Crystallization of finite-extensible nonlinear elastic Lennard-Jones coarse-grained polymers

Metaparticles: Computationally engineered nanomaterials with tunable and responsive properties

In simulations, particles are traditionally treated as rigid platforms with variable sizes, shapes, and interaction parameters. While this representation is applicable for rigid core platforms, particles consisting of soft platforms (e.g., micelles, polymers, elastomers, and lipids) inevitably deform upon application of external stress. We introduce a generic model for flexible particles, which we refer to as MetaParticles (MPs). These particles have tunable properties, can respond to applied tension, and can deform. A MP is represented as a collection of Lennard-Jones beads interconnected by spring-like potentials. We model a series of MPs of variable sizes and symmetries, which we subject to external stress, followed by relaxation upon stress release. The positions and the orientations of the individual beads are propagated by Brownian dynamics. The simulations show that the mechanical properties of the MPs vary with size, bead arrangement, and area of applied stress, and share an elastomer-like response to applied stress. Furthermore, MPs deform following different mechanisms, i.e., small MPs change shape in one step, while larger ones follow a multi-step deformation pathway, with internal rearrangements of the beads. This model is the first step toward the development and understanding of particles with adaptable properties with applications in the biomedical field and in the design of bioinspired metamaterials.

Local and Global Order in Dense Packings of Semi-Flexible Polymers of Hard Spheres

The local and global order in dense packings of linear, semi-flexible polymers of tangent hard spheres are studied by employing extensive Monte Carlo simulations at increasing volume fractions. The chain stiffness is controlled by a tunable harmonic potential for the bending angle, whose intensity dictates the rigidity of the polymer backbone as a function of the bending constant and equilibrium angle. The studied angles range between acute and obtuse ones, reaching the limit of rod-like polymers. We analyze how the packing density and chain stiffness affect the chains' ability to self-organize at the local and global levels. The former corresponds to crystallinity, as quantified by the Characteristic Crystallographic Element (CCE) norm descriptor, while the latter is computed through the scalar orientational order parameter. In all cases, we identify the critical volume fraction for the phase transition and gauge the established crystal morphologies, developing a complete phase diagram as a function of packing density and equilibrium bending angle. A plethora of structures are obtained, ranging between random hexagonal closed packed morphologies of mixed character and almost perfect face centered cubic (FCC) and hexagonal close-packed (HCP) crystals at the level of monomers, and nematic mesophases, with prolate and oblate mesogens at the level of chains. For rod-like chains, a delay is observed between the establishment of the long-range nematic order and crystallization as a function of the packing density, while for right-angle chains, both transitions are synchronized. A comparison is also provided against the analogous packings of monomeric and fully flexible chains of hard spheres.

Controlling the two components modified on nanoparticles to construct nanomaterials

Nanoparticle self-assembly technology has made great progress in the past 30 years. Many kinds of self-assembly strategies of modifiable nanoparticles have been developed and used to construct nano-aggregates by designing the shape, size and type of nanoparticles and controlling the components modified on nanoparticles. These strategies are widely used in many fields, such as medical diagnosis, biological detection, drug delivery, materials synthesis and sensors. The modified components can be DNA chains, polymer chains, proteins, and even organic molecules based on different molecular conformations and chemical properties. In recent years, the self-assembly of two-component modified nanoparticles has gradually attracted more attention. Nanoparticles modified with two components of different DNA strands can self-assemble to produce a variety of nano arrangement structures, such as BCC, FCC and other cubic crystals, which can be used in crystal materials. Two-component modification of hydrophilic and hydrophobic polymers can produce vesicular aggregates, which can be used for drug delivery. In this review, we summarize the latest experimental progress and theoretical simulation of self-assembly of two-component modified nanoparticles including different DNA chains, different polymer chains, DNA and polymer chains, proteins and polymer chains, and different organic molecules. Their self-assembly characteristics and application prospects were discussed. Compared with single-component modified nanoparticles, two-component nanoparticles have different tethered molecules or molecular chains, which can be multifunctional by regulating different modified components and types of nanoparticles and ultimately expand the scope of applications.

