Molecular simulation of polymer crystallization under chain and space confinement

Molecular dynamics simulations of single polyethylene chain folding during fast quenching using all-atom and united-atom models

Molecular dynamics simulations have been employed to investigate the folding behavior of a single linear polyethylene (PE) chain containing 1000 backbone carbon atoms under fast quenching based on all-atom and united-atom models. The single-chain folding characteristics were studied in detail for six different force fields by analyzing the evolution of chain conformations, folded structure characterisation, free energy and crystallisation. The results show that the all-trans chain undergoes a similar two-stage chain collapse mechanism during isothermal relaxation at T = 500 K, transitioning from local collapse to global collapse into a molten globule state under different force fields. During fast quenching at 100 K ns-1, the molten globule of all-atom model transitions into a folded, significantly anisotropic ordered structure under AMBER-AA or OPLS-AA force fields, while that of the united-atom model remains unchanged in its globular structure. The chain crystallization evolution indicates that the single chain folds into ordered lamellar structures with higher crystallinity under AMBER-AA and OPLS-AA force fields. In contrast, under the other four force fields, the single chain remains in a stable amorphous state.

Crystallization-dictated assembly of block copolymers and nanoparticles under three-dimensional confinement

Crystallization-dictated self-assembly of crystalline block copolymers (BCPs) in solution has been utilized to produce many impressive nanostructures. However, when the assembly of crystalline BCPs happens in a three-dimensional (3D) confined space, predicting the self-assembly structure of BCPs becomes challenging due to the competition between crystallization and microphase separation. In this feature article, we summarize the recent progress in the self-assembly of crystalline BCPs under confinement, emphasizing the impact of crystallization behavior on the assembly structure. Furthermore, we highlight the crystallization-directed assembly of inorganic nanoparticles (NPs), either by pre-assembling crystalline polymers as templates or using crystalline polymer chain segments as ligands. By exploring the impact of crystallization behavior on the assembled structure of BCPs and NPs, it is helpful to predict and manipulate the properties of polymer/nanoparticle composites, thereby enabling the precise design of polymer metamaterials.

Helical insertion of polyphenylene chains into confined cylindrical slits composed of two carbon nanotubes

The helical insertion behavior of poly(para-phenylene) (PP) chains into confined cylindrical slits constructed by two carbon nanotubes (CNTs) with different diameters is studied by molecular dynamics simulations. The contribution of system energy and each energy component to helical self-assembly is discussed to further explain the conditions, driving force and mechanism. The width and length of the slit, the diameter of the outer tube and the temperature have a great impact on the helical insertion of PP chains. Two equations are proposed to confirm the diameter and the distances between the PP helix and the inner and outer walls of the given CNTs. The helical self-assembly of PP with different numbers of chains inserted into the slits is further studied. This study has a great benefit in understanding the conformational behavior of polymers, even biological macromolecules in confinements.

The Effect of Mechanical Elongation on the Thermal Conductivity of Amorphous and Semicrystalline Thermoplastic Polyimides: Atomistic Simulations

Over the past few decades, the enhancement of polymer thermal conductivity has attracted considerable attention in the scientific community due to its potential for the development of new thermal interface materials (TIM) for both electronic and electrical devices. The mechanical elongation of polymers may be considered as an appropriate tool for the improvement of heat transport through polymers without the necessary addition of nanofillers. Polyimides (PIs) in particular have some of the best thermal, dielectric, and mechanical properties, as well as radiation and chemical resistance. They can therefore be used as polymer binders in TIM without compromising their dielectric properties. In the present study, the effects of uniaxial deformation on the thermal conductivity of thermoplastic PIs were examined for the first time using atomistic computer simulations. We believe that this approach will be important for the development of thermal interface materials based on thermoplastic PIs with improved thermal conductivity properties. Current research has focused on the analysis of three thermoplastic PIs: two semicrystalline, namely BPDA-P3 and R-BAPB; and one amorphous, ULTEM^TM. To evaluate the impact of uniaxial deformation on the thermal conductivity, samples of these PIs were deformed up to 200% at a temperature of 600 K, slightly above the melting temperatures of BPDA-P3 and R-BAPB. The thermal conductivity coefficients of these PIs increased in the glassy state and above the glass transition point. Notably, some improvement in the thermal conductivity of the amorphous polyimide ULTEM^TM was achieved. Our study demonstrates that the thermal conductivity coefficient is anisotropic in different directions with respect to the deformation axis and shows a significant increase in both semicrystalline and amorphous PIs in the direction parallel to the deformation. Both types of structural ordering (self-ordering of semicrystalline PI and mechanical elongation) led to the same significant increase in thermal conductivity coefficient.

Estimating the Relative Crystallinity of Biodegradable Polylactic Acid and Polyglycolide Polymer Composites by Machine Learning Methodologies

Biodegradable polymers have recently found significant applications in pharmaceutics processing and drug release/delivery. Composites based on poly (L-lactic acid) (PLLA) have been suggested to enhance the crystallization rate and relative crystallinity of pure PLLA polymers. Despite the large amount of experimental research that has taken place to date, the theoretical aspects of relative crystallinity have not been comprehensively investigated. Therefore, this research uses machine learning methods to estimate the relative crystallinity of biodegradable PLLA/PGA (polyglycolide) composites. Six different artificial intelligent classes were employed to estimate the relative crystallinity of PLLA/PGA polymer composites as a function of crystallization time, temperature, and PGA content. Cumulatively, 1510 machine learning topologies, including 200 multilayer perceptron neural networks, 200 cascade feedforward neural networks (CFFNN), 160 recurrent neural networks, 800 adaptive neuro-fuzzy inference systems, and 150 least-squares support vector regressions, were developed, and their prediction accuracy compared. The modeling results show that a single hidden layer CFFNN with 9 neurons is the most accurate method for estimating 431 experimentally measured datasets. This model predicts an experimental database with an average absolute percentage difference of 8.84%, root mean squared errors of 4.67%, and correlation coefficient (R²) of 0.999008. The modeling results and relevancy studies show that relative crystallinity increases based on the PGA content and crystallization time. Furthermore, the effect of temperature on relative crystallinity is too complex to be easily explained.