Effect of Nanorod Physical Roughness on the Aggregation and Percolation of Nanorods in Polymer Nanocomposites

BMSCs-laden mechanically reinforced bioactive sodium alginate composite hydrogel microspheres for minimally invasive bone repair

Minimally invasive, efficient, and satisfactory treatment for irregular and lacunar bone defects is still a challenge. Alginate hydrogels serve as promising stem cell (SC) delivery systems for bone regeneration but are limited by low cellular viability, poor osteogenic differentiation, and insufficient mechanical support. Herein, we developed a BMSCs-laden mechanically reinforced bioactive sodium alginate composite hydrogel microspheres (BCHMs) system via a microfluidic method that possesses 1) a uniform size and good injectability to meet clinical bone defects with complex shapes, 2) high cellular viability maintenance and further osteogenic induction capacity, and 3) improved mechanical properties. As the main matrix, the sodium alginate hydrogel maintains the high viability of encapsulated BMSCs and efficient substance exchange. Enhanced mechanical properties and osteogenic differentiation of the BCHMs in vitro were observed with xonotlite (Ca6Si6O17(OH)2, CSH) nanowires incorporated. Furthermore, BCHMs with 12.5 % CSH were injected into rat femoral bone defects, and satisfactory in situ regeneration outcomes were observed. Overall, it is believed that BCHMs expand the application of polysaccharide science and provide a promising injectable bone substitute for minimally invasive bone repair.

Depletion Strategies for Crystallized Layers of Two-Dimensional Nanosheets to Enhance Lithium-Ion Conductivity in Polymer Nanocomposites

The assembly of long-range aligned structures of two-dimensional nanosheets (2DNSs) in polymer nanocomposites (PNCs) is in urgent need for the design of nanoelectronics and lightweight energy-storage materials of high conductivity for electricity or heat. These 2DNS are thin and exhibit thermal fluctuations, leading to an intricate interplay with polymers in which entropic effects can be exploited to facilitate a range of different assemblies. In molecular dynamics simulations of experimentally studied 2DNSs, we show that the layer-forming crystallization of 2DNSs is programmable by regulating the strengths and ranges of polymer-induced entropic depletion attractions between pairs of 2DNSs, as well as between single 2DNSs and a substrate surface, by exclusively tuning the temperature and size of the 2DNS. Enhancing the temperature supports the 2DNS-substrate depletion rather than crystallization of 2DNSs in the bulk, leading to crystallized layers of 2DNSs on the substrate surfaces. On the other hand, the interaction range of the 2DNS-2DNS depletion attraction extends further than the 2DNS-substrate attraction whenever the 2DNS size is well above the correlation length of the polymers, which results in a nonmonotonic dependence of the crystallization layer on the 2DNS size. It is demonstrated that the depletion-tuned crystallization layers of 2DNSs contribute to a conductive channel in which individual lithium ions (Li ions) migrate efficiently through the PNCs. This work provides statistical and dynamical insights into the balance between the 2DNS-2DNS and 2DNS-substrate depletion interactions in polymer-2DNS composites and highlights the possibilities to exploit depletion strategies in order to engineer crystallization processes of 2DNSs and thus to control electrical conductivity.

Molecular dynamics simulation of the impact of the surface topology of carbon black on the mechanical properties of elastomer nanocomposites

Carbon black has always played a pivotal role in reinforcing elastomers because it remarkably improves the mechanical properties. The reinforcing effect of carbon black is influenced by its grades, which mainly depend on the difference in the structure of the carbon black particles. Despite many traditional experiments on the performance of carbon black composites, there has been less emphasis on reinforcement mechanisms due to the challenges associated with unraveling the intermolecular interactions. In this paper, a coarse grained molecular dynamics simulation was employed to examine the relationship between the morphology of the carbon black particles and the mechanical properties of the elastomer nanocomposites. Specifically, three different morphological carbon black nanoparticle models, including the smooth particle model, rough particle model, and the rough ellipsoid model, were constructed first. We then focused on investigating the changes of the mechanical properties by systematically varying the filling fraction of the carbon black particles, and the strength of the interfacial interaction between the filler and the rubber. The results indicated that the surface roughness and the filler's shape had a significant impact on the mechanical properties of the filled rubber models. The mechanical enhancement effect of the rough ellipsoidal carbon black is around 50-400% higher than that of the smooth carbon black, and the stronger the interfacial interactions, the more pronounced the enhancement. In addition, the rough ellipsoid filled system has low hysteresis, low permanent deformation, and high fatigue resistance. In general, this work explores the strengthening mechanism of carbon black on the elastomer at the molecular level and generates new insight into the design and fabrication of novel reinforcing fillers.

