Origin of particle clustering in a simulated polymer nanocomposite and its impact on rheology

Advancing elastomer performance with dynamic bond networks in polymer-grafted single-chain nanoparticles: a molecular dynamics exploration

This research introduces a method to enhance the mechanical properties of elastomers by grafting polymer chains onto single-chain flexible nanoparticles (SCNPs) and incorporating dynamic functional groups. Drawing on developments in grafting polymers onto hard nanoparticle fillers, this method employs the distinct flexibility of SCNPs to diminish heterogeneity and enhance core size control. We use molecular dynamics (MD) simulations for a mesoscale analysis of structural properties, particularly the effects of dynamic functional group quantities and their distribution. The findings demonstrate that increased quantities of functional groups are correlated with enhanced mechanical strength and toughness, showing improved stress-strain responses and energy dissipation capabilities. Moreover, the uniformity in the distribution of these functional groups is crucial, promoting a more cohesive and stable dynamic bonding network. The insights gained from MD simulations not only advance our understanding of the microstructural control necessary for optimizing macroscopic properties, but also provide valuable guidance for the design and engineering of advanced polymer nanocomposites, thereby enhancing the material performance through strategic molecular design.

Approaches and Perspective of Coarse-Grained Modeling and Simulation for Polymer-Nanoparticle Hybrid Systems

Molecular modeling and simulations have emerged as effective and indispensable tools to characterize polymeric systems. They provide fundamental and essential insights to design a product of the required properties and to improve the understanding of a phenomenon at the molecular level for a particular system. The polymer-nanoparticle hybrids are materials with outstanding properties and correspondingly large applications whose study has benefited from this new paradigm. However, despite the significant expansion of modern day computational powers, investigation of the long time and large length scale phenomenon in polymeric and polymer-nanoparticle systems is still a challenging task to complete through all-atom molecular dynamics (AA-MD) simulations. To circumvent this problem, a variety of coarse-grained (CG) models have been proposed, ranging from the generic CG models for qualitative properties predictions to more realistic chemically specific CG models for quantitative properties predictions. These CG models have already delivered some success stories in the study of several spatial and temporal evolutions of many processes. Some of these studies were beyond the feasibility of traditional atomistic resolution models due to either the size or the time constraints. This review captures the different types of popular CG approaches that are utilized in the investigation of the microscopic behavior of polymer-nanoparticle hybrid systems. The rationale of this article is to furnish an overview of the popular CG approaches and their applications, to review several important and most recent developments, and to delineate the perspectives on future directions in the field.

Synthesis, characterisation and thermo-physical properties of highly stable graphene oxide-based aqueous nanofluids for potential low-temperature direct absorption solar applications

Two types of highly stable 0.1% graphene oxide-based aqueous nanofluids were synthesised and investigated. The first nanofluid (GO) was prepared under the influence of ultrasonic irradiation without surfactant. The second nanofluid was treated with tetra ethyl ammonium hydroxide to reduce the graphene oxide to form reduced graphene oxide (RGO) during ultrasonic irradiation. The GO and RGO powders were characterised by various techniques such as field emission scanning electron microscopy, transmission electron microscopy, X-ray diffraction and Raman. Also UV–visible absorption spectroscopy was carried out and band gap energies were determined. Optical band gap energies for indirect transitions ranged from 3.4 to 4.4 eV and for direct transitions they ranged between 2.2 and 3.7 eV. Thermal conductivity measurements of the GO-based aqueous nanofluid revealed an enhancement of 9.5% at 40 °C compared to pure water, while the RGO-based aqueous nanofluid at 40 °C had a value 9.23% lower than pure water. Furthermore, the photothermal response of the RGO-based aqueous nanofluid had a temperature increase of 13.5 °C, (enhancement of 60.2%) compared to pure water, the GO-based aqueous nanofluid only displayed a temperature rise of 10.9 °C, (enhancement of 46.6%) after 20 min exposure to a solar irradiance of 1000 W m⁻². Both nanofluid types displayed good long-term stability, with the GO-based aqueous nanofluid having a zeta potential of 30.3 mV and the RGO-based aqueous nanofluid having a value of 47.6 mV after 6 months. The good dispersion stability and photothermal performance makes both nanofluid types very promising working fluids for low-temperature direct absorption solar collectors.

