Investigating nature of stresses in extension and compression of glassy polymers via stress relaxation

Liu, Jianning; Zheng, Yexin; Zhao, Zhiling; Yuan, Min; Tsige, Mesfin; Wang, Shi‐Qing

doi:10.1016/j.polymer.2020.122517

Cited by 11 publications

(6 citation statements)

References 70 publications

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“…To further justify our assumption about the chain network and structural relaxation mechanisms of glassy polymers, we note that it is quite necessary to test a constitutive model rested on the assumption with multistep deformation and stress relaxation experiments. [ 31,60–64 ] However, this cannot be achieved yet in the present work. The main reason is that weak linkages in the chain network can re‐associate/reform after dissociation, and this linkage re‐association is not considered in the extended AAS model yet.…”

Section: Network Linkage Dissociationmentioning

confidence: 72%

Experimental and constitutive analyses of the stress relaxation behavior of glassy polymers

Liu

Wang

Zhang

et al. 2023

Polymer Engineering & Sci

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The physical mechanism underlying the mechanical behavior of glassy polymers has been studied over decades but remains a long‐standing issue. A consensus view achieved is that the yield, flow, and stress relaxation behaviors are due to structural relaxation in the polymer mainly caused by chain conformation transitions. This is the key physical idea behind the many existing elastic–plastic constitutive models for glassy polymers. In this paper, such a constitutive model was employed for predicting and analyzing the stress relaxation of a glassy polymer. It is found that the model works well in predicting the pre‐yield stress relaxation but significantly underestimates the post‐yield stress relaxation. As considering the chain conformation transition alone leads to a dilemma for the model to concurrently represent the yield/flow and stress relaxation behaviors, the model was extended to incorporate an additional structural relaxation mechanism assumed to originate from the dissociation of weak linkages in the chain network. The extended model succeeds in concurrently representing the yield/flow, and stress relaxation behaviors in the whole deformation region, of which the reasons were analyzed. The knowledge revealed in this paper is instructive and may shed new light on understanding the structural relaxation and mechanical behavior of glassy polymers.

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Section: Network Linkage Dissociationmentioning

confidence: 72%

Experimental and constitutive analyses of the stress relaxation behavior of glassy polymers

Liu

Wang

Zhang

et al. 2023

Polymer Engineering & Sci

View full text Add to dashboard Cite

show abstract

“…In more recent years, access to ever-growing computational power has allowed the materials modelling community to carry out increasingly complex investigations of polymeric materials. [21][22][23][24][25][26][27][28] Modern computer simulation studies provide predictive understanding of molecular interactions and mechanisms that determine the bulk-scale properties of polymers of interest. In tandem with experimental data, this combined insight yields rational design principles for new polymers with enhanced target properties.…”

Section: Introductionmentioning

confidence: 99%

Modular Software for Generating and Modelling Diverse Polymer Databases

Santana‐Bonilla

Castro

Sun

et al. 2023

Preprint

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Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale however building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modelling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a workflow by which to create and curate molecular databases without requiring significant levels of user input. This ensures well-founded concerns regarding data provenance, reproducibility and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King's College London), that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with a minimal user intervention. PySoftK is available as an efficient, fully-tested, and easily installed Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modelling and curation of large polymer libraries to support functional materials discovery in nano- and bio-technology.

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“…Polymeric materials have been the focus of a significant amount of scientific research for many decades. In more recent years, access to ever-growing computational power has allowed the materials modeling community to carry out increasingly complex investigations of polymeric materials. − Modern computer simulation studies provide predictive understanding of molecular interactions and mechanisms that determine the bulk-scale properties of polymers of interest. In tandem with experimental data, this combined insight yields rational design principles for new polymers with enhanced target properties.…”

Section: Introductionmentioning

confidence: 99%

Modular Software for Generating and Modeling Diverse Polymer Databases

Santana‐Bonilla

Castro

Sun

et al. 2023

J. Chem. Inf. Model.

View full text Add to dashboard Cite

Machine learning methods offer the opportunity to design new functional materials on an unprecedented scale; however, building the large, diverse databases of molecules on which to train such methods remains a daunting task. Automated computational chemistry modeling workflows are therefore becoming essential tools in this data-driven hunt for new materials with novel properties, since they offer a means by which to create and curate molecular databases without requiring significant levels of user input. This ensures that well-founded concerns regarding data provenance, reproducibility, and replicability are mitigated. We have developed a versatile and flexible software package, PySoftK (Python Soft Matter at King’s College London) that provides flexible, automated computational workflows to create, model, and curate libraries of polymers with minimal user intervention. PySoftK is available as an efficient, fully tested, and easily installable Python package. Key features of the software include the wide range of different polymer topologies that can be automatically generated and its fully parallelized library generation tools. It is anticipated that PySoftK will support the generation, modeling, and curation of large polymer libraries to support functional materials discovery in the nanotechnology and biotechnology arenas.

show abstract

Investigating nature of stresses in extension and compression of glassy polymers via stress relaxation

Cited by 11 publications

References 70 publications

Experimental and constitutive analyses of the stress relaxation behavior of glassy polymers

Experimental and constitutive analyses of the stress relaxation behavior of glassy polymers

Modular Software for Generating and Modelling Diverse Polymer Databases

Modular Software for Generating and Modeling Diverse Polymer Databases

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