Please use this identifier to cite or link to this item: doi:10.22028/D291-46289
Title: Water-Induced Transparency Loss in Styrene Butadiene Block Copolymers: Mechanism, Morphology, and Predictive Modeling
Author(s): De Vrieze, Jenoff E.
Verswyvel, Michiel
Ghulam, Kinza Y.
Niebuur, Bart-Jan
Kraus, Tobias
Gallei, Markus
Niessner, Norbert
Language: English
Title: Macromolecules
Volume: 58
Issue: 15
Pages: 7673-7685
Publisher/Platform: ACS
Year of Publication: 2025
Free key words: Absorption
Diffusion
Materials
Optical properties
Polymers
DDC notations: 500 Science
Publikation type: Journal Article
Abstract: Water-induced transparency loss in styrene−butadiene block copolymers (SBCs) has been investigated under a variety of conditions. Consistent with earlier work on homopolymers, the opacity after prolonged water exposure is expected to be caused by water clustering, which results from stronger water−water than water−polymer interactions. The water clusters distort the surrounding polymer matrix, causing local changes in the refractive index. It was found that the hard phase has only a minor contribution to the transparency loss, while the rubbery phase appears to be the major contributor. However, the loss of transparency was found not to be directly proportional to the volume of the soft phase, and a significant effect of the block copolymer morphology was observed, which was confirmed by a series of transmission electron microscopy and SAXS measurements. This effect is particularly evident in the transition from a continuous hard phase through a co-continuous morphology to a continuous soft phase. The acquired insights were subsequently used to predict long-term optical performance in SBCs to provide a tool in product development. Loss of transparency predictions was proven to be adequate through a classical regression-extrapolation approach using a limited data set, accurately simulating performance beyond 2600 h exposure time using only 600 h of measurement time. Additionally, it was shown that artificial neural networks could provide a solid tool in predicting performance even prior to synthesis, granted that the selection of descriptors is complete and the appropriate amount of data is supplied with a proper spread over the descriptor space.
DOI of the first publication: 10.1021/acs.macromol.5c01354
URL of the first publication: https://pubs.acs.org/doi/10.1021/acs.macromol.5c01354
Link to this record: urn:nbn:de:bsz:291--ds-462892
hdl:20.500.11880/40577
http://dx.doi.org/10.22028/D291-46289
ISSN: 1520-5835
0024-9297
Date of registration: 17-Sep-2025
Description of the related object: Supporting Information
Related object: https://pubs.acs.org/doi/suppl/10.1021/acs.macromol.5c01354/suppl_file/ma5c01354_si_001.pdf
Faculty: NT - Naturwissenschaftlich- Technische Fakultät
Department: NT - Chemie
Professorship: NT - Prof. Dr. Markus Gallei
NT - Prof. Dr. Tobias Kraus
Collections:SciDok - Der Wissenschaftsserver der Universität des Saarlandes



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