Detecting bound polymer layers in attractive polymer-nanoparticle hybrids

When polymer-nanoparticle (NP) attractions are sufficiently strong, a bound polymer layer with a distinct dynamic signature spontaneously forms at the NP interface. A similar phenomenon occurs near a fixed attractive substrate for thin polymer films. While our previous simulations fixed the NPs to examine the dilute limit, here, we allow the NP to move. Our goal is to investigate how NP mobility affects the signature of the bound layer. For small NPs that are relatively mobile, the bound layer is slaved to the motion of the NP, and the signature of the bound layer relaxation in the intermediate scattering function essentially disappears. The slow relaxation of the bound layer can be recovered when the scattering function is measured in the NP reference frame, but this process would be challenging to implement in experimental systems with multiple NPs. Instead, we use the counterintuitive result that the NP mass affects its mobility in the nanoscale limit, along with the more expected result that the bound layer increases the effective NP mass, to suggest that the signature of the bound polymer manifests as a change in NP diffusivity. These findings allow us to rationalize and quantitatively understand the results of recent experiments focused on measuring NP diffusivity with either physically adsorbed or chemically end-grafted chains.

Analysis on Microstructure-Property Linkages of Filled Rubber Using Machine Learning and Molecular Dynamics Simulations

A better understanding of the microstructure–property relationship can be achieved by sampling and analyzing a microstructure leading to a desired material property. During the simulation of filled rubber, this approach includes extracting common aggregates from a complex filler morphology consisting of hundreds of filler particles. However, a method for extracting a core structure that determines the rubber mechanical properties has not been established yet. In this study, we analyzed complex filler morphologies that generated extremely high stress using two machine learning techniques. First, filler morphology was quantified by persistent homology and then vectorized using persistence image as the input data. After that, a binary classification model involving logistic regression analysis was developed by training a dataset consisting of the vectorized morphology and stress-based class. The filler aggregates contributing to the desired mechanical properties were extracted based on the trained regression coefficients. Second, a convolutional neural network was employed to establish a classification model by training a dataset containing the imaged filler morphology and class. The aggregates strongly contributing to stress generation were extracted by a kernel. The aggregates extracted by both models were compared, and their shapes and distributions producing high stress levels were discussed. Finally, we confirmed the effects of the extracted aggregates on the mechanical property, namely the validity of the proposed method for extracting stress-contributing fillers, by performing coarse-grained molecular dynamics simulations.

Thermoplastic bio-nanocomposites: From measurement of fundamental properties to practical application

Although the discovery of plastic has revolutionized materials used in many industries and by consumers, their non-biodegradable nature has led to one of the greatest problems of our times: plastic waste in the environment. Bioplastics which are biobased and biodegradable, have been suggested as alternatives for their fossil based counterparts, but their properties often do not meet the requirements that standard plastics would, and are in clear need of improvement. One way to do so is by the addition of nanoparticles which, when homogeneously dispersed, have been reported to result in great improvements. However, in practice, homogenous distribution of nanoparticles is not that trivial due to their tendency to aggregate, also after addition to the polymer matrix. Although theoretical frameworks to prevent this process are available, we feel that the options explored in practice are often rather trial and error in nature. For that reason, we review the theories available, aiming to facilitate the design of the nanocomposites for a sustainable future. We first discuss thermodynamic frameworks which revolve around nanoparticle aggregation. To minimize nanoparticle aggregation, the nanoparticle and polymer can be selected in such a way that they have similar polar and dispersive surface energies. The second part is dedicated to nanocomposite processing, where kinetic effects act on the nanocomposite material therewith influencing its final morphology, although it is good to point out that other factors such as reaggregation also affect the final nanocomposite morphology. The third section is dedicated to how nanoparticles affect the polymer matrix to which they are added. We describe how interactions at an atomic scale, result in the formation of an interphasial region which ultimately leads to changed bulk material properties. From these three sections, we conclude that three parameters are often overlooked when designing nanocomposites, namely the surface energies of the nanoparticles and polymers, the aggregation bond energy or strength, and the interphase region. Therefore, in the fourth section we provide an overview of techniques to identify these three parameters. We finish with a summery and outlook for the design of bio nanocomposites, where we bring all insights from the previous four sections together.

