Journal
ADVANCED SCIENCE
Volume 9, Issue 29, Pages -Publisher
WILEY
DOI: 10.1002/advs.202202638
Keywords
bulk rheology; machine learning; printability; rheology additives
Categories
Funding
- Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [326998133-TRR 225]
- European Union (European Regional Development Fund-EFRE Bayern 2014-2020, Bio3D-Druck project) [20-3400-2-10]
- Projekt DEAL
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A machine learning approach is used to explain the process of rendering hydrogel ink formulations printable. Thirteen critical rheological measures are identified to describe the printability, providing a qualitative and physically interpretable guideline for designing new printable materials.
Hydrogel ink formulations based on rheology additives are becoming increasingly popular as they enable 3-dimensional (3D) printing of non-printable but biologically relevant materials. Despite the widespread use, a generalized understanding of how these hydrogel formulations become printable is still missing, mainly due to their variety and diversity. Employing an interpretable machine learning approach allows the authors to explain the process of rendering printability through bulk rheological indices, with no bias toward the composition of formulations and the type of rheology additives. Based on an extensive library of rheological data and printability scores for 180 different formulations, 13 critical rheological measures that describe the printability of hydrogel formulations, are identified. Using advanced statistical methods, it is demonstrated that even though unique criteria to predict printability on a global scale are highly unlikely, the accretive and collaborative nature of rheological measures provides a qualitative and physically interpretable guideline for designing new printable materials.
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