Issue 10, 2021

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

Abstract

Microfluidic technologies have long enabled the manipulation of flow-driven cells en masse under a variety of force fields with the goal of characterizing them or discriminating the pathogenic ones. On the other hand, a microfluidic platform is typically designed to function under optimized conditions, which rarely account for specimen heterogeneity and internal/external perturbations. In this work, we demonstrate a proof-of-principle adaptive microfluidic system that consists of an integrated network of distributed electrical sensors for on-chip tracking of cells and closed-loop feedback control that modulates chip parameters based on the sensor data. In our system, cell flow speed is measured at multiple locations throughout the device, the data is interpreted in real-time via deep learning-based algorithms, and a proportional-integral feedback controller updates a programmable pressure pump to maintain a desired cell flow speed. We validate the adaptive microfluidic system with both static and dynamic targets and also observe a fast convergence of the system under continuous external perturbations. With an ability to sustain optimal processing conditions in unsupervised settings, adaptive microfluidic systems would be less prone to artifacts and could eventually serve as reliable standardized biomedical tests at the point of care.

Graphical abstract: Closed-loop feedback control of microfluidic cell manipulation via deep-learning integrated sensor networks

Supplementary files

Article information

Article type
Paper
Submitted
31 янв 2021
Accepted
26 мар 2021
First published
29 мар 2021

Lab Chip, 2021,21, 1916-1928

Author version available

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

N. Wang, R. Liu, N. Asmare, C. Chu, O. Civelekoglu and A. F. Sarioglu, Lab Chip, 2021, 21, 1916 DOI: 10.1039/D1LC00076D

To request permission to reproduce material from this article, please go to the Copyright Clearance Center request page.

If you are an author contributing to an RSC publication, you do not need to request permission provided correct acknowledgement is given.

If you are the author of this article, you do not need to request permission to reproduce figures and diagrams provided correct acknowledgement is given. If you want to reproduce the whole article in a third-party publication (excluding your thesis/dissertation for which permission is not required) please go to the Copyright Clearance Center request page.

Read more about how to correctly acknowledge RSC content.

Social activity

Spotlight

Advertisements