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Chemical recycling of polymers is taking off as a circular technology, typically targeting pure recyclates. However, this is often not achieved efficiently due to high energy demand of separation and purification steps. In addition, many polymer applications have complex formulations that may be sensitive to impure feedstocks. Substitution of virgin feedstocks by complex recyclates (often containing impurities) requires a good knowledge of the structure/composition-property relations of polymer formulations. As this is often not the case, current practice relies on costly and rather inefficient enumeration experiments, or, at best, classical design-of-experiments approaches. We review the state of the art in structure-property modeling, present an example for polyurethane formulations, and propose a hybrid model-based framework. This involves a machine learning workflow for substitution problems in complex polymer formulations, combining existing data, novel reaction kinetics, structure-property models, molecular dynamics, and a minimum of experimental-analytical data where necessary, to build and validate the model.
|Journal||Current Opinion in Green and Sustainable Chemistry|
|Publication status||Published - Jun 2023|
Bibliographical noteFunding Information:
The authors gratefully acknowledge the Fonds voor Wetenschappelijk Onderzoek (FWO) Flanders for the financial support for the senior postdoctoral fellowships of Philippe Nimmegeers (1215523N) and Joost Brancart (12E1123N). Attila Kovács was supported by the University of Antwerp BOF DOCPRO3 40005.
The authors gratefully acknowledge the Fonds voor Wetenschappelijk Onderzoek (FWO) Flanders for the financial support for the senior postdoctoral fellowships of Philippe Nimmegeers (1215523N) and Joost Brancart (12E1123N). Attila Kovács was supported by the U niversity of Antwerp BOF DOCPRO3 40005 .
© 2023 Elsevier B.V.
Copyright 2023 Elsevier B.V., All rights reserved.
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