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- W4220760038 abstract "The introduction of dynamic bonds in polymeric materials facilitates the emergence of new functionalities, such as self-healing capacity. Understanding the role of the molecular structure in the efficiency of the self-healing process is fundamental to design new materials with improved features. Computational chemistry has emerged as a valuable tool for the characterization of polymeric materials. In this work, computational chemistry is used to analyze the observed self-healing capacity of a set of disulfide-containing polyurethanes and polymethacrylates, including different hard segments and dynamic bonds. For this purpose, a recently developed theoretical protocol has been used. This protocol is based on three parameters: the probability of generating radicals by cleavage of the disulfide bond, the energetic barrier of the exchange reaction among disulfides and the dynamics of the polymeric chains. This protocol is able to qualitatively explain the experimental self-healing properties of these materials. In particular, it explains both the great performance of two materials and the lack of self-healing capacity of another two. Besides, it can also describe the improvement of the self-healing capacity with increasing temperature. These results demonstrate the robustness and usefulness of this approach for the analysis and prediction of self-healing properties in polymeric materials. Therefore, this protocol allows to predict new materials with improved properties and will help the experimental community in the development of these improved materials." @default.
- W4220760038 created "2022-04-03" @default.
- W4220760038 creator A5016253452 @default.
- W4220760038 creator A5053289903 @default.
- W4220760038 creator A5089684436 @default.
- W4220760038 date "2022-03-21" @default.
- W4220760038 modified "2023-09-26" @default.
- W4220760038 title "Combined DFT and MD Simulation Protocol to Characterize Self-Healing Properties in Disulfide-Containing Materials: Polyurethanes and Polymethacrylates as Case Studies" @default.
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- W4220760038 doi "https://doi.org/10.3389/fmats.2022.859482" @default.
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