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.

1.
N.
Shokraneh
,
M.
Alimi
,
S.-A.
Shahidi
,
M.
Mizani
,
M.
Bameni Moghadam
, and
A.
Rafe
, “
Textural and rheological properties of sliceable ketchup
,”
Gels
9
(
3
),
222
(
2023
).
2.
D. M.
Prajapati
,
N. M.
Shrigod
,
R. J.
Prajapati
, and
P. D.
Pandit
, “
Textural and rheological properties of yoghurt: A review
,”
Adv. Life Sci.
5
,
5238
5254
(
2016
).
3.
D.
Foresti
,
K. T.
Kroll
,
R.
Amissah
,
F.
Sillani
,
K. A.
Homan
,
D.
Poulikakos
, and
J. A.
Lewis
, “
Acoustophoretic printing
,”
Sci. Adv.
4
(
8
),
eaat1659
(
2018
).
4.
Y.
Sun
,
L.
Wang
,
Y.
Ni
,
H.
Zhang
,
X.
Cui
,
J.
Li
,
Y.
Zhu
,
J.
Liu
,
S.
Zhang
,
Y.
Chen
, and
M.
Li
, “
3D printing of thermosets with diverse rheological and functional applicabilities
,”
Nat. Commun.
14
(
1
),
245
(
2023
).
5.
G.
Haghiashtiani
,
K.
Qiu
,
J. D.
Zhingre Sanchez
,
Z. J.
Fuenning
,
P.
Nair
,
S. E.
Ahlberg
,
P. A.
Iaizzo
, and
M. C.
McAlpine
, “
3D printed patient-specific aortic root models with internal sensors for minimally invasive applications
,”
Sci. Adv.
6
(
35
),
eabb4641
(
2020
).
6.
M.
Zhou
,
Z.
Qi
,
Z.
Xia
,
Y.
Li
,
W.
Ling
,
J.
Yang
,
Z.
Yang
,
J.
Pei
,
D.
Wu
,
W.
Huo
, and
X.
Huang
, “
Miniaturized soft centrifugal pumps with magnetic levitation for fluid handling
,”
Sci. Adv.
7
(
44
),
eabi7203
(
2021
).
7.
R. P.
Chhabra
, “
Non-Newtonian fluids: An introduction
,” in
Rheology of Complex Fluids
, edited by
J. M.
Krishnan
,
A. P.
Deshpande
, and
P. B. S.
Kumar
(
Springer
,
New York
,
2010
), pp.
3
34
.
8.
J. R.
Sempionatto
,
M.
Lin
,
L.
Yin
,
E.
De la paz
,
K.
Pei
,
T.
Sonsa-ard
,
A. N.
de Loyola Silva
,
A. A.
Khorshed
,
F.
Zhang
,
N.
Tostado
,
S.
Xu
, and
J.
Wang
, “
An epidermal patch for the simultaneous monitoring of haemodynamic and metabolic biomarkers
,”
Nat. Biomed. Eng.
5
(
7
),
737
748
(
2021
).
9.
I.
Fyrippi
,
I.
Owen
, and
M. P.
Escudier
, “
Flowmetering of non-Newtonian liquids
,”
Flow Meas. Instrum.
15
(
3
),
131
138
(
2004
).
10.
A.
Bista
,
S. A.
Hogan
,
C. P.
O'Donnell
,
J. T.
Tobin
, and
N.
O'Shea
, “
Evaluation and validation of an inline Coriolis flowmeter to measure dynamic viscosity during laboratory and pilot-scale food processing
,”
Innovative Food Sci. Emerging Technol.
54
,
211
218
(
2019
).
11.
C.
Mills
, “
The consistency of pressure effects between three identical Coriolis flow meters
,”
Flow Meas. Instrum.
80
,
102001
(
2021
).
12.
S.
Basu
,
Plant Flow Measurement and Control Handbook
(
Academic Press
,
London/Cambridge, MA
,
2019
).
13.
D. A. J.
Brion
and
S. W.
Pattinson
, “
Generalisable 3D printing error detection and correction via multi-head neural networks
,”
Nat. Commun.
13
(
1
),
4654
(
2022
).
14.
Y.
Ma
,
J.
Potappel
,
A.
Chauhan
,
M. A. I.
Schutyser
,
R. M.
Boom
, and
L.
Zhang
, “
Improving 3D food printing performance using computer vision and feedforward nozzle motion control
,”
J. Food Eng.
339
,
111277
(
2023
).
15.
T. J. K.
Buchner
,
S.
Rogler
,
S.
Weirich
,
Y.
Armati
,
B. G.
Cangan
,
J.
Ramos
,
S. T.
Twiddy
,
D. M.
Marini
,
A.
Weber
,
D.
Chen
,
G.
Ellson
,
J.
Jacob
,
W.
Zengerle
,
D.
Katalichenko
,
C.
Keny
,
W.
Matusik
, and
R. K.
Katzschmann
, “
Vision-controlled jetting for composite systems and robots
,”
Nature
623
(
7987
),
522
530
(
2023
).
16.
S.
Razvarz
,
C.
Vargas-Jarillo
,
R.
Jafari
, and
A.
Gegov
, “
Flow control of fluid in pipelines using PID controller
,”
IEEE Access
7
,
25673
25680
(
2019
).
17.
B.
Gholami
,
W. M.
Haddad
,
J. M.
Bailey
, and
W. W.
Muir
, “
Closed-loop control for fluid resuscitation: Recent advances and future challenges
,”
Front. Vet. Sci.
