Macromolecular Theory and Simulations最新文献

Neutron spin echo spectroscopy of entangled polymer melts [M. Zamponi, et al. J. Phys. Chem. B 2008, 112, 16220], and of tracer diffusion of short polymer chains in highly entangled polymer melt [M. Zamponi et al. Phys. Rev. Lett. 2021, 126, 187801.] and [M. Kruteva et al. Macromolecules 2021, 54, 11384] found the center-of-mass mean-square displacements at shorter times are subdiffusive, heterogeneous, non-Gaussian, and cooperative. These properties contradict the assumption of reptation within the tube in the tube-reptation (TR) model, but are in accord with the predictions from the many-chain cooperative dynamics in the theory of Guenza. The inadequacy of the TR model revealed by the microscopic experiments and theory motivates the author to reexamine previously published data of diffusion of entangled polymer chains from experiments and simulations used to test the TR model. The results reported in this study lead to the conclusion that the key predictions of the TR model are at variance with experimental and simulation data. The cause lies in the reptation hypothesis contradicting the cooperative nature of entangled chain diffusion proven by its dynamics being isomorphic to cooperative diffusion in other materials. The Coupling Model has predictions consistent with the cooperative diffusion properties in interacting materials [Prog. Mater. Sci., 2023, 139, 101130.]. Applied to the entangled polymers, the predictions successfully explain the data, especially those contradicting the TR model. Thus, diffusion of entangled polymer chains is a cooperative many-chain process in having the universal properties of many-body cooperative diffusion established in many other interacting materials, and the reptation hypothesis is unwarranted.

The double quench phase separation is a simplified type of continuous cooling process that is widely seen in industrial processes for polymeric membrane formation. Uncommon quenching conditions can lead to the creation of novel membrane microstructures. This study aims to theoretically investigate the impact of nonisothermality on the morphology formation during the double-quench thermally-induced phase separation process. First, quench is employed during different stages of phase separation to observe the possibility of secondary morphology formation. Next, two initial quench temperatures are selected, one shallow and the other deep. The initial solution temperature and the secondary quench temperature are kept constant to inspect the impact of the initial quench temperature on the structure formation. Lastly, the results of the secondary quench are compared with and without employing the enthalpy of demixing. Results verified that the stage of phase separation, the initial and secondary quench temperatures, cooling rate, and the secondary quench composition are the most important parameters in the the nonisothermal double quench phase separation process. The morphology should be well-developed in order for the secondary structure formation. In addition, it is shown that heat generation during demixing in the primary and secondary quenches significantly influences the secondary morphology formation.

Swelling experiments are conducted on nonfiller natural rubber using four solvents (toluene, cyclohexane, tetrahydrofuran (THF), and methylethylketone (MEK)) over temperatures from 10 to 70 °C. Toluene, cyclohexane, and THF, classified as effective solvents, show swelling ratios between 3 and 7, influenced by the crosslink density of the rubber. MEK, however, has a lower ratio of 1.5 to 2. Temperature has a minor impact on swelling compared to the crosslink density. The study evaluates the Extended Modified Double Lattice (EMDL) model for its mixing contribution in polymer network swelling, aiming to improve the Flory–Hüggins (FH) model. The superiority of EMDL above FH is in the boundary condition at the unvulcanized state, the former aligning its interaction energy with values from solvent activities in primary linear polymer/solvent solutions, unlike the FH model. The EMDL model also accounts for oriented interactions in polar solvents through a secondary lattice, linking specific interaction energy with solvent dipole moments. The study observes a nonlinear correlation between crosslinking density and sulfur amount, proposing a nonrandom mixing at lower sulfur concentrations. This model shows strong alignment with experimental data, suggesting that replacing the FH model's mixing contribution with the EMDL model could improve results with minimal additional complexity.

The coating process is widely used in various industries to enhance the production quality and efficiency. This study gives a comprehensive analysis of non-isothermal blade coating of non-Newtonian nanofluid incorporating magnetic, thermophoresis, and Brownian effects. The mathematical equations derived from mass, momentum, and energy conservation laws are initially streamlined by means of lubrication approximation theory (LAT). Subsequently, these dimensionless equations are solved in dimensionless form numerically using fourth order Runge–Kutta and Newton–Raphson methods. This study includes the effects of the slip parameter, magnetohydrodynamic (MHD) and other material parameters on the coating thickness ($�h_1$), blade load, velocity, temperature, concentration, and pressure profiles through graphs and tables. The velocity of molten polymer increases near the substrate while it decreases near the blade surface as the slip parameter increases. The temperature distribution increases as the Brinkman number rises, with the maximum temperature occurring in the nip region of the flow. The coating thickness and load-carrying force for both plane and exponential coater increase with higher values of the magnetohydrodynamic (MHD) parameter.

