real-world conditions for microfluidic applications.

Keywords:

AlexNet MobileNetV2 VGG19 convolutional neural networks (CNN)dielectrophoretic (DEP)force neural network pearl chain textile electrode

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Area of Science:

Microfluidics
Biotechnology
Machine Learning

Background:

Accurate quantification of dielectrophoretic (DEP) force is essential for advancing microfluidic systems.
Current methods for DEP force measurement can be complex and lack real-time applicability.

Purpose of the Study:

To develop and validate a deep learning model for estimating dielectrophoretic forces using a textile electrode-based sensing system.
To assess the model's performance and robustness under various experimental conditions and image perturbations.

Main Methods:

A textile electrode-based DEP sensing system was developed.
Micrographs of polystyrene microbead chains were captured at varying voltages.
Deep convolutional neural networks (AlexNet, MobileNetV2, VGG19) were trained on experimental images.
Model performance was evaluated using validation accuracy and adversarial testing (noise, blur, contrast changes).

Main Results:

The deep learning models successfully processed micrographs to estimate dielectrophoretic forces.
The developed method demonstrated robustness against image noise, blur, and contrast variations.
High validation accuracies were achieved across the tested deep learning architectures.

Conclusions:

The combination of textile electrodes and deep learning provides a robust method for direct dielectrophoretic force estimation.
This approach shows significant potential for real-time DEP force monitoring in point-of-care diagnostic devices.
The system's resilience to real-world imaging challenges makes it suitable for practical microfluidic applications.