Local Structural Fingerprints of Nanoparticle-Bound Polymer Layers

The molecular structure of bound layers at attractive polymer-nanoparticle interfaces strongly influences the properties of nanocomposites. Thus, a unifying theoretical framework that can provide insights into the correlations between the molecular structure of the bound layers, their thermodynamics, and macroscopic properties is highly desirable. In this work, molecular dynamics simulations were used in combination with local fingerprint analysis of configurational entropy and interaction energy at the segmental scale, with the goal to establish such physical grounds. The thickness of bound polymer layers is found to be independent of the polymer chain length, as deduced from density oscillations at the surface of a nanotube. The local configurational entropy of layers is estimated from pair correlations in equilibrium structures. By plotting mean layer entropy vs internal energy on a phase diagram, a one-to-one equivalence is established between the local structures of layers and their thermodynamic properties. Moreover, a gradient in local dynamics of segments in bound layers under equilibrium is observed normal to the nanoparticle surface. The relaxation times of individual layers show correspondence to their phase diagram fingerprints, thus suggesting that a unified perspective can be envisioned for such materials built on the grounds of locally heterogeneous interfaces.

Assembly of Polymer-Grafted Nanoparticles in Polymer Matrices

Leibler pioneered the idea that long enough matrix polymers of length P will spontaneously dewet a chemically identical polymer layer, comprising chains of length N, densely end-grafted to a flat surface ("brush"). This entropically driven idea is routinely used to explain experiments in which 10-20 nm diameter nanoparticles (NPs) densely grafted with polymer chains are found to phase separate from chemically identical melts for P/N ≳4. At lower grafting densities, these effects are also thought to underpin the self-assembly of grafted NPs into a variety of structures. To explore the validity of this picture, we conducted large-scale molecular dynamics simulations of grafted NPs in a chemically identical polymer melt. For the NPs we consider, in the ≈5 nm diameter range, we find no phase separation even for P/N = 10 in the absence of attractions. Instead, we find behavior that more closely parallels experiments when all of the chain monomers are equally attractive to each other but repel the NPs. Our results thus imply that experimental situations investigated to date are dominated by the surfactancy of the NPs, which is driven by the chemical mismatch between the inorganic core and the organic ligands (the graft and free chains are chemically identical). Entropic effects, that is, the translational entropy of the NPs and the matrix, the entropy of mixing of the grafts and the matrix, and the conformational entropy of the chains appear to thus have a second-order effect even in the context of these model systems.

Synthesis of computer simulation and machine learning for achieving the best material properties of filled rubber

Molecular dynamics (MD) simulation is used to analyze the mechanical properties of polymerized and nanoscale filled rubber. Unfortunately, the computation time for a simulation can require several months’ computing power, because the interactions of thousands of filler particles must be calculated. To alleviate this problem, we introduce a surrogate convolutional neural network model to achieve faster and more accurate predictions. The major difficulty when employing machine-learning-based surrogate models is the shortage of training data, contributing to the huge simulation costs. To derive a highly accurate surrogate model using only a small amount of training data, we increase the number of training instances by dividing the large-scale simulation results into 3D images of middle-scale filler morphologies and corresponding regional stresses. The images include fringe regions to reflect the influence of the filler constituents outside the core regions. The resultant surrogate model provides higher prediction accuracy than that trained only by images of the entire region. Afterwards, we extract the fillers that dominate the mechanical properties using the surrogate model and we confirm their validity using MD.

Creep attenuation in glassy polymer nanocomposites with variable polymer-nanoparticle interactions

The use of nanoparticle reinforced polymer matrices in continuous fiber composites for infrastructure applications requires a comprehensive understanding of viscoelastic creep. Critical parameters affecting the mechanical reinforcement offered by nanoparticles include nanoparticle size and concentration, as well as the interaction between the nanoparticle surface and polymer matrix. Here, we study the viscoelastic creep of nanocomposite systems comprised of glassy thermoplastic polymers and spherical silica nanoparticles of varying sizes and surface functionalization using a dynamic mechanical analysis (DMA) accelerated testing methodology. Significant differences in the nanoparticle dispersions in these nanocomposites were observed via transmission electron microscopy (TEM) and small-angle X-ray scattering (SAXS) and are attributed to differences in the polymer-polymer and polymer-particle interaction strengths. The DMA measurements indicate a decrease in compliance at short times with increased nanoparticle loading that is largely independent of nanoparticle dispersion morphology and polymer-particle interaction strength. Conversely, long term creep behavior shows a much stronger dependence on these parameters with the creep onset time increasing by up to three orders of magnitude. For similar nanoparticle loadings, the time to critical deformation in systems with well-distributed, networked nanoparticle morphologies was larger by an order of magnitude compared to systems exhibiting strong nanoparticle aggregation. The networked systems delayed the time to critical deformation by three orders of magnitude over that of neat polymer. The increase in time to critical deformation is also greater in composites with smaller nanoparticles at similar loadings, which we attribute to the development of percolated nanoparticle networks. These results demonstrate the significant effects polymer-particle interactions and dispersion morphologies can have on the long-term creep compliance of thermoplastic nanocomposites.