Statistical learning is employed to present a principled framework for the establishment of quantitative structure-property relationships (QSPR). Property predictions of industrial polymers formed by multiple reagents and at varying molecular weights are focused. A theoretical description of QSPR as well as a rigorous mathematical method is developed for the assimilation of experimental data. Results show that these methods can perform exceptionally well at establishing QSPR for glass transition temperature and intrinsic viscosity of polyesters.

According to recent reviews and experiments, some key dynamic properties of cyclic polymers from neutron spin echo spectroscopy, molecular dynamics simulations, and rheological measurements are at variance with the predictions from theories based on motions restricted by fixed obstacles. These dynamic properties including non-Gaussianity, heterogeneity, and subdiffusive center of mass mean square displacements turn out to be hallmarks of cooperative dynamics found in entangled linear polymers, and in other many-units interacting systems that are not polymers. The current situation suggests new theory emphasizing that cooperative many-chain dynamics is needed to explain the properties. The Coupling Model is such a theory. Its predictions are applied to the dynamic properties of cyclic polymers here to show consistency with experiments and simulations.

The effect of spatial inhomogeneity on the dimensions of network polymers is investigated by using model networks containing highly crosslinked domains. It is found that the dimensions of network architecture consisting of densely crosslinked domains connected by long chains are larger than those of loosely crosslinked domains connected by short chains, given the cycle rank is the same. The cases with the domains connected by the domains are also investigated. In all cases, the dimensions are larger than the corresponding randomly crosslinked homogeneous networks. This is because the loosely crosslinked regions dominate the dimensions of network polymers. The master curve relationship found for the statistical networks is applicable also for the present types of spatial inhomogeneous network polymers when the cycle rank is increased to make the network well-developed in a homologous series of networks.

Spatial gradient materials occupy an important research position in the field of functional materials with their unique porous structure. Gradient changes in pore size and density distribution have received extensive attention in the fields of biomimetic and smart materials. The gradient transition law is mathematically related to the driving force of isomerization reaction and component phase separation. In this study, a dissipative particle dynamics simulation is used to introduce photoisomerization reactions into the system. Lambert's law is used to construct a reaction model for the variation of light intensity with irradiation depth, and a gradient structure with a spatial transition law is obtained. The effects of the extinction coefficient ε, the initial reaction probability Pr₀, and the interactions α(A,B) between the isomerized molecules as well as the viscosity on the formation of the gradient structure are investigated in detail. Furthermore, the mathematical proportionality between the size of the phase region and interfacial energy of the two phases is elucidated. This study provides preliminary computational insights into the factors affecting the photoinduced phase separation process of polymeric gradient materials. It may help to develop effective strategies to improve the phase separation and properties of polymer gradient materials in subsequent studies.

The conformation of macromolecules attached to a surface is influenced by both their excluded volume and steric forces. Here, self-avoiding random walk simulations are used to evaluate the occurrence of various conformations as a function of the number of monomeric units to estimate the effect of conformational entropy of a tethered chain. Then, a more realistic scenario is assessed, which can more accurately reproduce the shape of a tethered macromolecule. The simulations presented here confirm that it is more likely for a polymer to undergo a collapse conformation rather than a stretched one, as a collapse conformation can be realized in more different ways. Also, they confirm the “mushroom” shape of polymers close to a surface. From this simple approach, the conformation entropy of a model 100-unit polymer close to a surface is estimated to contribute with over 129 $��k_B�T�$ toward its collapse. This conformation entropy is higher than that of typical hydrogen bonds and even barriers that keep proteins folded. As such, entropic collapse of macromolecules plays an important role in realizing the mushroom shape of attached polymers and can be the driving force in protein folding, while the polypeptide chain emerges from the ribosome.

The macroscopic properties of polymer nanocomposites (PNC) rely largely on the interphase between the polymer chains and the filler particles. One significant difficulty to solve this issue is to quantitatively model the structure-property correlations due to the interfacial region in these complex materials. While dielectric spectroscopy (DS) measurements are routinely used to characterize the effective permittivity of filled polymers, fitting standard effective medium models and mixing equations to these data remains notoriously difficult to interpret. This is due to the absence of explicit reference to internal length scales characterizing the interfaces in the PNC. As an illustrative example, a two-level homogenization framework is proposed which enables the extraction of useful information on the impact of a thin interphase confined on a nanometer length scale based on broadband DS data. This model leads to new ways of tuning the interphase so as to optimize the material's response to electric field, a situation relevant for electromagnetic shielding. This approach provides guidance on how to observe directly and experimentally the actual properties of the interface between the phases (as opposed to model-based inference). Aside from its secure physical foundation in the theory of effective medium, a significant advantage of this approach is that a genetic algorithm (GA) technique applied to this physics-based model enables the uniqueness of the fit parameters to be considered, as the GA method is robust in terms of finding globally optimum solutions, therefore placing confidence in non-universal values of the percolation exponents. Recent work in physics-informed machine learning indicates that the effective dielectric properties of PNC with many degrees of freedom due to their complex morphology can be described by considering only a few degrees of freedom describing the interface features between the phases in these composites.