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Title: Closed-loop feedback control of microfluidic cell manipulation via deep-learning integrated sensor networks
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.  more » « less
Award ID(s):
1752170
PAR ID:
10407957
Author(s) / Creator(s):
; ; ; ; ;
Date Published:
Journal Name:
Lab on a Chip
Volume:
21
Issue:
10
ISSN:
1473-0197
Page Range / eLocation ID:
1916 to 1928
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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