Iranian Journal of Chemical Engineering (IJChE)

Abstract In this study, a novel strategy was devised to evaluate the mechanical properties of semi-crystalline polymer nanocomposites. To this end, the Halpin-Tsai model was comprehensively improved to also include the impact of dispersion quality, nanoparticle aggregation/agglomeration, the formation of the polymer/particle interphase region and induced/self-crystallinity. Besides enhancing the tensile modulus of the system, a particular fraction of either dispersed or clustered nanoparticle domains was considered to participate in the induced crystallization process (active domains). The content of active domains was estimated using the Gutzow-Dobreva theory or a specifically designed method based on the requirements of the system. On the contrary, the so-called inactive domains were assumed to remain distributed within the polymer matrix and contribute to the system response mechanism. The mechanical features of the interphase region were defined using the De Gennes’s self-similar carpet theory considering the molecular characteristics of the polymer phase. Moreover, the porosity index and bonding nature between aggregated or agglomerated nanoparticles were used to estimate the tensile modulus of the clustered doamins. The model validation process was conducted via benchmarking its predictions against the obtained data from the literature, which showed acceptable agreement and proved the model to be a quite accurate interpretation tool.

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 In this study, the main purpose has been to investigate the behavior of the nanoparticles with different structures and similar based materials in polymer nanocomposites. To this end, different samples, containing PS as the matrix, and layered graphene oxide (GO) and/or hollow graphene oxide nanoparticles (HGO), were prepared via the melt mixing process and were subjected to heat conduction and tensile tests. To evaluate all features of the interaction between the polymer phase and the nanoparticles, a thermal/mechanical analytical model was proposed and the results were used to simulate the behavior of specific geometrical structures, corresponding to the real samples, under different thermal/mechanical conditions. The results showed good agreement between the obtained experimental data and simulation/analytical model interpretations. In addition, it was found that the HGO nanoparticle had such a good performance in enhancing the thermal and mechanical properties of the nanocomposite, due to its unique structure.

Abstract In this study, a comprehensive model was proposed in order to predict the tensile strength of nanocomposites considering the effects of the random orientation of nanoparticles, interphase properties and also the inevitable aggregation/agglomeration phenomenon. The model was structured based on the nanoparticle shape (e. g. platelet, cylindrical and spherical) so it could perfectly demonstrate the random orientation. It was revealed that neglecting the aggregation/agglomeration of the nanoparticles ( ) drastically rises the prediction error to about 12% while considering decreased the error to about 7-9% for samples with low content of nanoparticles (< 0.5 Vol.). Moreover, the model was completely capable of characterizing the polymer/particle interphase regardless of the polymer type and the nanoparticles shape. Model verification was accomplished by comparing its predictions with the tensile test results of the prepared nanocomposite samples of different compositions (Polystyrene/Silica, Polystyrene/Graphene Oxide and Polystyrene/Carbon nano-tubes). Also, the samples were subjected to TEM in order to qualitatively evaluate the behavior of the different shaped nanoparticles in the polystyrene matrix.

Abstract In this work, hydrophobic silica aerogels were synthesized using sol-gel method and drying at ambient pressure. The surface morphology, pore size, and the presence of functional groups on the surface of the nanoparticles were analyzed using FE-SEM, TGA, FT-IR, and EDX, respectively. After calcination at 500 °C, the hydrophilic property of the adsorbents was evaluated by water contact angle measurements. The calcinated silica aerogels were used for adsorption of nitrate from aqueous solution in both batch and continuous processes. In the batch process, the effect of initial nitrate concentration, contact time, pH level, and adsorbent dosage were investigated. Results showed that the nitrate removal percentage increased with the decrement of the pH level and the initial nitrate concentration. On the other hand, increasing the contact time and the adsorbent dosage resulted in higher removal percentage. Accordingly, process optimization resulted in a nitrate removal of 92.2 %. Furthermore, it was found that the equilibrium results were in agreement with the Langmuir isotherm model better than with the Freundlich model and also the adsorption kinetics followed the pseudo-second-order model. In the continuous process, the effects of the input flow rate, the bed height, and the initial nitrate concentration were investigated.