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Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures
Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428936/ https://www.ncbi.nlm.nih.gov/pubmed/28498875 http://dx.doi.org/10.1371/journal.pone.0177614 |
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author | Gok, Abdulkerim Ngendahimana, David K. Fagerholm, Cara L. French, Roger H. Sun, Jiayang Bruckman, Laura S. |
author_facet | Gok, Abdulkerim Ngendahimana, David K. Fagerholm, Cara L. French, Roger H. Sun, Jiayang Bruckman, Laura S. |
author_sort | Gok, Abdulkerim |
collection | PubMed |
description | Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R(2) values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction. |
format | Online Article Text |
id | pubmed-5428936 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-54289362017-05-26 Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures Gok, Abdulkerim Ngendahimana, David K. Fagerholm, Cara L. French, Roger H. Sun, Jiayang Bruckman, Laura S. PLoS One Research Article Accelerated weathering exposures were performed on poly(ethylene-terephthalate) (PET) films. Longitudinal multi-level predictive models as a function of PET grades and exposure types were developed for the change in yellowness index (YI) and haze (%). Exposures with similar change in YI were modeled using a linear fixed-effects modeling approach. Due to the complex nature of haze formation, measurement uncertainty, and the differences in the samples’ responses, the change in haze (%) depended on individual samples’ responses and a linear mixed-effects modeling approach was used. When compared to fixed-effects models, the addition of random effects in the haze formation models significantly increased the variance explained. For both modeling approaches, diagnostic plots confirmed independence and homogeneity with normally distributed residual errors. Predictive R(2) values for true prediction error and predictive power of the models demonstrated that the models were not subject to over-fitting. These models enable prediction under pre-defined exposure conditions for a given exposure time (or photo-dosage in case of UV light exposure). PET degradation under cyclic exposures combining UV light and condensing humidity is caused by photolytic and hydrolytic mechanisms causing yellowing and haze formation. Quantitative knowledge of these degradation pathways enable cross-correlation of these lab-based exposures with real-world conditions for service life prediction. Public Library of Science 2017-05-12 /pmc/articles/PMC5428936/ /pubmed/28498875 http://dx.doi.org/10.1371/journal.pone.0177614 Text en © 2017 Gok et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Gok, Abdulkerim Ngendahimana, David K. Fagerholm, Cara L. French, Roger H. Sun, Jiayang Bruckman, Laura S. Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures |
title | Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures |
title_full | Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures |
title_fullStr | Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures |
title_full_unstemmed | Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures |
title_short | Predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures |
title_sort | predictive models of poly(ethylene-terephthalate) film degradation under multi-factor accelerated weathering exposures |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5428936/ https://www.ncbi.nlm.nih.gov/pubmed/28498875 http://dx.doi.org/10.1371/journal.pone.0177614 |
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