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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...

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Autores principales: Mhlanga, Phumzile, Wan Hassan, Wan Aminah, Hamerton, Ian, Howlin, Brendan J.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2013
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.
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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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