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Rapid Temperature-Dependent Rheological Measurements of Non-Newtonian Solutions Using a Machine-Learning Aided Microfluidic Rheometer
Francesco Del Giudice 
,
Claire Barnes
,
Claire Barnes
Analytical Chemistry, Volume: 94, Issue: 8, Pages: 3617 - 3628
Swansea University Authors:
Francesco Del Giudice , Claire Barnes
DOI (Published version): 10.1021/acs.analchem.1c05208
Abstract
Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non-Newtonian properties to the biofluids. The concentration of such protein macromolecules in biofluids is regarded as an important biomarker for the diagnosis of several health conditions, including ca...
| Published in: | Analytical Chemistry |
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| ISSN: | 0003-2700 1520-6882 |
| Published: |
American Chemical Society (ACS)
2022
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa59330 |
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| Abstract: |
Biofluids such as synovial fluid, blood plasma, and saliva contain several proteins which impart non-Newtonian properties to the biofluids. The concentration of such protein macromolecules in biofluids is regarded as an important biomarker for the diagnosis of several health conditions, including cardiovascular disorders, joint quality, and Alzheimer’s. Existing technologies for the measurements of macromolecules in biofluids are limited; they require a long turnaround time, or require complex protocols, thus calling for alternative, more suitable, methodologies aimed at such measurements. According to the well-established relations for polymer solutions, the concentration of macromolecules in solutions can also be derived via measurement of rheological properties such as shear-viscosity and the longest relaxation time. We here introduce a microfluidic rheometer for rapid simultaneous measurement of shear viscosity and longest relaxation time of non-Newtonian solutions at different temperatures. At variance with previous technologies, our microfluidic rheometer provides a very short turnaround time of around 2 min or less thanks to the implementation of a machine-learning algorithm. We validated our platform on several aqueous solutions of poly(ethylene oxide). We also performed measurements on hyaluronic acid solutions in the clinical range for joint grade assessment. We observed monotonic behavior with the concentration for both rheological properties, thus speculating on their use as potential rheo-markers, i.e., rheological biomarkers, across several disease states. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
F.D.G. acknowledges support from EPSRC New Investigator Award (grant ref no. EP/S036490/1). |
| Issue: |
8 |
| Start Page: |
3617 |
| End Page: |
3628 |