Dispersion and orientation patterns in nanorod-infused polymer melts

Introducing nanorods into a polymeric matrix can enhance the physical and mechanical properties of the resulting material. In this paper, we focus on understanding the dispersion and orientation patterns of nanorods in an unentangled polymer melt, particularly as a function of nanorod concentration, using molecular dynamics simulations. The system is comprised of flexible polymer chains and multi-thread nanorods that are equilibrated in the NPT ensemble. All interactions are purely repulsive except for those between polymers and rods. Results with attractive vs repulsive polymer-rod interactions are compared and contrasted. The concentration of rods has a direct impact on the phase behavior of the system. At lower concentrations, rods phase separate into nematic clusters, whereas at higher concentrations more isotropic and less structured rod configurations are observed. A detailed examination of the conformation of the polymer chains near the rod surface shows extension of the chains along the director of the rods (especially within clusters). The dispersion and orientation of the nanorods are a result of the competition between depletion entropic forces responsible for the formation of rod clusters, the enthalpic effects that improve mixing of rods and polymer, and entropic losses of polymers interpenetrating rod clusters.

Deep learning potential of mean force between polymer grafted nanoparticles

Grafting polymer chains on the surfaces of nanoparticles is a well-known route to control their self-assembly and distribution in a polymer matrix. A wide variety of self-assembled structures are achieved by changing the grafting patterns on the surface of an individual nanoparticle. However, an accurate estimation of the effective potential of mean force between a pair of grafted nanoparticles that determines their assembly and distribution in a polymer matrix is an outstanding challenge in nanoscience. We address this problem via deep learning. As a proof of concept, here we report a deep learning framework that learns the interaction between a pair of single-chain grafted spherical nanoparticles from their molecular dynamics trajectory. Subsequently, we carry out the deep learning potential of mean force-based molecular simulation that predicts the self-assembly of a large number of single-chain grafted nanoparticles into various anisotropic superstructures, including percolating networks and bilayers depending on the nanoparticle concentration in three-dimensions. The deep learning potential of mean force-predicted self-assembled superstructures are consistent with the actual superstructures of single-chain polymer grafted spherical nanoparticles. This deep learning framework is very generic and extensible to more complex systems including multiple-chain grafted nanoparticles. We expect that this deep learning approach will accelerate the characterization and prediction of the self-assembly and phase behaviour of polymer-grafted and unfunctionalized nanoparticles in free space or a polymer matrix.

Force-driven active dynamics of thin nanorods in unentangled polymer melts

Recent advances in the functional material and biomedical applications of nanorods call for a fundamental understanding of the active motion of nanorods in a viscoelastic medium. Molecular dynamics simulations are performed to investigate a model system consisting of force-driven active thin nanorods in a melt of unentangled polymers. The activeness of a thin nanorod arises from a constant external force applied uniformly along the rod. The simulations demonstrate that the active force overcomes the randomness of the diffusive motion and results in a ballistic motion along the direction of the applied force at long timescales. The constant speed of the force-driven ballistic motion is determined by the balance of the active force and the friction from the coupling of the nanorod with the polymer viscosity. The friction coefficient, which is computed as the ratio of the active force and the speed, decreases as the active force increases. The origin of the reduction in the friction coefficient is the high speed that allows the nanorod to renew its local environment faster than the relaxation time of melt chains. A scaling theory is developed to quantify the dependence of the friction coefficient on the strength of the active force. The simulations also demonstrate that the force-driven ballistic motion suppresses the rotational diffusion of the rod and cuts off the de-correlation of the rod axis with time. On the scaling level, the long-time trajectory of a force-driven active nanorod piercing through unentangled polymers may be described as a stretched array of "active blobs", where the short-time random-walk trajectory within an active blob is unperturbed by the active force.