Search research articles

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

High throughput cell stiffness measurement via multiplexed impedance sensors.

Biosensors & bioelectronics·2025

Same author

Electronic detection of apoptotic cells on a microchip.

Biosensors & bioelectronics·2024

Same author

Centrifugation-Assisted Three-Dimensional Printing of Devices Embedded with Fully Enclosed Microchannels.

3D printing and additive manufacturing·2023

Same author

Chemically Amplified Multiplex Detection of SARS-CoV-2 and Influenza A and B Viruses via Paint-Programmed Lateral Flow Assays.

Small (Weinheim an der Bergstrasse, Germany)·2023

Same author

Paper-based multi-well depletion ELISA.

Lab on a chip·2023

Same author

An autonomous microchip for real-time, label-free immune cell analysis.

Biosensors & bioelectronics·2022

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Nov 5, 2025

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Published on: April 17, 2021

Closed-loop feedback control of microfluidic cell manipulation via deep-learning integrated sensor networks.

Ningquan Wang¹, Ruxiu Liu¹, Norh Asmare¹

¹School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA. sarioglu@gatech.edu.

|May 19, 2021

Summary

This study introduces an adaptive microfluidic system using electrical sensors and deep learning for real-time cell tracking. The system maintains consistent cell flow speeds despite disruptions, improving reliability for biomedical tests.

More Related Videos

Revealing Electromechanical Control of Tissue Homeostasis Using a Two-Layer Microfluidic Device

Revealing Electromechanical Control of Tissue Homeostasis Using a Two-Layer Microfluidic Device

Published on: September 19, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Related Experiment Videos

Last Updated: Nov 5, 2025

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Generation of Dynamical Environmental Conditions using a High-Throughput Microfluidic Device

Published on: April 17, 2021

Revealing Electromechanical Control of Tissue Homeostasis Using a Two-Layer Microfluidic Device

Revealing Electromechanical Control of Tissue Homeostasis Using a Two-Layer Microfluidic Device

Published on: September 19, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

Biomedical Engineering
Microfluidics
Sensor Technology

Background:

Microfluidic devices manipulate cells using force fields for characterization.
Standard microfluidic platforms struggle with specimen variability and external disturbances.
Optimized conditions in microfluidics often fail to account for real-world complexities.

Purpose of the Study:

To develop and validate an adaptive microfluidic system for robust cell manipulation.
To integrate on-chip electrical sensors and closed-loop feedback control for real-time adjustments.
To enhance the reliability and reduce artifacts in microfluidic cell analysis.

Main Methods:

An adaptive microfluidic system with distributed electrical sensors was designed.
Deep learning algorithms interpreted real-time cell flow speed data from sensors.
A proportional-integral feedback controller modulated a pressure pump to maintain target flow speeds.

Main Results:

The adaptive system successfully tracked cells and modulated chip parameters in real-time.
Validation with static and dynamic targets demonstrated system efficacy.
The system showed rapid convergence and resilience to continuous external perturbations.

Conclusions:

Adaptive microfluidic systems can sustain optimal processing conditions autonomously.
This technology reduces susceptibility to artifacts, enhancing data quality.
The system holds potential for reliable, standardized biomedical tests at the point of care.