Numerical Simulation of the Rheological Behavior of Nanoparticulate Suspensions

Nanoparticles significantly alter the rheological properties of a polymer or monomeric resin with major effect on the further processing of the materials. In this matter, especially the influence of particle material and disperse properties on the viscosity is not yet understood fully, but can only be modelled to some extent empirically after extensive experimental effort. In this paper, a numerical study on an uncured monomeric epoxy resin, which is filled with boehmite nanoparticles, is presented to elucidate the working principles, which govern the rheological behavior of nanoparticulate suspensions and to simulate the suspension viscosity based on assessable material and system properties. To account for the effect of particle surface forces and hydrodynamic interactions on the rheological behavior, a resolved CFD is coupled with DEM. It can be shown that the particle interactions caused by surface forces induce velocity differences between the particles and their surrounding fluid, which result in increased drag forces and cause the additional energy dissipation during shearing. The paper points out the limits of the used simulation method and presents a correction technique with respect to the Péclet number, which broadens the range of applicability. Valuable information is gained for a future mechanistic modelling of nanoparticulate suspension viscosity by elucidating the interdependency between surface forces, shear rate and resulting drag forces on the particles.

Engineering interfacial entropic effects to generate giant viscosity changes in nanoparticle embedded polymer thin films

Thin polymer and polymer nanocomposite (PNC) films are being extensively used as advanced functional coating materials in various technological applications. Since it is widely known that various properties of these thin films, especially their thermo-mechanical behavior, can be considerably different from the bulk depending on the thickness as well as interaction with surrounding media, it is imperative to study these properties directly on the films. However, quite often, it becomes difficult to perform these measurements reliably due to a dearth of techniques, especially to measure mechnical or transport properties like the viscosity of thin polymer or PNC films. Here, we demonstrate a new method to study the viscosity of PNC thin films using atomic force microscopy based force-distance spectroscopy. Using this method we investigated viscosity and the glass transition, Tg, of PNC thin films consisting of polymer grafted nanoparticles (PGNPs) embedded in un-entangled homopolymer melt films. The PGNP-polymer interfacial entropic interaction parameter, f, operationally controlled through the ratio of grafted and matrix molecular weight, was systematically tuned while maintaining good dispersion even at very high PGNP loadings, φ. We observed both a significant reduction (low f) and giant enhancement (high f) in the viscosity of the PNC thin films with the effect becoming more prominent with increasing φ. Significantly, none of the established theoretical models for viscosity changes observed earlier in suspensions or polymer nanocomposites can explain the observed viscosity variation. Our results thus not only demonstrate the tunability of the interfacial entropic effect to facilitate a dramatic change in the viscosity of PNC coatings, which could be of great utility in various applications of these materials, but also suggest a new regime of viscosity variation in athermal PNC films indicating the possible need for a new theoretical model.

Evidence of Structural Inhomogeneities in Hard-Soft Dimeric Particles without Attractive Interactions

We perform Monte Carlo simulations of a simple hard-soft dimeric model constituted by two tangent spheres experiencing different interactions. Specifically, two hard spheres belonging to different dimers interact via a bare hard-core repulsion, whereas two soft spheres experience a softly repulsive Hertzian interaction. The cross correlations are soft as well. By exploring a wide range of temperatures and densities we investigate the capability of this model to document the existence of structural inhomogeneities indicating the possible onset of aggregates, even if no attraction is set. The fluid phase behavior is studied by analyzing structural and thermodynamical properties of the observed structures, in particular by computing radial distribution functions, structure factors and cluster size distributions. The numerical results are supported by integral equation theories of molecular liquids which allow for a finer and faster spanning of the temperature-density diagram. Our results may serve as a framework for a more systematic investigation of self-assembled structures of functionalized hard-soft dimers able to aggregate in a variety of structures widely oberved in colloidal dispersion.

Multiscale Nature of Thixotropy and Rheological Hysteresis in Attractive Colloidal Suspensions under Shear

Colloids with short range attractions self-assemble into sample-spanning structures, whose dynamic nature results in a thermokinematic memory of the deformation history, also referred to as "thixotropy." Here, we study the origins of the thixotropic effect in these time- and rate-dependent materials by investigating hysteresis across different length scales: from particle-level local measurements of coordination number (microscale), to the appearance of density and velocity fluctuations (mesoscale), and up to the shear stress response to an imposed deformation (macroscale). The characteristic time constants at each scale become progressively shorter, and hysteretic effects become more significant as we increase the strength of the interparticle attraction. There are also strong correlations between the thixotropic effects we observe at each scale.

