Iranian Journal of Chemical Engineering (IJChE)

Abstract Polymer nanocomposites reinforced with multi-walled carbon nanotubes (MWCNTs) offer promising mechanical performance; however, predicting their tensile modulus remains challenging due to the complex interplay of multiple factors such as filler content, functionalization, and interphase quality. In this study, a dataset of 229 samples was compiled from the literature, augmented via cubic spline interpolation to 4,933 training points, and analyzed using six machine learning models, including SVR, Random Forest, Gradient Boosting Regressor, XGBoost, KNN, and Artificial Neural Networks (ANNs). The inclusion of the interphase modulus (Ei), calculated via an extended Ji model, proved critical for improving prediction accuracy. Among all models, Gradient Boosting Regressor and XGBoost achieved the best predictive performance (Test R² = 0.9868 and 0.9837, respectively), while ANN demonstrated competitive accuracy (Test R² = 0.9703) but higher sensitivity under cross-validation (Mean CV R² = 0.7486). Feature importance analysis using SHAP further confirmed the significant contribution of Ei to prediction outcomes. Overall, this work demonstrates that incorporating physically-informed features like interphase modulus, combined with robust machine learning pipelines, can substantially enhance the predictive modeling of nanocomposite mechanical properties, providing a valuable tool for material design and optimization.

Abstract Morphological and thermal properties of PA6/NBR nanocomposites prepared through a direct melt mixing process in an internal mixer were studied. The effects of the NBR content (10, 30, and 50%) and nanoclay loading (3, 5, and 7%) on the microstructure properties of nanocomposites have been reported and compared with PA6/NBR blends as well. The thermoplastic elastomer (TPE) nanocomposites were characterized by X-ray diffraction (XRD), transmission electron microscopy (TEM), scanning electron microscopy (SEM), volume swelling in oil, differential scanning calorimeter (DSC) and dynamic mechanical thermal analysis (DMTA). XRD results show that Cloisite 30B is exfoliated into the PA6 and NBR. TEM image of the PA6/NBR/nanoclay composite confirms partial exfoliated structure of silicate layers dispersed into the both NBR and PA6 phases. The SEM photomicrograph of PA6/NBR nanocomposite shows an increasing of the rubber particles size in comparison with unfilled PA6/NBR TPE. By the presence of nanoclay, improved oil resistances of the prepared TPE nanocomposites were achieved. DSC studies show that loading of the nanoclay reduces the degree of crystallinity of the nanocomposite samples. The DMTA test shows that storage modulus of the PA6/NBR nanocomposite increases in comparison with the PA6/NBR blend. It also explains a reduction in damping by loading of the nanoclay.