Erickson Meade, Casañola-Martin Gerardo, Han Yulun, Rasulev Bakhtiyor, Kilin Dmitri
Department of Coatings and Polymeric Materials, North Dakota State University, Fargo, North Dakota 58105, United States.
Department of Chemistry and Biochemistry, North Dakota State University, Fargo, North Dakota 58105, United States.
J Phys Chem B. 2024 Mar 7;128(9):2190-2200. doi: 10.1021/acs.jpcb.3c06854. Epub 2024 Feb 22.
The development of reusable polymeric materials inspires an attempt to combine renewable biomass with upcycling to form a biorenewable closed system. It has been reported that 2,5-furandicarboxylic acid (FDCA) can be recovered for recycling when incorporated as monomers into photodegradable polymeric systems. Here, we develop a procedure to better understand the photodegradation reactions combining density functional theory (DFT) based time-dependent excited-state molecular dynamics (TDESMD) studies with machine learning-based quantitative structure-activity relationships (QSAR) methodology. This procedure allows for the unveiling of hidden structural features between active orbitals that affect the rate of photodegradation and is coined InfoTDESMD. Findings show that electrotopological features are influential factors affecting the rate of photodegradation in differing environments. Additionally, statistical validations and knowledge-based analysis of descriptors are conducted to further understand the structural features' influence on the rate of photodegradation of polymeric materials.
可重复使用聚合物材料的发展激发了一种将可再生生物质与升级回收相结合以形成生物可再生封闭系统的尝试。据报道，当2,5-呋喃二甲酸（FDCA）作为单体掺入光降解聚合物体系时，可以回收用于再循环。在此，我们开发了一种程序，将基于密度泛函理论（DFT）的含时激发态分子动力学（TDESMD）研究与基于机器学习的定量构效关系（QSAR）方法相结合，以更好地理解光降解反应。该程序能够揭示影响光降解速率的活性轨道之间隐藏的结构特征，被称为InfoTDESMD。研究结果表明，电子拓扑特征是影响不同环境中光降解速率的重要因素。此外，还进行了描述符的统计验证和基于知识的分析，以进一步了解结构特征对聚合物材料光降解速率的影响。