Fabrication of High Permittivity Resin Composite for Vat Photopolymerization 3D Printing: Morphology, Thermal, Dynamic Mechanical and Dielectric Properties

The formulation of a high dielectric permittivity ceramic/polymer composite feedstock for daylight vat photopolymerization 3D printing (3DP) is demonstrated, targeting 3DP of devices for microwave and THz applications. The precursor is composed of a commercial visible light photo-reactive polymer (VIS-curable photopolymer) and dispersed titanium dioxide (TiO2, TO) ceramic nano-powder or calcium copper titanate (CCT) micro-powder. To provide consistent 3DP processing from the formulated feedstocks, the carefully chosen dispersant performed the double function of adjusting the overall viscosity of the photopolymer and provided good matrix-to-filler bonding. Depending on the ceramic powder content, the optimal viscosities for reproducible 3DP with resolution better than 100 µm were η(TO) = 1.20 ± 0.02 Pa.s and η (CCT) = 0.72 ± 0.05 Pa.s for 20% w/v TO/resin and 20% w/v CCT/resin composites at 0.1 s-1 respectively, thus showing a significant dependence of the "printability" on the dispersed particle sizes. The complex dielectric properties of the as-3D printed samples from pure commercial photopolymer and the bespoke ceramic/photopolymer mixes are investigated at 2.5 GHz, 5 GHz, and in the 12-18 GHz frequency range. The results show that the addition of 20% w/v of TO and CCT ceramic powder to the initial photopolymer increased the real part of the permittivity of the 3DP composites from ε' = 2.7 ± 0.02 to ε'(TO) = 3.88 ± 0.02 and ε'(CCT) = 3.5 ± 0.02 respectively. The present work can be used as a guideline for high-resolution 3DP of structures possessing high-ε.

Spatial Uniformity Assessment of Particles Distributed in a Spherical Fuel Element Using a Non-Destructive Approach

In many manufacturing procedures, a large number of identical particles need to be disseminated uniformly into a given space. The uniformity of the spatial distribution of the particles can be critical to the properties of the final products. We proposed an image processing-based non-destructive technique to evaluate the particles' spatial uniformity in a spherical space imaged with computed tomography. Both graphic (qualitative) and numerical (quantitative) methods were developed to demonstrate the (non-) uniformity of the particles. Simulation results indicated that the technique helped detecting the non-uniformity in the particles' spatial distribution accurately. We conclude that the proposed technique can be used to test whether a number of particles in a sphere are uniformly distributed statistically and graphically.

Diminishing Interfacial Effects with Decreasing Nanoparticle Size in Polymer-Nanoparticle Composites

Using molecular simulations on model polymer nanocomposites at fixed filler loading, we show that interfacial polymer dynamics are affected less with decreasing nanoparticle (NP) size. However, the glass transition temperature T_{g} changes substantially more for an extremely small NP. The reason for this apparent contradiction is that the mean NP spacing decreases with decreasing particle size. Thus, all polymers are effectively interfacial for sufficiently small NPs, resulting in relatively large T_{g} shifts, even though the interfacial effects are smaller. For larger NPs, interfacial relaxations are substantially slower than the matrix for favorable NP-polymer interactions. The minority "bound" polymer dynamically decouples from the polymer matrix, and we only find small changes in T_{g} relative to that of the bulk polymer for large NPs. These results are used to organize a large body of relevant experimental data, and we propose an apparent universal dependence on the ratio of the face-to-face distance between the NPs and the chain radius of gyration.

Effect of copolymer sequence on structure and relaxation times near a nanoparticle surface

We simulate a simple nanocomposite consisting of a single spherical nanoparticle surrounded by coarse-grained polymer chains. The polymers are composed of two different monomer types that differ only in their interaction strengths with the nanoparticle. We examine the effect of adjusting copolymer sequence on the structure as well as the end-to-end vector autocorrelation, bond vector autocorrelation, and self-intermediate scattering function relaxation times as a function of distance from the nanoparticle surface. We show how the range and magnitude of the interphase of slowed dynamics surrounding the nanoparticle depend strongly on sequence blockiness. We find that, depending on block length, blocky copolymers can have faster or slower dynamics than a random copolymer. Certain blocky copolymer sequences lead to relaxation times near the nanoparticle surface that are slower than those of either homopolymer system. Thus, tuning copolymer sequence could allow for significant control over the nanocomposite behavior.

