Macromolecular Theory and Simulations最新文献

Conventional gelation theories have long faced challenges in accounting for the effects of cyclization, a persistent issue in polymerization kinetics. To address this, we introduce a kinetic framework focused on the evolution of structural units (monads), which balances mathematical simplicity with the retention of critical structural information. Analytical solutions for monad distribution functions (MDFs) are derived. A novel concept, polymer growth dimensionality (PGD, D_P) derived from MDF, is introduced to quantify monads' connectivity. By defining gelation at D_P = 1, the newly developed gelation criterion reproduces the classical gel points of Carothers and Flory–Stockmayer for various self- and cross-condensation systems. The identification of two additional crossover points at D_P = 2 and 3, corresponding to the formation of 2D and 3D network structures, respectively, represents an extension of conventional gelation theory. Furthermore, as primary cyclization reactions occur at the monad level, their impact on gelation can be theoretically quantified, addressing a longstanding challenge. Our monad-based PGD offers a simplified, parallel, and expanded toolkit for gelation analysis.

The injection molding process critically affects the mechanical reliability of 110 kV silicone rubber cable intermediate joints. In this study, a 3D geometric model based on the actual joint cross-section was established and applied to insert injection molding simulations. An orthogonal experimental design combined with range analysis and analysis of variance (ANOVA) was used to quantify the effects of key process parameters on flow behavior, viscosity, and volume shrinkage. The results show that melt temperature predominantly controls the flow front temperature and maximum viscosity, with contribution ratios of 87.89% and 75.72%, respectively, while mold surface temperature is the primary factor governing both maximum and average volume shrinkage rates, contributing up to 98.63%. At a significance level of 0.01, packing time is identified as the only parameter with a statistically significant effect on average volume shrinkage, and interaction analysis indicates that only the flow front temperature is sensitive to the interaction between melt and mold surface temperatures. A comprehensive evaluation using the entropy weight method yields an optimal parameter combination: mold surface temperature 470 K, melt temperature 285 K, packing pressure 80%, packing time 12 s, and curing time 30 s. These findings provide quantitative guidance for high-quality molding of high-voltage silicone rubber joints.

The melt flow index (MFI) is a fundamental indicator of polymer processability, directly related to molecular weight and melt viscosity, particularly during industrial granulation with twin-screw extruders. Conventional laboratory measurement of MFI is offline, time-consuming, and unsuitable for real-time quality control. To address this limitation, a machine-learning–based soft sensor was developed to predict MFI in-line using multivariate process-variable data from an industrial extrusion–granulation system. Key process parameters were identified based on field knowledge, followed by data preprocessing and feature engineering. Minority transition regions were augmented using the k-Nearest Neighbour Synthetic Minority Oversampling Technique (KNN-SMOTE), thereby improving model robustness in high-deviation regimes and across grade transition boundaries. Also, eXtreme Gradient Boosting (XGBoost) models, which employ an ensemble gradient-boosting framework, were evaluated via hyperparameter optimization. The results indicated that augmentation with smaller k values and lower thresholds for detecting high-deviation responses achieved superior performance, with an R² of 0.99 and an RMSE of 0.59. In addition, SHapley Additive exPlanations (SHAP) analysis confirmed model interpretability by identifying the dominant process relevant features and their consistent directional influence on MFI predictions. Overall, the proposed framework enables real-time monitoring of MFI in polyethylene granulation, reducing the frequency of laboratory testing and improving product quality control.

To design high performance of organic/inorganic thermoelectric composite materials by molecular simulation, composite materials (PPy/NG) are constructed by incorporating graphene (GE) modified with N atoms (NG) into the Polypyrrole (PPy) matrix. For different GE doping concentrations and different concentration N atoms modified in GE (mod-Ns), the thermoelectric properties of PPy/n-NG composite materials (n represents different N atoms modified concentration) is systematically investigated using the non-equilibrium molecular dynamics (NEMD) and density functional theory (DFT). It is found that N-modification on GE has a significant influence on the reduction on the thermal conductivity of composites with 7.06 wt.% graphene concentration. Moreover, when the mod-Ns concentration reached 3.66%, the thermal conductivity of the PPy/NG composite material is decreased by 49.19%. Additionally, the electron properties of PPy/n-NG are studied. It is found that the energy differences between the HOMO of n-NG and the LUMO of PPy decrease with the increase of mod-Ns concentration in NG. Overall, this study reveals that n-NG reduces the thermal conductivity of PPy/n-NG composites and promotes the transfer of electrons between n-NG and PPy. It establishes a theoretical foundation for designing high-performance organic/ inorganic thermoelectric materials.

Blade coating is a process used to provide a consistent liquid coating on a moving sheet, with significant applications in coated plastic industries, plastic polyvinyl chloride (PVC) fabrics, and paints. In this article, we studied the non-isothermal analysis of the blade coating process with non-linear slip effects at the blade surface. The material used to coating the substrate/web is characterized using the Yeleswarapu fluid model. The basic laws of fluid dynamics are implemented to modeled the 2D incompressible flow equations in the process of blade coating. The modeled equations are simplified with the help of normalized variables and the low Reynolds approximation theory. The simplified partial differential equations are solved numerically using a hybrid scheme, which is a combination of shooting and finite difference algorithms. The influence of the material parameters and slip coefficient on the engineering variables and flow characteristics are visualized with the help of various graphs and tables. The results reveal that the velocity of the coated substrate and coating thickness increase with increasing slip values compared to the Newtonian model. The temperature distribution rises with increasing Brinkman and Weissenberg numbers. The coating thickness decreases by 2.4% and the blade load increases by 49% relative to Newtonian values as the Weissenberg number increases.

The thermodynamic behaviors occur in either the process with one thermodynamic factor in the P, V, T, and S being constant or that with two thermodynamic factors being constant. The thermodynamic changes of P, V, T, and S in a specific process when two of the thermodynamic factors are constant were determined for polyethylene (PE) based on the intrinsic equation of state for various ensembles determined by the empirical P-V-T-S equation of state. The intrinsic thermodynamic behaviors of the internal energy, U, enthalpy, H, Helmholtz free energy, A, and Gibbs free energy, G, for PE in various specific processes when two thermodynamic factors, P, V, T and S, are constant were determined quantitatively based on the thermodynamic changes of P, V, T, and S in the specific process.