Preparation and dynamic simulation of a hemin reversible associated copolymer with self-healing properties
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Huijia Lu
and
Bin Pan
Abstract
A reversible associated copolymer capable of healing at room temperature was constructed using acrylic acid (AA) and hemin as anionic monomers, and methacryloyloxyethyl trimethylammonium chloride (DMC) as the cationic monomer. Through optimization of the synthesis conditions, characterization by 1HNMR, infrared spectroscopy, ultraviolet-visible spectroscopy, Thermogravimetric analysis, differential scanning calorimetry, and construction of reasonable models for dynamic simulation, the following conclusions were obtained: The introduction of Hemin could convert light energy into heat energy, accelerating the healing of damaged areas; the predicted glass transition temperature value matched well with the experimental value, demonstrating the reliability of the constructed polymer model; the cohesive energy density (CED), dominated by electrostatic interactions, was identified as the key factor in the self-healing mechanism; the study also visually captured the microscopic process of self-healing within the polymer, providing a detailed understanding of the molecular rearrangements that facilitate this process. This work offers valuable insights into the design of reversible associated polymers, contributing to the broader goal of developing functional polymer materials aligned with circular economy principles.
Funding source: The general program of the Education Department of Liaoning Province
Award Identifier / Grant number: LJKMZ20220742
Funding source: Talent Scientific Research Fund of LNPU
Award Identifier / Grant number: No. 2016XJJ-010
Funding source: National Natural Science Foundation of China
Award Identifier / Grant number: No.61602228
Funding source: Liaoning Revitalization Talents Program
Award Identifier / Grant number: No. XLYC1807266
Acknowledgments
The authors would like to acknowledge Liaoning Petrochemical University.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: Xiaorong Wang contributed to the conception of the study and wrote the manuscript; Huijia Lu and Xiaozhen Zhang performed the experiment; Bin Pan contributed significantly to analysis; Zhimin Ma performed the data analyses and manuscript preparation; Hui Lv helped perform the analysis with constructive discussions; Ye Tian helped do the revision; Jianxue Zhou helped perform the analysis with constructive discussions.
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Use of Large Language Models, AI and Machine Learning Tools: None declared.
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Conflict of interest: The authors state no conflict of interest.
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Research funding: This work is financially supported by the Talent Scientific Research Fund of LNPU (No. 2016XJJ-010), the general program of the Education Department of Liaoning Province (No. LJKMZ20220742), National Natural Science Foundation of China (No.61602228), and the Liaoning Revitalization Talents Program (No. XLYC1807266).
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Data availability: All data generated or analyzed during this study are included in this published article.
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Supplementary Material
This article contains supplementary material (https://doi.org/10.1515/polyeng-2024-0102).
© 2024 Walter de Gruyter GmbH, Berlin/Boston
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