Perspective: Outstanding theoretical questions in polymer-nanoparticle hybrids

This topical review discusses the theoretical progress made in the field of polymer nanocomposites, i.e., hybrid materials created by mixing (typically inorganic) nanoparticles (NPs) with organic polymers. It primarily focuses on the outstanding issues in this field and is structured around five separate topics: (i) the synthesis of functionalized nanoparticles; (ii) their phase behavior when mixed with a homopolymer matrix and their assembly into well-defined superstructures; (iii) the role of processing on the structures realized by these hybrid materials and the role of the mobilities of the different constituents; (iv) the role of external fields (electric, magnetic) in the active assembly of the NPs; and (v) the engineering properties that result and the factors that control them. While the most is known about topic (ii), we believe that significant progress needs to be made in the other four topics before the practical promise offered by these materials can be realized. This review delineates the most pressing issues on these topics and poses specific questions that we believe need to be addressed in the immediate future.

Influence of a nanoparticle on the structure and dynamics of model ionomer melts

We simulate a single spherical nanoparticle (NP) surrounded by partially neutralized ionomers. The coarse-grained ionomers consist of a linear backbone of neutral monomer beads with charged pendant beads and counterions, along with pendant 'sticker' beads that represent unneutralized acid groups. Two different NP interactions are considered; one in which the NP interacts uniformly with all beads in the system (neutral NP) and another in which the NP has higher cohesive interactions with ions and stickers (sticky NP). Ions are depleted around the neutral NP relative to the bulk, but are denser around the surface of the sticky NP. The bond vector autocorrelation function was computed as a function of distance from the NP. For the neutral NP, due to the absence of ions, there is an increase in bond rotational dynamics near the surface relative to the bulk, while the reverse trend is observed in the case of the sticky NP. These analyses were done systematically for differing mole content of pendants, levels of neutralization, and NP sizes; lower pendant content causes a significantly larger difference in the bond dynamics near and far from the NP surface.

Bulk polymer nanocomposites with preparation protocol governed nanostructure: the origin and properties of aggregates and polymer bound clusters

Polymer nanocomposites (PNCs) hold great promise as future lightweight functional materials processable by additive manufacturing technologies. However, their rapid deployment is hindered by their performance depending strongly on the nanoparticle (NP) spatial organization. Therefore, the ability to control nanoparticle dispersion in the process of PNC preparation is a crucial prerequisite for utilizing their potential in functional composites. We report on the bulk processing technique of tailored NP spatial organization in a model glass forming polymer matrix controlled by structural and kinetic variables of the preparation protocol. Namely, we studied the impact of solvent on the NP arrangement, which was already known as a tuning parameter of the solid-state structure. We emphasized the qualitative differences between "poorly dispersed" NP arrays, which, by combination of rheological assessment and structural analysis (TEM, USAXS), we identified as chain bound clusters and aggregates of either thermodynamic or kinetical origin. They are characterized by substantially distinct formation kinetics and mismatched properties compared to each other and individually dispersed NPs. We quantitatively linked all the currently observed types of NP dispersion with their rheological properties during the solution blending step and the amount of polymer adsorption and depletion attraction. We propose the ratio of NP-polymer and NP-solvent enthalpy of adsorption as a parameter capable of the quantitative prediction of NP arrangement in systems similar to our current model PNC. Finally, we bring forth the comparison of glass transition temperatures to further demonstrate the importance of NP spatial organization in PNCs.

Molecular dynamics simulation of the viscoelasticity of polymer nanocomposites under oscillatory shear: effect of interfacial chemical coupling

In this work by adopting coarse-grained molecular dynamics simulation, we focus our attention on investigating the effect of the chemical coupling between polymer and nanoparticles (NPs) on the viscoelastic properties of polymer nanocomposites (PNCs). Firstly we examine the effect of the interfacial chemical coupling on the non-linear behavior, such as the change of the storage moduli, the loss moduli and the loss factor as a function of the strain amplitude. Besides the reinforcing effect contributed by the interfacial chemical interaction, a much smaller loss factor is also observed attributed to less molecular friction and dissipation. Meanwhile, the effects of temperature, frequency, and the interfacial physical interaction between NPs and polymers on the viscoelastic properties are also probed. To uncover the structural and dynamic effect of the interfacial chemical coupling, we calculate the radial distribution function of polymer chains around NPs, the content of the polymer beads in the first layer of the interfacial region under quiescent and dynamic conditions, the incoherent intermediate dynamic structure factor of the polymer beads, which are chemically or physically tethered to the NPs, and all the polymer beads of the system, the quantitative comparison of the mean relaxation time for different interfacial chemical coupling, and the mean-square displacement of the polymer chains. Lastly we analyze the change of the interfacial energy such as the physical and chemical energies during oscillatory shear. Through these analyses, we conclude that with the increase of the interfacial chemical coupling, the change extent of the interfacial physical interaction versus the periodic strain decreases, attributed to a much smaller adsorption-desorption reversible process. This can rationalize the much weaker non-linear behavior or the "Payne effect". Based on these results, we anticipate that a better molecular-level understanding is provided on the effect of the interfacial coupling on the viscoelastic properties of PNCs.

