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Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines
The Molecular Operating Environment software (MOE) is used to construct a series of benzoxazine monomers for which a variety of parameters relating to the structures (e.g. water accessible surface area, negative van der Waals surface area, hydrophobic volume and the sum of atomic polarizabilities, e...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3542367/ https://www.ncbi.nlm.nih.gov/pubmed/23326419 http://dx.doi.org/10.1371/journal.pone.0053367 |
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author | Mhlanga, Phumzile Wan Hassan, Wan Aminah Hamerton, Ian Howlin, Brendan J. |
author_facet | Mhlanga, Phumzile Wan Hassan, Wan Aminah Hamerton, Ian Howlin, Brendan J. |
author_sort | Mhlanga, Phumzile |
collection | PubMed |
description | The Molecular Operating Environment software (MOE) is used to construct a series of benzoxazine monomers for which a variety of parameters relating to the structures (e.g. water accessible surface area, negative van der Waals surface area, hydrophobic volume and the sum of atomic polarizabilities, etc.) are obtained and quantitative structure property relationships (QSPR) models are formulated. Three QSPR models (formulated using up to 5 descriptors) are first used to make predictions for the initiator data set (n = 9) and compared to published thermal data; in all of the QSPR models there is a high level of agreement between the actual data and the predicted data (within 0.63–1.86 K of the entire dataset). The water accessible surface area is found to be the most important descriptor in the prediction of T(g). Molecular modelling simulations of the benzoxazine polymer (minus initiator) carried out at the same time using the Materials Studio software suite provide an independent prediction of T(g). Predicted T(g) values from molecular modelling fall in the middle of the range of the experimentally determined T(g) values, indicating that the structure of the network is influenced by the nature of the initiator used. Hence both techniques can provide predictions of glass transition temperatures and provide complementary data for polymer design. |
format | Online Article Text |
id | pubmed-3542367 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2013 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-35423672013-01-16 Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines Mhlanga, Phumzile Wan Hassan, Wan Aminah Hamerton, Ian Howlin, Brendan J. PLoS One Research Article The Molecular Operating Environment software (MOE) is used to construct a series of benzoxazine monomers for which a variety of parameters relating to the structures (e.g. water accessible surface area, negative van der Waals surface area, hydrophobic volume and the sum of atomic polarizabilities, etc.) are obtained and quantitative structure property relationships (QSPR) models are formulated. Three QSPR models (formulated using up to 5 descriptors) are first used to make predictions for the initiator data set (n = 9) and compared to published thermal data; in all of the QSPR models there is a high level of agreement between the actual data and the predicted data (within 0.63–1.86 K of the entire dataset). The water accessible surface area is found to be the most important descriptor in the prediction of T(g). Molecular modelling simulations of the benzoxazine polymer (minus initiator) carried out at the same time using the Materials Studio software suite provide an independent prediction of T(g). Predicted T(g) values from molecular modelling fall in the middle of the range of the experimentally determined T(g) values, indicating that the structure of the network is influenced by the nature of the initiator used. Hence both techniques can provide predictions of glass transition temperatures and provide complementary data for polymer design. Public Library of Science 2013-01-10 /pmc/articles/PMC3542367/ /pubmed/23326419 http://dx.doi.org/10.1371/journal.pone.0053367 Text en © 2013 Mhlanga 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, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited. |
spellingShingle | Research Article Mhlanga, Phumzile Wan Hassan, Wan Aminah Hamerton, Ian Howlin, Brendan J. Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines |
title | Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines |
title_full | Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines |
title_fullStr | Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines |
title_full_unstemmed | Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines |
title_short | Using Combined Computational Techniques to Predict the Glass Transition Temperatures of Aromatic Polybenzoxazines |
title_sort | using combined computational techniques to predict the glass transition temperatures of aromatic polybenzoxazines |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3542367/ https://www.ncbi.nlm.nih.gov/pubmed/23326419 http://dx.doi.org/10.1371/journal.pone.0053367 |
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