Real-time, all-electronic control of non-Newtonian fluid flow through a microscale channel is crucial for various applications in manufacturing and healthcare. However, existing methods lack the sensitivity required for accurate measurement and the real-time responsiveness necessary for effective adjustment. Here, we demonstrate an all-electronic system that enables closed-loop, real-time, high-sensitivity control of various waveforms of non-Newtonian fluid flow (0.76 μl min−1) through a micro-sized outlet. Our approach combines a contactless, cuff-like flow sensor with a neural-network control program. This system offers a simple, miniaturized, versatile, yet high-performance solution for non-Newtonian fluid flow control, easily integrated into existing setups.
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14 October 2024
Research Article|
October 18 2024
Neural network–enabled, all-electronic control of non-Newtonian fluid flow
Huilu Bao
;
Huilu Bao
(Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft)
1
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst
, Amherst, Massachusetts 01003, USA
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Xin Zhang
;
Xin Zhang
(Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft)
1
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst
, Amherst, Massachusetts 01003, USA
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Xiaoyu Zhang
;
Xiaoyu Zhang
(Methodology, Resources, Writing – review & editing)
1
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst
, Amherst, Massachusetts 01003, USA
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Xiao Fan
;
Xiao Fan
(Methodology, Resources, Writing – review & editing)
1
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst
, Amherst, Massachusetts 01003, USA
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J. William Boley
;
J. William Boley
(Conceptualization, Funding acquisition, Resources, Writing – review & editing)
2
Department of Mechanical Engineering, Boston University
, Boston, Massachusetts 02215, USA
3
Division of Materials Science and Engineering, Boston University
, Boston, Massachusetts 02215, USA
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Jinglei Ping
Jinglei Ping
a)
(Conceptualization, Formal analysis, Funding acquisition, Resources, Supervision, Writing – review & editing)
1
Department of Mechanical and Industrial Engineering, University of Massachusetts Amherst
, Amherst, Massachusetts 01003, USA
4
Institute of Applied Life Sciences, University of Massachusetts Amherst
, Amherst, Massachusetts 01003, USA
a)Author to whom correspondence should be addressed: ping@engin.umass.edu
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a)Author to whom correspondence should be addressed: ping@engin.umass.edu
Appl. Phys. Lett. 125, 164105 (2024)
Article history
Received:
July 02 2024
Accepted:
September 17 2024
Connected Content
A companion article has been published:
Machine learning method monitors non-Newtonian fluid flow in real time
Citation
Huilu Bao, Xin Zhang, Xiaoyu Zhang, Xiao Fan, J. William Boley, Jinglei Ping; Neural network–enabled, all-electronic control of non-Newtonian fluid flow. Appl. Phys. Lett. 14 October 2024; 125 (16): 164105. https://doi.org/10.1063/5.0226525
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