Macromolecular Theory and Simulations最新文献

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.

The effect of the broadening of the molecular weight distribution (MWD) in a continuous operation in an HCSTR accompanied by an increased tendency to form gel caused by branching and crosslinking reactions is well known. A more detailed understanding of this phenomenon can be gained from the age distribution. This new concept, introduced here, is a generalization of the residence time distribution and describes the age of individual monomeric units of a polymer chain, which is the time elapsed since the unit entered the reactor as a monomer. It is shown that in living and step growth polymerization the branching density depends on the length of the polymer chain. In living polymerization, it is a linear function of chain length whereas in step growth polymerization it follows a square root function. This leads to high molecular weight tailing and the formation of very inhomogeneous polymer molecules or networks. The onset of gelation is much earlier than in case of an uniform distribution of branch points. The concept of the age distribution is very general and applies to general reaction systems. In a reactor with recycles, the time a chemical species spends in the reactor depends on the reaction path.