Chiral selecting crystallization of helical polymers: A molecular dynamics simulation for the POM-like bare helix

Polymer crystallization has long been a fascinating problem and is still attracting many researchers. Most of the previous simulations are concentrated on clarifying the universal aspects of polymer crystallization using model linear polymers such as polyethylene. We are recently focusing on a nearly untouched but very interesting problem of chiral selecting crystallization in helical polymers. We previously proposed a stepwise approach using two kinds of helical polymers, simple "bare" helical polymers made of backbone atoms only such as polyoxymethylene (POM) and "general" helical polymers containing complicated side groups such as isotactic polypropylene (iPP). We have already reported on the crystallization in oligomeric POM-like helix but have observed only weak chiral selectivity during crystallization. In the present paper, we investigate the crystallization of sufficiently long POM-like polymer both from the isotropic melt and from the highly stretched melt. We find in both cases that the polymer shows a clear chiral selecting crystallization. Especially the observation of a single crystal growing from the isotropic melt is very illuminating. It shows that the crystal thickness and the crystal chirality is closely correlated; thicker crystals show definite chirality while thinner ones are mostly mixtures of the R- and the L- handed stems. The single crystal is found to have a marked lenticular shape, where the thinner growth front, since being made of the mixture, shows no chiral selectivity. Final chiral crystal is found to be completed through helix reversal processes within thicker regions.

Polymer crystallization under external flow

The general aspects of polymer crystallization under external flow, i.e., flow-induced crystallization (FIC) from fundamental theoretical background to multi-scale characterization and modeling results are presented. FIC is crucial for modern polymer processing, such as blowing, casting, and injection modeling, as two-third of daily-used polymers is crystalline, and nearly all of them need to be processed before final applications. For academics, the FIC is intrinsically far from equilibrium, where the polymer crystallization behavior is different from that in quiescent conditions. The continuous investigation of crystallization contributes to a better understanding on the general non-equilibrium ordering in condensed physics. In the current review, the general theories related to polymer nucleation under flow (FIN) were summarized first as a preliminary knowledge. Various theories and models, i.e., coil-stretch transition and entropy reduction model, are briefly presented together with the modified versions. Subsequently, the multi-step ordering process of FIC is discussed in detail, including chain extension, conformational ordering, density fluctuation, and final perfection of the polymer crystalline. These achievements for a thorough understanding of the fundamental basis of FIC benefit from the development of various hyphenated rheometer, i.e., rheo-optical spectroscopy, rheo-IR, and rheo-x-ray scattering. The selected experimental results are introduced to present efforts on elucidating the multi-step and hierarchical structure transition during FIC. Then, the multi-scale modeling methods are summarized, including micro/meso scale simulation and macroscopic continuum modeling. At last, we briefly describe our personal opinions related to the future directions of this field, aiming to ultimately establish the unified theory of FIC and promote building of the more applicable models in the polymer processing.

Integration of Machine Learning and Coarse-Grained Molecular Simulations for Polymer Materials: Physical Understandings and Molecular Design