8
,
642440
(
2021
).
18.
Y.
Hu
,
G.
Tang
, and
D.
Huang
, “
An intelligent control algorithm applied to the flow control of phosphor colloid
,” in
34th Chinese Control and Decision Conference (CCDC)
(
IEEE
,
2022
), pp.
1602
1607
.
19.
G.
Fiore
,
G.
Perrino
,
M.
di Bernardo
, and
D.
di Bernardo
, “
In vivo real-time control of gene expression: A comparative analysis of feedback control strategies in yeast
,”
ACS Synth. Biol.
5
(
2
),
154
162
(
2016
).
20.
J.-B.
Lugagne
,
S.
Sosa Carrillo
,
M.
Kirch
,
A.
Köhler
,
G.
Batt
, and
P.
Hersen
, “
Balancing a genetic toggle switch by real-time feedback control and periodic forcing
,”
Nat. Commun.
8
(
1
),
1671
(
2017
).
21.
X.
Zhang
,
X.
Fan
,
H.
Bao
, and
J.
Ping
, “
Electrical contactless microfluidic flow quantification
,”
Appl. Phys. Lett.
120
(
4
),
044102
(
2022
).
22.
M.
Jafari
,
G.
Marquez
,
J.
Selberg
,
M.
Jia
,
H.
Dechiraju
,
P.
Pansodtee
,
M.
Teodorescu
,
M.
Rolandi
, and
M.
Gomez
, “
Feedback control of bioelectronic devices using machine learning
,”
IEEE Control Syst. Lett.
5
(
4
),
1133
1138
(
2021
).
23.
J.
Selberg
,
M.
Jafari
,
J.
Mathews
,
M.
Jia
,
P.
Pansodtee
,
H.
Dechiraju
,
C.
Wu
,
S.
Cordero
,
A.
Flora
,
N.
Yonas
,
S.
Jannetty
,
M.
Diberardinis
,
M.
Teodorescu
,
M.
Levin
,
M.
Gomez
, and
M.
Rolandi
, “
Machine learning-driven bioelectronics for closed-loop control of cells
,”
Adv. Intell. Syst.
2
(
12
),
2000140
(
2020
).
24.
K. T.
Estelle
and
B. A.
Gozen
, “
Complex ink flow mechanisms in micro-direct-ink-writing and their implications on flow rate control
,”
Addit. Manuf.
59
,
103183
(
2022
).
25.
P. L.
Mage
,
B. S.
Ferguson
,
D.
Maliniak
,
K. L.
Ploense
,
T. E.
Kippin
, and
H. T.
Soh
, “
Closed-loop control of circulating drug levels in live animals
,”
Nat. Biomed. Eng.
1
(
5
),
0070
(
2017
).
26.
J. W.
Kopatz
,
J.
Unangst
,
A. W.
Cook
, and
L. N.
Appelhans
, “
Compositional effects on cure kinetics, mechanical properties and printability of dual-cure epoxy/acrylate resins for DIW additive manufacturing
,”
Addit. Manuf.
46
,
102159
(
2021
).
27.
Y.
Ezzyat
,
P. A.
Wanda
,
D. F.
Levy
,
A.
Kadel
,
A.
Aka
,
I.
Pedisich
,
M. R.
Sperling
,
A. D.
Sharan
,
B. C.
Lega
,
A.
Burks
,
R. E.
Gross
,
C. S.
Inman
,
B. C.
Jobst
,
M. A.
Gorenstein
,
K. A.
Davis
,
G. A.
Worrell
,
M. T.
Kucewicz
,
J. M.
Stein
,
R.
Gorniak
,
S. R.
Das
,
D. S.
Rizzuto
, and
M. J.
Kahana
, “
Closed-loop stimulation of temporal cortex rescues functional networks and improves memory
,”
Nat. Commun.
9
(
1
),
365
(
2018
).
28.
K. W.
Scangos
,
A. N.
Khambhati
,
P. M.
Daly
,
G. S.
Makhoul
,
L. P.
Sugrue
,
H.
Zamanian
,
T. X.
Liu
,
V. R.
Rao
,
K. K.
Sellers
,
H. E.
Dawes
,
P. A.
Starr
,
A. D.
Krystal
, and
E. F.
Chang
, “
Closed-loop neuromodulation in an individual with treatment-resistant depression
,”
Nat. Med.
27
(
10
),
1696
1700
(
2021
).
29.
T.
Cover
and
P.
Hart
, “
Nearest neighbor pattern classification
,”
IEEE Trans. Inf. Theory
13
(
1
),
21
27
(
1967
).
30.
B.
Qiu
,
X.
Chen
,
F.
Xu
,
D.
Wu
,
Y.
Zhou
,
W.
Tu
,
H.
Jin
,
G.
He
,
S.
Chen
, and
D.
Sun
, “
Nanofiber self-consistent additive manufacturing process for 3D microfluidics
,”
Microsyst. Nanoeng.
8
(
1
),
102
(
2022
).
31.
Y.
Liu
,
Q.
Yu
,
X.
Luo
,
L.
Yang
, and
Y.
Cui
, “
Continuous monitoring of diabetes with an integrated microneedle biosensing device through 3D printing
,”
Microsyst. Nanoeng.
7
(
1
),
75
(
2021
).
32.
K. H. K.
Wong
,
J. M.
Chan
,
R. D.
Kamm
, and
J.
Tien
, “
Microfluidic models of vascular functions
,”
Annu. Rev. Biomed. Eng.
14
,
205
230
(
2012
).
You do not currently have access to this content.