Premechanical Training Enables Mechanical Reinforcement of Dynamic Covalent Polymer Networks: Insights from Molecular Dynamics Simulations

To address the growing demand for highly tunable mechanical properties in polymer networks, this study introduces a novel premechanical training (PMT) method based on bond exchange reactions (BERs), which allows for extensive modulation and tailoring of mechanical properties by inducing polymer network anisotropy. The PMT process can modulate the alignment of linear polymer ends according to the uniaxial deformation ratio λ during the BER phase: they tend to align parallel and perpendicular to the Z-axis (deformation direction) for λ < 1.00 and λ > 1.00, respectively. The degree of alignment is intensified by increasing the deviation of λ from 1.00 and extending the BER duration. This oriented arrangement of linear polymers induces anisotropy in the polymer network, which is fundamental for achieving the modulation of mechanical properties. When linear polymers tend to be aligned parallel to the Z-axis (λ < 1.00), significant enhancements in tensile strength, storage modulus, and toughness are achieved. In contrast, perpendicular alignment of linear polymers results in increased loss factor and fracture strain. More importantly, based on this mechanical property modulation mechanism, further personalization of mechanical properties can be achieved. In summary, this PMT approach leveraging BERs provides a versatile and effective strategy for modulating and tailoring the overall mechanical properties of polymer networks, offering substantial potential for the development of high-performance polymeric materials.

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Publication Details

Journal
Macromolecules
Published
2025-05-22
DOI
https://doi.org/10.1021/acs.macromol.4c03208
Citations
2
Primary Topic
Polymer composites and self-healing
Type
article
Field-Weighted Citation Impact
1.21

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article

Premechanical Training Enables Mechanical Reinforcement of Dynamic Covalent Polymer Networks: Insights from Molecular Dynamics Simulations

Pengwei Duan, Jun Liu, Liqun Zhang, Qionghai Chen
2 citations
Macromolecules
Polymer composites and self-healing
1.21
article

Premechanical Training Enables Mechanical Reinforcement of Dynamic Covalent Polymer Networks: Insights from Molecular Dynamics Simulations

Pengwei Duan, Jun Liu, Liqun Zhang, Qionghai Chen
article en
2 citations

Abstract

To address the growing demand for highly tunable mechanical properties in polymer networks, this study introduces a novel premechanical training (PMT) method based on bond exchange reactions (BERs), which allows for extensive modulation and tailoring of mechanical properties by inducing polymer network anisotropy. The PMT process can modulate the alignment of linear polymer ends according to the uniaxial deformation ratio λ during the BER phase: they tend to align parallel and perpendicular to the Z-axis (deformation direction) for λ < 1.00 and λ > 1.00, respectively. The degree of alignment is intensified by increasing the deviation of λ from 1.00 and extending the BER duration. This oriented arrangement of linear polymers induces anisotropy in the polymer network, which is fundamental for achieving the modulation of mechanical properties. When linear polymers tend to be aligned parallel to the Z-axis (λ < 1.00), significant enhancements in tensile strength, storage modulus, and toughness are achieved. In contrast, perpendicular alignment of linear polymers results in increased loss factor and fracture strain. More importantly, based on this mechanical property modulation mechanism, further personalization of mechanical properties can be achieved. In summary, this PMT approach leveraging BERs provides a versatile and effective strategy for modulating and tailoring the overall mechanical properties of polymer networks, offering substantial potential for the development of high-performance polymeric materials.

MacromoleculesVol. 58(11)
Composite Components (Czechia) (CZ), Beijing University of Chemical Technology (CN)
National Natural Science Foundation of China, National Outstanding Youth Science Fund Project of National Natural Science Foundation of China
Openalex Percentile: Top 23%
Polymer composites and self-healing
1.21
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