Arjomand Nima, Mohamadi Mahboube, Kaklar Javad Alizadeh
Department of Mechanical Engineering, Faculty of Engineering, Urmia University, 57561-51818, Urmia, Iran.
Department of Polymer Engineering, Faculty of Engineering, Urmia University, 57561-51818, Urmia, Iran.
Sci Rep. 2025 Feb 28;15(1):7177. doi: 10.1038/s41598-025-90421-5.
This study aimed to develop a morphological-based model for predicting the Young's modulus and tensile strength of polymer blends with phase-separated structures. The analytical model employed the geometrical approach of the knotted and interconnected skeleton structural (KISS) model, incorporating morphological variation of immiscible polymer blends and the percolation thresholds of the components. The effect of the polymer/polymer interface on mechanical properties was accounted for by assuming a thin interfacial layer of specific thickness across the various morphological states. The prediction capability of the proposed model was evaluated using experimental data for iPP/PA, PP/PET, and LDPE/PP polymer blends, sourced from existing literature. The results established a reasonable accordance between the predicted and observed data. The model's predictions were also compared with those of established models for the tensile strength and Young's modulus of immiscible polymer blends, demonstrating its validity. Incorporating the interfacial region in the modeling procedure of mechanical properties represents a key distinguishing feature of the proposed model, enhancing its compatibility with the actual microstructure of polymer blends. Furthermore, the model's reliance on relatively simple mathematical calculations presents another crucial advantage.
本研究旨在开发一种基于形态学的模型,用于预测具有相分离结构的聚合物共混物的杨氏模量和拉伸强度。该分析模型采用了打结和互连骨架结构(KISS)模型的几何方法,纳入了不相容聚合物共混物的形态变化以及各组分的渗流阈值。通过假设在各种形态状态下存在特定厚度的薄界面层,考虑了聚合物/聚合物界面对力学性能的影响。使用从现有文献中获取的iPP/PA、PP/PET和LDPE/PP聚合物共混物的实验数据,评估了所提出模型的预测能力。结果表明预测数据与观测数据之间具有合理的一致性。该模型的预测结果还与已建立的不相容聚合物共混物拉伸强度和杨氏模量模型的预测结果进行了比较,证明了其有效性。在所提出的模型中,将界面区域纳入力学性能建模过程是一个关键的区别特征,增强了其与聚合物共混物实际微观结构的兼容性。此外,该模型依赖相对简单的数学计算是另一个关键优势。