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Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives
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 stil...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561784/ https://www.ncbi.nlm.nih.gov/pubmed/36008135 http://dx.doi.org/10.1002/advs.202202638 |
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author | Nadernezhad, Ali Groll, Jürgen |
author_facet | Nadernezhad, Ali Groll, Jürgen |
author_sort | Nadernezhad, Ali |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-9561784 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-95617842022-10-16 Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives Nadernezhad, Ali Groll, Jürgen Adv Sci (Weinh) Research Articles 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. John Wiley and Sons Inc. 2022-08-25 /pmc/articles/PMC9561784/ /pubmed/36008135 http://dx.doi.org/10.1002/advs.202202638 Text en © 2022 The Authors. Advanced Science published by Wiley‐VCH GmbH https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Research Articles Nadernezhad, Ali Groll, Jürgen Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives |
title | Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives |
title_full | Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives |
title_fullStr | Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives |
title_full_unstemmed | Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives |
title_short | Machine Learning Reveals a General Understanding of Printability in Formulations Based on Rheology Additives |
title_sort | machine learning reveals a general understanding of printability in formulations based on rheology additives |
topic | Research Articles |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9561784/ https://www.ncbi.nlm.nih.gov/pubmed/36008135 http://dx.doi.org/10.1002/advs.202202638 |
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