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Molecular Structures and Mechanisms of Waterborne Biodegradable Polyurethane Nanoparticles.

Chien-Hui Wen | Shun-Chieh Hsu | Shan-Hui Hsu | Shu-Wei Chang
Computational and structural biotechnology journal | 2019

Biodegradable hydrogels have become promising materials for many biological applications in the past years. Recently, novel waterborne biodegradable polyurethane (WDPU) nanoparticles have been synthesized by a green water-based process, and serve as fundamental building blocks to form materials with great biocompatibility, biodegradability, and mechanical properties. However, the molecular structures and mechanisms of the WDPU nanoparticles and the relationship between the chemical compositions of the polymer segments and the material properties of the biodegradable hydrogels at macro-scale are still not well understood. In this study, we explore the fundamental mechanisms of WDPU nanoparticles through a full atomistic simulation approach to understand how the chemical compositions at the molecular level affect the molecular structures and material properties of WDPU nanoparticles. Specifically, we compare two WDPUs, i.e. PCL75LL25 and PCL75DL25, of the same hard segment composition and very similar soft segment composition [75% poly(e-caprolatone) and 25% polylactide], except the lactide in the former is L-form and in the latter is D,L-form. Our results show that the material properties of the biodegradable hydrogel can be designed by tuning the chemical compositions of the polymer segments. We find that the PCL75DL25 and PCL75LL25 have distinct molecular structures and physical crosslinks within the nanoparticles. The molecular structure of WDPU with PDLLA as soft segments is more extended, leading to more physical crosslinks between PCL segments. This study provide fundamental insights into the molecular structures and mechanisms of WDPU nanoparticles and help enabling the design of material properties of biocompatible hydrogel.

Pubmed ID: 30728918

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COMPASS (tool)

RRID:SCR_015874

Algorithm for MATLAB and the EEGLAB toolbox that enables the automatic detection of independent components from an ICA that represent event-related brain potentials. It performs automatic Independent Component (IC) selection with respect to the contributions of the ICs to a certain ERP.

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