4.6 Article

ReaxFF reactive force field model enables accurate prediction of physiochemical and mechanical properties of crystalline and amorphous shape-memory polyurethane

Journal

JOURNAL OF APPLIED POLYMER SCIENCE
Volume 140, Issue 39, Pages -

Publisher

WILEY
DOI: 10.1002/app.54466

Keywords

glass transition temperature; mechanical properties; ReaxFF; shape memory polymers; thermoplastics

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In this study, we used the ReaxFF simulation method to investigate the physiochemical and mechanical properties, as well as the stimuli response behavior, of a shape-memory polyurethane (SMPU) rigid segment. We developed models for both crystalline and amorphous forms of a common rigid segment of SMPUs and performed molecular dynamics simulations to predict their structural and physiochemical properties. This study provides a theoretical pathway for the application-oriented design of high-performance SMPUs in fields such as soft robotics.
We investigate reactive force field (ReaxFF) prediction of physiochemical and mechanical properties, and stimuli response behavior of a shape-memory polyurethane (SMPU) rigid segment. We used SMPUs as a platform to link the rigid/hard segment domain crystallinity with properties that can also be experimentally measured. Specifically, we developed models for crystalline and amorphous forms of a common rigid segment of SMPUs, for example, 4,40-diphenylmethane diisocyanate (MDI) with n-butanediol (BDO), denoted as MDI-BDO. ReaxFF molecular dynamics simulations with equilibrium dynamics are performed by controlling mass, temperature, and pressure or volume to predict the structural and physiochemical properties of crystalline and amorphous rigid segments of SMPU systems, followed by simulating their XRD and FTIR patterns, respectively. Non-equilibrium ReaxFF simulations are implemented by using uniaxial box deformation technique to study response-behavior of crystalline SMPU under tensile loading and during stress relaxation. The model and simulation scheme presented in this study demonstrates a robust theoretical pathway toward application-dictated, on-demand design of high-performance SMPUs in applications such as soft robotics.

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