Journal article 92 views 29 downloads
Dimensional analysis meets AI for non-Newtonian droplet generation
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Alexandre Fabregat ,
Salvatore Cito ,
Francesco Del Giudice
Lab on a Chip, Volume: 25, Issue: 7, Pages: 1681 - 1693
Swansea University Authors:
Claire Barnes , Francesco Del Giudice
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© The Royal Society of Chemistry 2025. This Open Access Article is licensed under a Creative Commons Attribution 3.0 Unported Licence (CC BY).
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DOI (Published version): 10.1039/d4lc00946k
Abstract
Non-Newtonian droplets are used across various applications, including pharmaceuticals, food processing, drug delivery and material science. However, predicting droplet formation using such complex fluids is challenging due to the intricate multiphase interactions between fluids with varying viscosi...
| Published in: | Lab on a Chip |
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| ISSN: | 1473-0197 1473-0189 |
| Published: |
Royal Society of Chemistry (RSC)
2025
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| Online Access: |
Check full text
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa68926 |
| Abstract: |
Non-Newtonian droplets are used across various applications, including pharmaceuticals, food processing, drug delivery and material science. However, predicting droplet formation using such complex fluids is challenging due to the intricate multiphase interactions between fluids with varying viscosities, elastic properties and geometrical constraints. In this study, we introduce a novel hybrid machine-learning architecture that integrates dimensional analysis with machine learning to predict the flow rates required to generate droplets with specified sizes in systems involving non-Newtonian fluids. Unlike previous approaches, our model is designed to accommodate shear-rate-dependent viscosities and a simple estimate of the elastic properties of the fluids. It provides accurate predictions of the dispersed and continuous phases flow rates for given droplet length, height, and viscosity curves, even when the fluid properties deviate from those used during training. Our model demonstrates strong predictive power, achieving R2 values of up to 0.82 for unseen data. The significance of our work lies in its ability to generalize across a broad range of non-Newtonian systems having different viscosity curves, offering a powerful tool for optimizing droplet generation. This model represents a significant advancement in the application of machine learning to microfluidics, providing new opportunities for efficient experimental design in complex multiphase systems. |
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| College: |
Faculty of Science and Engineering |
| Funders: |
FH acknowledges funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 945413 and from the Universitat Rovira i Virgili (URV). FDG acknowledges support from Royal Society Grant RS/R1/221263, BBSRC Grant BB/Y513337/1, and the Ser Cymru programme ‘Enhancing Competitiveness Equipment Awards’ Grant MA/VG/2715/22-PN47. |
| Issue: |
7 |
| Start Page: |
1681 |
| End Page: |
1693 |