陈伟婷,王金科,郭鑫,陈质彬,马力,蔺存国,马菱薇,张达威.计算材料学在自修复涂层领域的研究进展[J].表面技术,2024,53(22):1-15.
CHEN Weiting,WANG Jinke,GUO Xin,CHEN Zhibin,MA Li,LIN Cunguo,MA Lingwei,ZHANG Dawei.Research Progress of Computational Materials Science for the Self-healing Coatings[J].Surface Technology,2024,53(22):1-15 |
| 计算材料学在自修复涂层领域的研究进展 |
| Research Progress of Computational Materials Science for the Self-healing Coatings |
| 投稿时间：2024-02-27 修订日期：2024-06-12 |
| DOI：10.16490/j.cnki.issn.1001-3660.2024.22.001 |
| 中文关键词: 计算材料学 自修复 防腐涂层 微胶囊 缓蚀剂 分子动力学 密度泛函理论 有限元分析 |
| 英文关键词:computational materials science self-healing anti-corrosion coating microcapsules corrosion inhibitor molecular dynamics density function theory finite element analysis |
| 基金项目:国家自然科学基金(52371049);中国船舶集团有限公司第七二五研究所(LSMRI)海洋腐蚀与防护重点实验室研究基金(JS220414) |
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| Author | Institution |
| CHEN Weiting | Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing 100083, China;State Key Laboratory for Marine Corrosion and Protection, Luoyang Ship Material Research Institute, Shandong Qingdao 266101, China |
| WANG Jinke | Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing 100083, China |
| GUO Xin | Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing 100083, China |
| CHEN Zhibin | Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing 100083, China |
| MA Li | State Key Laboratory for Marine Corrosion and Protection, Luoyang Ship Material Research Institute, Shandong Qingdao 266101, China |
| LIN Cunguo | State Key Laboratory for Marine Corrosion and Protection, Luoyang Ship Material Research Institute, Shandong Qingdao 266101, China |
| MA Lingwei | Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing 100083, China |
| ZHANG Dawei | Institute of Advanced Materials & Technology, University of Science and Technology Beijing, Beijing 100083, China |
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| 中文摘要: |
| 自修复防腐涂层是指在遭受环境或外力损伤破坏后，可以自行修复从而仍具备良好防腐性能的一种新型智能材料。自修复涂层的设计方法主要是通过基于实验的树脂链段设计、填料设计和制备工艺调控，获得具有良好修复性能的防腐材料。近年来，计算材料学技术飞速发展，其利用热力学计算和动力学模拟来预测并设计新材料的结构和性能，是连接材料学理论与实验的桥梁。自修复防腐涂层的理论计算研究有利于理解微观层面的自修复行为，指导自修复防腐涂层的制备与性能优化，并大幅度降低成本。综述了计算材料学在自修复防腐涂层领域的研究进展，介绍了目前基于外援型和本征型两种自修复防腐涂层体系研究的多种计算方法，主要包括分子动力学、密度泛函理论计算、蒙特卡罗模拟和有限元分析等。这些计算方法可以从微观和宏观角度对防腐涂层体系进行直观的机理解释和性能预测。动态键所进行的交换反应及自愈过程、微胶囊的破裂及愈合剂的释放过程、缓蚀剂在基底的吸附方式和吸附强度等都能在自修复防腐涂层的理论研究中得以体现。分析了这些计算模拟方法在自修复涂层方面应用的优势与不足，为自修复防腐涂层的开发与应用提供了理论支持。未来将进一步加强计算模拟、实验研究、人工智能、大数据分析和机器学习技术的深度融合，建立涂层自修复性能数据库，更高效地进行材料筛选和设计，从而在自修复涂层的研究和应用中实现突破。 |
| 英文摘要: |
| Self-healing anti-corrosion coatings refer to a novel class of intelligent materials capable of autonomously repairing damage inflicted by environmental factors or external forces, thereby retaining their effective corrosion resistance. Based on the healing mechanisms, self-healing coatings are primarily categorized into extrinsic self-healing ones and intrinsic self-healing ones. The design methodologies for self-healing coatings primarily involve experimental-based design of resin segments, filler design, and fabrication process control to achieve materials with excellent self-healing capabilities. In recent years, the rapid development of computational materials science has utilized computational thermodynamics and kinetics simulations to predict and design the structure and properties of new materials, serving as a bridge between materials science theory and experimental practices. Theoretical and computational studies on self-healing coatings are instrumental in elucidating the micro-level mechanisms of self-healing, guiding the fabrication and performance optimization of these coatings, and significantly reducing costs. The research advancements in computational materials science within the domain of self-healing anti-corrosion coatings were reviewed. Various computational approaches employed in the study of extrinsic and intrinsic self-healing coating systems were presented, including density functional theory calculations, molecular dynamics simulations, Monte Carlo simulations, and finite element analysis. The computational simulation of intrinsic self-healing coatings primarily involves dynamic reversible covalent bonds (such as Diels-Alder reactions and disulfide bonds), dynamic reversible non-covalent bonds (such as hydrogen bonds), and shape memory functionalities. Computational simulations of extrinsic self-healing coatings mainly focus on film-forming substance types and corrosion inhibitor adsorption types. Simulations of intrinsic self-healing coatings largely rely on molecular dynamics simulations, while simulations for extrinsic self-healing anti-corrosion coatings are more often conducted by quantum chemical calculations and finite element analysis. The exchange reactions and self-healing processes of dynamic bonds, the rupture and healing agent release processes of microcapsules, and the adsorption modes and strengths of corrosion inhibitors on substrates can all be represented in the theoretical studies of self-healing anti-corrosion coatings. These simulation methods not only help understand the self-healing behavior of materials at the micro-level but also predict and optimize the macroscopic performance changes of the coatings during application. This review analyzes the strengths and limitations of these computational simulation methods in the application to self-healing coatings and provides theoretical backing for the development and application of self-healing anti-corrosion coatings. These methods combined with computational models from quantum scale to macroscale not only accurately simulate the internal structure and behavior of materials, but also predict the performance of coatings in practical applications. At present, the calculation simulation of self-healing coating stays on the thermodynamic properties, mechanical properties and self-healing properties of the substance, but the surface properties, adhesion properties and aging properties of the coating need to be evaluated and predicted, and the changes in the self-healing properties of the coating in a specific service environment will play a great role in the development of its simulation technology. Future endeavors will focus on the deeper integration of computational simulations, experimental research, artificial intelligence, and machine learning technologies, establishing a database for the self-healing performances of coatings, and facilitating more efficient material screening and design. This will enable breakthroughs in the research and application of self-healing coatings. |
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