Tailoring the dispersion of nanoparticles and the mechanical behavior of polymer nanocomposites by designing the chain architecture

The dispersion of nanoparticles with different polymer–nanoparticle interaction strengths and chain architectures.

Self-assembly of polymer-grafted nanoparticles in solvent-free conditions

Grafting of polymer chains onto the surface of spherical nanoparticles leads to a hybrid type of fluid that exhibits properties of both particle suspensions and melts of star polymers-these properties being controlled by the relative dimensions of the grafted polymer chains to the nanoparticle diameter, D, and the number of the number of chains grafted on the nanoparticle surface, f. While polymer-grafted nanoparticles (GNP) of this kind typically have a spherical average shape after grafting even a moderate number of chains, their instantaneous molecular shape can fluctuate significantly due to the deformation of the grafted chains. Both simulations and measurements have previously revealed that these "conformationally polarizable" particles can exhibit self-assembly into large scale polymeric structures in both solution and in polymer melts, and we simulate polymer-grafted nanoparticles with D and temperature (T) variations without a dispersing solvent to better understand the nature of this self-assembly process. We observe a reversible self-assembly into linear and branched dynamic GNP structures, where the extent of the assembly and geometry depend on D and T, and we constructed a map capturing the GNP structural behavior with D and T variations. Since the shape of the GNPs appeared to be correlated with the occurrence of the GNP self-assembly, we quantified the average shape and a measure of shape fluctuations to better understand how molecular shape influences their propensity to self-assemble into different structural forms. Based on this framework, we describe the clustering process of the GNPs as an equilibrium polymerization phenomenon and we calculate the order parameter governing the dynamic clustering behavior of GNPs, the average mass of the clusters, size distribution, and the apparent fractal dimension of the clusters.

Systematic comparison of model polymer nanocomposite mechanics

Polymer nanocomposites render a range of outstanding materials from natural products such as silk, sea shells and bones, to synthesized nanoclay or carbon nanotube reinforced polymer systems. In contrast to the fast expanding interest in this type of material, the fundamental mechanisms of their mixing, phase behavior and reinforcement, especially for higher nanoparticle content as relevant for bio-inorganic composites, are still not fully understood. Although polymer nanocomposites exhibit diverse morphologies, qualitatively their mechanical properties are believed to be governed by a few parameters, namely their internal polymer network topology, nanoparticle volume fraction, particle surface properties and so on. Relating material mechanics to such elementary parameters is the purpose of this work. By taking a coarse-grained molecular modeling approach, we study an range of different polymer nanocomposites. We vary polymer nanoparticle connectivity, surface geometry and volume fraction to systematically study rheological/mechanical properties. Our models cover different materials, and reproduce key characteristics of real nanocomposites, such as phase separation, mechanical reinforcement. The results shed light on establishing elementary structure, property and function relationship of polymer nanocomposites.

Self-Assembly of Colloidal Nanocrystals: From Intricate Structures to Functional Materials

Chemical methods developed over the past two decades enable preparation of colloidal nanocrystals with uniform size and shape. These Brownian objects readily order into superlattices. Recently, the range of accessible inorganic cores and tunable surface chemistries dramatically increased, expanding the set of nanocrystal arrangements experimentally attainable. In this review, we discuss efforts to create next-generation materials via bottom-up organization of nanocrystals with preprogrammed functionality and self-assembly instructions. This process is often driven by both interparticle interactions and the influence of the assembly environment. The introduction provides the reader with a practical overview of nanocrystal synthesis, self-assembly, and superlattice characterization. We then summarize the theory of nanocrystal interactions and examine fundamental principles governing nanocrystal self-assembly from hard and soft particle perspectives borrowed from the comparatively established fields of micrometer colloids and block copolymer assembly. We outline the extensive catalog of superlattices prepared to date using hydrocarbon-capped nanocrystals with spherical, polyhedral, rod, plate, and branched inorganic core shapes, as well as those obtained by mixing combinations thereof. We also provide an overview of structural defects in nanocrystal superlattices. We then explore the unique possibilities offered by leveraging nontraditional surface chemistries and assembly environments to control superlattice structure and produce nonbulk assemblies. We end with a discussion of the unique optical, magnetic, electronic, and catalytic properties of ordered nanocrystal superlattices, and the coming advances required to make use of this new class of solids.

