Bamane Swapnil S, Deshpande Prathamesh P, Patil Sagar U, Maiaru Marianna, Odegard Gregory M
Michigan Technological University, Houghton, Michigan 49931, United States.
Columbia University, New York, New York 10027, United States.
J Phys Chem C Nanomater Interfaces. 2024 Sep 10;128(37):15639-15648. doi: 10.1021/acs.jpcc.4c04061. eCollection 2024 Sep 19.
Elium-based thermoplastic composites are a key material for future use in the marine, wind energy, and automotive industries because of their recyclability and ease of manufacture. To optimize the processing of the Elium composites to yield optimal structural properties, computational process modeling can be used to relate processing parameters to residual stresses and material durability. The key ingredient for reliable and accurate process modeling is the evolution of physical, thermal, and mechanical properties during polymerization. The objective of this study is to use molecular dynamics to predict the mass density, bulk modulus, shear modulus, Young's modulus, Poisson's ratio, glass transition temperature, and coefficient of thermal expansion as a function of the extent of reaction of the polymer. The predicted properties compare favorably to the experimentally measured values in the fully polymerized state. This data set of properties provides needed input data for process modeling of Elium-based composites for process parameter optimization and improved durability and performance.
基于Elium的热塑性复合材料因其可回收性和易于制造,是未来在海洋、风能和汽车行业中使用的关键材料。为了优化Elium复合材料的加工以获得最佳结构性能,可使用计算过程建模将加工参数与残余应力和材料耐久性联系起来。可靠且准确的过程建模的关键要素是聚合过程中物理、热和机械性能的演变。本研究的目的是使用分子动力学来预测质量密度、体积模量、剪切模量、杨氏模量、泊松比、玻璃化转变温度和热膨胀系数作为聚合物反应程度的函数。预测的性能与完全聚合状态下的实验测量值相比具有优势。该性能数据集为基于Elium的复合材料的过程建模提供了所需的输入数据,以优化过程参数并提高耐久性和性能。