Macromolecular Theory and Simulations最新文献

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.

The graph diameter correlates with the mean-square radius of gyration and can be used to represent the 3D size of the polymer molecules. The graph diameter of the finite network polymer is investigated for both random and nonrandom architectures. By considering miniemulsion copolymerization of vinyl and divinyl monomers in Monte Carlo simulations, various types of networks are generated for investigation. The master curve relationship between the fraction d of diameter chain and cycle rank found earlier is revisited, which represents the maximum d of a randomly crosslinked homogeneous network for a given cycle rank. The relationship applies in a wide range of network maturity index NMI that is the number of cycle ranks per primary chain, approximately 3< NMI < 20. For the nonrandom inhomogeneous networks, the maximum d for a given cycle is f_d times the master curve, and the calibrated d/f_d curve follows the master curve. Larger NMI-values are needed for inhomogeneous networks to reach the maximum d, however, setting NMI larger than about 5 allows d to be larger than the corresponding random networks. Although the current study focuses on microgels, the design concept can also be applied to the synthesis of macrogels.

Coarse-graining (CG) is crucial for simulating polymers at extended scales, addressing the computational limitations of atomistic molecular dynamics. However, developing accurate and transferable CG force fields remains a formidable challenge, due to high-dimensional parameter spaces, conflicting objectives, and inherent noise. This review highlights Bayesian Optimization (BO) as a transformative, data-driven framework for automating the parameterization of CG force fields. BO leverages probabilistic Gaussian process surrogates and acquisition functions to navigate complex landscapes efficiently, minimizing expensive simulations while quantifying uncertainty. We survey BO's application in CG model development, from single- to multi-objective optimization for achieving structural, thermodynamic, and dynamic fidelity, and enhancing transferability across conditions. Key examples include CG models for electrolytes, block copolymers, and epoxy resins, often integrated with advanced machine learning techniques for learning potentials, optimal mappings, and active data acquisition. We also discuss emerging autonomous pipelines like SPACIER, RAPSIDY, CAMELOT, and PAL 2.0, which streamline the inverse design. Finally, we outline persistent challenges such as surrogate scalability, handling nonstationarity, and extending to reactive/multiscale systems, and envision BO as a cornerstone of future automated materials discovery, accelerating the design of novel polymeric materials.

Selective Laser Sintering (SLS) is an additive manufacturing method that enables the rapid and cost-effective production of complex polymer parts with high strength and dimensional accuracy. However, achieving these improved properties depends on laser processing parameters, which significantly influence melt behavior, melt pool properties, and the overall quality of the components. Because empirical approach is both time-consuming and cost-intensive, finite element method (FEM)-based simulations have become a popular field of study. However, existing simulation models are often oversimplified and inadequate to fully understand the multi-physics nature of SLS. To address this gap, a comprehensive 3D multi-physics finite element model was developed to simulate the SLS processing of Polyamide 12 powder. This study incorporates multiple interacting physical phenomena, such as heat transfer, fluid flow dynamics, melt-solidification kinetics, and phase transformations, to provide a realistic depiction of the transient thermal and fluid behavior of the SLS process. The simulation systematically examined the influence of laser process parameters on molten pool formation, temperature distribution, and overall sintering quality. The results show that insufficient volumetric energy density (VED) or high scan speeds resulted in incomplete melting and porosity, whereas excessive energy input promoted overheating and material degradation.