Phase separation of comb polymer nanocomposite melts

In this work, the spinodal phase demixing of branched comb polymer nanocomposite (PNC) melts is systematically investigated using the polymer reference interaction site model (PRISM) theory. To verify the reliability of the present method in characterizing the phase behavior of comb PNCs, the intermolecular correlation functions of the system for nonzero particle volume fractions are compared with our molecular dynamics simulation data. After verifying the model and discussing the structure of the comb PNCs in the dilute nanoparticle limit, the interference among the side chain number, side chain length, nanoparticle-monomer size ratio and attractive interactions between the comb polymer and nanoparticles in spinodal demixing curves is analyzed and discussed in detail. The results predict two kinds of distinct phase separation behaviors. One is called classic fluid phase boundary, which is mediated by the entropic depletion attraction and contact aggregation of nanoparticles at relatively low nanoparticle-monomer attraction strength. The second demixing transition occurs at relatively high attraction strength and involves the formation of an equilibrium physical network phase with local bridging of nanoparticles. The phase boundaries are found to be sensitive to the side chain number, side chain length, nanoparticle-monomer size ratio and attractive interactions. As the side chain length is fixed, the side chain number has a large effect on the phase behavior of comb PNCs; with increasing side chain number, the miscibility window first widens and then shrinks. When the side chain number is lower than a threshold value, the phase boundaries undergo a process from enlarging the miscibility window to narrowing as side chain length increases. Once the side chain number overtakes this threshold value, the phase boundary shifts towards less miscibility. With increasing nanoparticle-monomer size ratio, a crossover of particle size occurs, above which the phase separation is consistent with that of chain PNCs. The miscibility window for this condition gradually narrows while the other parameters of the PNCs system are held constant. These results indicate that the present PRISM theory can give molecular-level details of the underlying mechanisms of the comb PNCs. It is hoped that the results can be used to provide useful guidance for the future design control of novel, thermodynamically stable comb PNCs.

How the flow affects the phase behaviour and microstructure of polymer nanocomposites

We address the issue of flow effects on the phase behaviour of polymer nanocomposite melts by making use of a recently reported Hamiltonian set of evolution equations developed on principles of non-equilibrium thermodynamics. To this end, we calculate the spinodal curve, by computing values for the nanoparticle radius as a function of the polymer radius-of-gyration for which the second derivative of the generalized free energy of the system becomes zero. Under equilibrium conditions, we recover the phase diagram predicted by Mackay et al. [Science 311, 1740 (2006)]. Under non-equilibrium conditions, we account for the extra terms in the free energy due to changes in the conformations of polymer chains by the shear flow. Overall, our model predicts that flow enhances miscibility, since the corresponding miscibility window opens up for non-zero shear rate values.

Rheology and morphology of no-slip sheared polymer nanocomposite under creep condition

Dissipative particle dynamics simulations are performed on wet polymer nanocomposite blends under the discrete imposed velocity profile and no-slip boundary conditions. To be able to study the chain length dependency of the rheological properties, a number of blends of mono-disperse polymer chains of lengths varying from 10 to 100 repeat units and nanoparticles of diameters 2.5 and 5 have been simulated. The wall velocity was imposed on a thin polymer layer (the no-slip layer). Linear velocity profiles for polymer confined in the pore were observed at the steady state. We found that the flow has a shear thinning effect on the chains with a radius of gyration less than the filler radius. Long chains (with a radius of gyration longer than the filler's radius), however, obey the Newtonian behavior over a much wider shear rate than that which causes shear thinning in short chains. The effect of particle-monomer interactions, polymer entanglements, chain morphology, and link formation on the shear rate dependency of the viscosity coefficient has been studied. Our results show that the particle-polymer interactions have no effect on shear thinning behavior of the blend. In contrast, the long range polymer-polymer interactions and the chain length have considerable effects on the rheological behavior of the blend. Finally, the phase diagram of the rheological properties of polymer nanocomposite as a function of strain rate and the chain length is extracted.