Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning

We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness...

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Autores principales: Özkan, Merve, Borghei, Maryam, Karakoç, Alp, Rojas, Orlando J., Paltakari, Jouni
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856819/
https://www.ncbi.nlm.nih.gov/pubmed/29549298
http://dx.doi.org/10.1038/s41598-018-23114-x
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author Özkan, Merve
Borghei, Maryam
Karakoç, Alp
Rojas, Orlando J.
Paltakari, Jouni
author_facet Özkan, Merve
Borghei, Maryam
Karakoç, Alp
Rojas, Orlando J.
Paltakari, Jouni
author_sort Özkan, Merve
collection PubMed
description We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness and wetting behavior of the films were assessed. Optimization was carried out as a function of film composition following the “random forest” machine learning algorithm for regression analysis. As a result, the design of tailor-made TOCNF-based films can be achieved with reduced experimental expenditure. We envision this approach to be useful in facilitating adoption of TOCNF for the design of emerging flexible electronics, and related platforms.
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spelling pubmed-58568192018-03-22 Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning Özkan, Merve Borghei, Maryam Karakoç, Alp Rojas, Orlando J. Paltakari, Jouni Sci Rep Article We systematically investigated the effect of film-forming polyvinyl alcohol and crosslinkers, glyoxal and ammonium zirconium carbonate, on the optical and surface properties of films produced from TEMPO-oxidized cellulose nanofibers (TOCNFs). In this regard, UV-light transmittance, surface roughness and wetting behavior of the films were assessed. Optimization was carried out as a function of film composition following the “random forest” machine learning algorithm for regression analysis. As a result, the design of tailor-made TOCNF-based films can be achieved with reduced experimental expenditure. We envision this approach to be useful in facilitating adoption of TOCNF for the design of emerging flexible electronics, and related platforms. Nature Publishing Group UK 2018-03-16 /pmc/articles/PMC5856819/ /pubmed/29549298 http://dx.doi.org/10.1038/s41598-018-23114-x Text en © The Author(s) 2018 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Özkan, Merve
Borghei, Maryam
Karakoç, Alp
Rojas, Orlando J.
Paltakari, Jouni
Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning
title Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning
title_full Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning
title_fullStr Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning
title_full_unstemmed Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning
title_short Films based on crosslinked TEMPO-oxidized cellulose and predictive analysis via machine learning
title_sort films based on crosslinked tempo-oxidized cellulose and predictive analysis via machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5856819/
https://www.ncbi.nlm.nih.gov/pubmed/29549298
http://dx.doi.org/10.1038/s41598-018-23114-x
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