Macromolecular Theory and Simulations最新文献

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.

The current investigation reports the rheological implications on thin film production with magnetized hybrid nanofluid during the calendering process. Hybrid nanofluids are reported to have better rheological characteristics, improved mechanism of heat transfer in liquid, viscosity modification, and thermal conductivity enhancement as related to the usual unitary nanofluid. Lubrication Approximation Theory (LAT) is applied to simplify the respective system of non-dimensional equations and solved by employing analytical as well as numerical approaches to find velocity, temperature, pressure gradient, exiting film thickness, pressure, and other mechanical quantities. The graphical illustrations are thoroughly explained by providing physical reasoning for the obtained variations. Hybrid nanoparticle-based molten polymer modifies the fluid viscosity, enhancing pressure gradient and temperature distribution. The relation between film attachment and detachment point also varies under hybrid nanoparticle volume fraction and Hartmann number. Engineering quantities like separating force and power input are enhanced due to hybrid nanoparticles because of higher fluid viscosity and pressure distribution, but an opposite trend is detected due to MHD. The current investigation focuses on the rheology and controlling factors for the heat transfer mechanism and film thickness, without extensive experimentation to save project cost and precious time. It also helps improve product performance and quality in industrial thin film applications.

The adsorption behavior of a polymer chain within a slit composed of two patch-patterned surfaces is investigated using Langevin dynamics simulations. Each surface exhibits periodic attractive square patches (period d, size L, attraction energy ε_ps), with a 0.5d misalignment between the two surfaces. The adsorption degree increases with L, while the mean number of occupied patches <n_pa> and the surface-parallel component of the mean square radius of gyration <R²_g,xy> exhibit a complex dependence on L. We identify distinct adsorption regimes: a single-surface and single-patch adsorbed state for L > L_s, an upper-lower and multi-patch adsorbed state for L > L_m, and a complete adsorbed state for L > L_c. At weak ε_ps, the polymer chain adopts single-surface and single-patch adsorption for middle L and upper-lower and multi-patch adsorption for large L. Conversely, a strong ε_ps promotes upper-lower and multi-patch adsorption for small L, and single-surface and single-patch adsorption as L increases. The adsorption degree increases monotonically to saturate with ε_ps, while <n_p> initially increases and subsequently decays toward stability. Notably, <R²_g,xy> first decreases, then increases, and finally decreases to stabilize as ε_ps increases. These results highlight the role of patch geometry and interaction strength in governing polymer adsorption behavior.