Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives.

ABSTRACT: 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 ide

SUBMITTER: Nadernezhad A 

PROVIDER: S-EPMC9561784 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature