In-chain functionalized polymer induced assembly of nanoparticles: toward materials with tailored properties

Dispersion and Diffusion Mechanism of Nanofillers with Different Geometries in Bottlebrush Polymers: Insights from Molecular Dynamics Simulation

The dispersion and diffusion mechanism of nanofillers in polymer nanocomposites (PNCs) are crucial for understanding the properties of PNCs, which is of great significance for the design of novel materials. Herein, we investigate the dispersion and diffusion behavior of two geometries of nanofillers, namely, spherical nanoparticles (SNPs) and nanorods (NRs), in bottlebrush polymers by utilizing coarse-grained molecular dynamics simulations. With the increase of the interaction strength between the nanofiller and polymer (ε_np), both the SNPs and NRs experience a typical "aggregated phase-dispersed phase-bridged phase" state transition in the bottlebrush polymer matrix. We evaluate the validity of the Stokes-Einstein (SE) equation for predicting the diffusion coefficient of nanofillers in bottlebrush polymers. The results demonstrate that the SE predictions are slightly larger than the simulated values for small SNP sizes because the local viscosity that is felt by small SNPs in the densely grafted bottlebrush polymer does not differ much from the macroscopic viscosity. The relative size of the length of the NRs (L) and the radius of gyration (R_g) of the bottlebrush polymer play a key role in the diffusion of NRs. In addition, we characterize the anisotropic diffusion of NRs to analyze their translational and rotational diffusion. The motion of NRs in the direction perpendicular to the end-to-end vector is more hindered, indicating that there is a strong coupling between the rotation of NRs and the motion of the polymer. The NR motion shows stronger anisotropic diffusion at short time scales because of the steric effects generated by side chains of the bottlebrush polymer. In general, our results provide a fundamental understanding of the dispersion of nanofillers and the microscopic mechanism of nanofiller diffusion in bottlebrush polymers.

Role of a nanoparticle network in polymer mechanical reinforcement: insights from molecular dynamics simulations

Fully understanding the mechanism by which nanoparticles (NPs) strengthen polymer matrices is crucial for fabricating high-performance polymer nanocomposites (PNCs). Herein, coarse-grained molecular dynamics simulations were adopted to explicitly investigate the reinforcing effect of a NP network. Our results revealed that increasing the NP-NP interactions induced the self-assembly of NPs into a three-dimensional (3D) network that reinforced the polymer matrix. The reinforcing mechanism of NP-NP interactions was quite different from that of NP-polymer interactions. The latter promoted the orientation of polymer chains to transfer the external stress, while the former distributed the stress throughout the NP network. This work revealed the mechanism by which the NP network reinforced the polymer matrix at the molecular level and also provided guidelines for developing high performance PNCs via interfacial modification.

Shear-induced parallel and transverse alignments of cylinders in thin films of diblock copolymers

Coarse-grained Langevin dynamics simulations were performed to investigate the alignment behavior of monolayer films of cylinder-forming diblock copolymers under steady shear, a structure of significant importance for many technical applications such as nanopatterning. The influences of shear conditions, the interactions involved in the films, and the initial morphology of the cylinder-forming phase were examined. Our results showed that above a critical shear rate, the cylinders can align either along the shearing direction or transverse (log-rolling) to the shearing direction depending on the relative strength between the interchain attraction in the cylinders (εAA) and the surface attraction of the confining walls with the film (εBW). To understand the underlying mechanism, the microscopic properties of the films under shear were systematically investigated. It was found that at low εAA/εBW, the majority blocks of the diblock polymer that are adsorbed on the confining walls prefer to move synchronously with the walls, inducing the cylinder-forming blocks to align along the flow direction. When εAA/εBW is above a threshold value, a strong attraction between the cylinder-forming blocks restrains their movement during shear, leading to the log-rolling motions of the cylinders. To predict the threshold εAA/εBW, we developed an approach based on equilibrium thermodynamics data and found good agreement with our shear simulations.

Supramolecular Gel-Templated In Situ Synthesis and Assembly of CdS Quantum Dots Gels

Although many studies have attempted to develop strategies for spontaneously organizing nanoparticles (NPs) into three-dimensional (3D) geometries, it remains a fascinating challenge. In this study, a method for in situ synthesis and self-assembly of a CdS quantum dots (QDs) gel using a Cd supramolecular gel as a scaffold was demonstrated. During the QDs formation process, the Cd ions that constituted the Cd gels served as the precursors of the CdS QDs, and the oleic acid (OA) that ligated with the Cd in the supramolecular gels was capped on the surface of the CdS QDs in the form of carboxylate. The OA-stabilized CdS QDs were in situ synthesized in the entangled self-assembled fibrillar networks (SAFIN) of the Cd gels through reactions between the gelator and H2S. As a result, the QDs exactly replicated the framework of the SAFIN in the CdS QD gels instead of simply assembling along the SAFIN of the supramolecular gels. Moreover, the CdS QDs showed extraordinary sensitivity in the fluorescence detection of IO4- anions. The facile one-step method developed here is a new approach to assembling nanostructured materials into 3D architectures and has general implications for the design of low molecular mass gelators to bring desired functionality to the developed supramolecular gels.

Self-Assembly of Block Copolymer Chains To Promote the Dispersion of Nanoparticles in Polymer Nanocomposites

In this paper we adopt molecular dynamics simulations to study the amphiphilic AB block copolymer (BCP) mediated nanoparticle (NP) dispersion in polymer nanocomposites (PNCs), with the A-block being compatible with the NPs and the B-block being miscible with the polymer matrix. The effects of the number and components of BCP, as well as the interaction strength between A-block and NPs on the spatial organization of NPs, are explored. We find that the increase of the fraction of the A-block brings different dispersion effect to NPs than that of B-block. We also find that the best dispersion state of the NPs occurs in the case of a moderate interaction strength between the A-block and the NPs. Meanwhile, the stress-strain behavior is probed. Our simulation results verify that adopting BCP is an effective way to adjust the dispersion of NPs in the polymer matrix, further to manipulate the mechanical properties.

Neural-Network-Biased Genetic Algorithms for Materials Design: Evolutionary Algorithms That Learn

Machine learning has the potential to dramatically accelerate high-throughput approaches to materials design, as demonstrated by successes in biomolecular design and hard materials design. However, in the search for new soft materials exhibiting properties and performance beyond those previously achieved, machine learning approaches are frequently limited by two shortcomings. First, because they are intrinsically interpolative, they are better suited to the optimization of properties within the known range of accessible behavior than to the discovery of new materials with extremal behavior. Second, they require large pre-existing data sets, which are frequently unavailable and prohibitively expensive to produce. Here we describe a new strategy, the neural-network-biased genetic algorithm (NBGA), for combining genetic algorithms, machine learning, and high-throughput computation or experiment to discover materials with extremal properties in the absence of pre-existing data. Within this strategy, predictions from a progressively constructed artificial neural network are employed to bias the evolution of a genetic algorithm, with fitness evaluations performed via direct simulation or experiment. In effect, this strategy gives the evolutionary algorithm the ability to "learn" and draw inferences from its experience to accelerate the evolutionary process. We test this algorithm against several standard optimization problems and polymer design problems and demonstrate that it matches and typically exceeds the efficiency and reproducibility of standard approaches including a direct-evaluation genetic algorithm and a neural-network-evaluated genetic algorithm. The success of this algorithm in a range of test problems indicates that the NBGA provides a robust strategy for employing informatics-accelerated high-throughput methods to accelerate materials design in the absence of pre-existing data.

Tailoring the alignment of string-like nanoparticle assemblies in a functionalized polymer matrix via steady shear

This work reports the steady shear induced aligning behaviour of nanoparticle strings in a functionalized polymer matrix.