In recent years, the synthesis of monomer sequence-defined polymers has expanded into broad-spectrum applications in biomedical, chemical, and materials science fields. Pursuing the characterization and inverse design of these polymer systems requires our fundamental understanding not only at the individual monomer level, but also considering the chain scales, such as polymer configuration, self-assembly, and phase separation. However, our accessibility to this field is still rudimentary due to the limitations of traditional design approaches, the complexity of chemical space along with the burdened cost and time issues that prevent us from unveiling the underlying monomer sequence-structure-property relationships. Fortunately, thanks to the recent advancements in molecular dynamics simulations and machine learning (ML) algorithms, the bottlenecks in the tasks of establishing the structure-function correlation of the polymer chains can be overcome. In this review, we will discuss the applications of the integration between ML techniques and coarse-grained molecular dynamics (CGMD) simulations to solve the current issues in polymer science at the chain level. In particular, we focus on the case studies in three important topics-polymeric configuration characterization, feed-forward property prediction, and inverse design-in which CGMD simulations are leveraged to generate training datasets to develop ML-based surrogate models for specific polymer systems and designs. By doing so, this computational hybridization allows us to well establish the monomer sequence-functional behavior relationship of the polymers as well as guide us toward the best polymer chain candidates for the inverse design in undiscovered chemical space with reasonable computational cost and time. Even though there are still limitations and challenges ahead in this field, we finally conclude that this CGMD/ML integration is very promising, not only in the attempt of bridging the monomeric and macroscopic characterizations of polymer materials, but also enabling further tailored designs for sequence-specific polymers with superior properties in many practical applications.

Strain induced crystallization of polymers at and above the crystallization temperature by coarse-grained simulations

We examine the behavior of short and long polymers by means of coarse-grained computer simulations of a by-polyvinyl alcohol inspired model. In particular, we focus on the structural changes in the monomer and polymer scales during cooling and the application of uni-axial true strain. The straining of long polymers results in the formation of a semi-crystalline system at temperatures well above the crystallization temperature, which allows for the study of strain induced crystallization.

Crystallization of semiflexible polymers in melts and solutions

We studied the crystallization of semiflexible polymer chains in melts and poor-solvent solutions with different concentrations using dissipative particle dynamics (DPD) computer simulation techniques. We used the coarse-grained polymer model to reveal the general principles and microscopic scenario of crystallization in such systems at large time and length scales. It covers both primary and secondary nucleation as well as crystallites' merging. The parameters of the DPD model were chosen appropriately to reproduce the entanglements of polymer chains. We started from an initial homogeneous disordered solution of Gaussian chains and observed the initial stages of crystallization process caused in our model by orientational ordering of polymer chains and polymer-solvent phase separation. We found that the overall crystalline fraction at the end of the crystallization process decreases with the increasing polymer volume fraction while the steady-state crystallization speed at later stages does not depend on the polymer volume fraction. The average crystallite size has a maximal value in the systems with a polymer volume fraction from 0.7 to 0.95. In our model, these polymer concentrations represent an optimal value in the sense of balance between the amount of polymer material available to increase the crystallite size and chain entanglements, that prevent crystallites' growth and merging. On large time scales, our model allows us to observe lamellar thickening linear in logarithmic time scale.

Development of Coarse-Grained Models for Poly(4-vinylphenol) and Poly(2-vinylpyridine): Polymer Chemistries with Hydrogen Bonding

In this paper, we identify the modifications needed in a recently developed generic coarse-grained (CG) model that captured directional interactions in polymers to specifically represent two exemplary hydrogen bonding polymer chemistries-poly(4-vinylphenol) and poly(2-vinylpyridine). We use atomistically observed monomer-level structures (e.g., bond, angle and torsion distribution) and chain structures (e.g., end-to-end distance distribution and persistence length) of poly(4-vinylphenol) and poly(2-vinylpyridine) in an explicitly represented good solvent (tetrahydrofuran) to identify the appropriate modifications in the generic CG model in implicit solvent. For both chemistries, the modified CG model is developed based on atomistic simulations of a single 24-mer chain. This modified CG model is then used to simulate longer (36-mer) and shorter (18-mer and 12-mer) chain lengths and compared against the corresponding atomistic simulation results. We find that with one to two simple modifications (e.g., incorporating intra-chain attraction, torsional constraint) to the generic CG model, we are able to reproduce atomistically observed bond, angle and torsion distributions, persistence length, and end-to-end distance distribution for chain lengths ranging from 12 to 36 monomers. We also show that this modified CG model, meant to reproduce atomistic structure, does not reproduce atomistically observed chain relaxation and hydrogen bond dynamics, as expected. Simulations with the modified CG model have significantly faster chain relaxation than atomistic simulations and slower decorrelation of formed hydrogen bonds than in atomistic simulations, with no apparent dependence on chain length.

Property Decoupling across the Embryonic Nucleus-Melt Interface during Polymer Crystal Nucleation

Spatial distributions are presented that quantitatively capture how polymer properties (e.g., segment alignment, density, and potential energy) vary with distance from nascent polymer crystals (nuclei) in prototypical polyethylene melts. It is revealed that the spatial extent of nuclei and their interfaces is metric-dependent as is the extent to which nucleus interiors are solid-like. As distance from a nucleus increases, some properties, such as density, decay to melt-like behavior more rapidly than polymer segment alignment, indicating that a polymer nucleus resides in a nematic-like droplet. This nematic-like droplet region coincides with enhanced formation of ordered polymer segments that are not part of the nucleus. It is more favorable to find nonconstituent ordered polymer segments near a nucleus than in the surrounding metastable melt, pointing to the possibility of one nucleus inducing the formation of other nuclei. In this vein, there is also a second region of enhanced ordering that lies along the nematic director of a nucleus, but beyond its nematic droplet and fold regions. These results indicate that crystal stacking, a key characteristic of lamellae in semicrystalline polymeric materials, begins to emerge during the earliest stages of polymer crystallization (i.e., crystal nucleation). More generally, the findings of this study provide a conceptual bridge between polymer crystal nucleation under nonflow and flow conditions and are used to rationalize previous results.

Monodisperse Polymer Melts Crystallize via Structurally Polydisperse Nanoscale Clusters: Insights from Polyethylene

This study demonstrates that monodisperse entangled polymer melts crystallize via the formation of nanoscale nascent polymer crystals (i.e., nuclei) that exhibit substantial variability in terms of their constituent crystalline polymer chain segments (stems). More specifically, large-scale coarse-grain molecular simulations are used to quantify the evolution of stem length distributions and their properties during the formation of polymer nuclei in supercooled prototypical polyethylene melts. Stems can adopt a range of lengths within an individual nucleus (e.g., ∼1-10 nm) while two nuclei of comparable size can have markedly different stem distributions. As such, the attainment of chemically monodisperse polymer specimens is not sufficient to achieve physical uniformity and consistency. Furthermore, stem length distributions and their evolution indicate that polymer crystal nucleation (i.e., the initial emergence of a nascent crystal) is phenomenologically distinct from crystal growth. These results highlight that the tailoring of polymeric materials requires strategies for controlling polymer crystal nucleation and growth at the nanoscale.

Disentangling and Lamellar Thickening of Linear Polymers during Crystallization: Simulation of Bimodal and Unimodal Molecular Weight Distribution Systems

We have performed coarse-grained molecular dynamics simulations to study the isothermal crystallization of bimodal and unimodal molecular weight distribution (MWD) polymers with equivalent average molecular weight (M_w). By using primitive path analysis, we can monitor the entanglement evolution during the process of crystallization. We have discovered a quantitative correlation between the degree of disentanglement and crystallinity, indicating that chain disentanglement permits the process of crystallization. In addition, the crystalline stem length also displays a linear relation with the degree of disentanglement at different temperatures. Based on the observation in our simulations, we can build a scenario of the whole process of chain disentangling and lamellar thickening on the basis of chain sliding diffusion. Furthermore, we have enough evidence to infer that the temperature dependence of crystalline stem length is basically a result of temperature dependence of chain sliding diffusion. Our observations are also in agreement with Hikosaka's sliding diffusion theory. Compared to the unimodal system, the disentanglement degree of the bimodal system is more delayed than its crystallinity due to the slower chain sliding of the long-chain component; the bimodal system reaches a larger crystalline stem length at all temperatures due to the promotion of higher chain sliding mobility of the short-chain component.