Session L03: Hierarchical Structural Emergence in Elastomer Nanocomposites: Dispersion, Dynamics, Structure, Modeling, and Simulation

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We review our work from Laboratory Léon Brillouin, associated groups, and other groups in the world, on the structure of polymer-nanoparticles composites, under deformation, in relation with mechanical properties improvement of plastics.
First, about the nanoparticles: depending on preparation, but also on composition (non grafted or grafted particles), and on the matrix chains molecular weight, they can show individually dispersion, fractal aggregation, compact aggregation (1), or initially anisotropic aggregates. These conditions can be created by physicochemical ways, like solution casting, or combined with an external field, or by industrial protocols like in tyre industry…We will review how to obtain the different cases, and their response to deformation (2). For this we will use first different chemical synthesis, including chain grafting on the particles; second, physical characterization: Small Angle Scattering, mostly from X Rays(SAXS), complemented by Electronic Transmission Microscopy to help us for interpretation of the scattering (fitting).
Second, the polymer: we will observe the chain conformation in the nanocomposite( for non-crystalline matrix), as well as the grafted chains brush, combining now X Rays with neutron radiation (SANS), at rest first. Under deformation (3), the effect of nanoparticle reinforcement on the comformation is strikingly different from the one on the stress-strain curves!
We will compare and harmonise with results of other groups(4).
(1) C Chevigny, F. Dalmas, E. Di Cola, D. Gigmes, D. Bertin, F. Boué , J. Jestin, Macromolecules 2011, 44 (1)
(2) N. Jouault, F. Dalmas, F. Boué, J. Jestin, Polymer, Polymer 2014, 55, 2523-2534.
(3) N. Jouault, C. Chevigny, F. Dalmas, S. Said, E. Di-Cola, R. Schweins, J. Jestin, F. Boué Physical Review E. 2010 (82) 3
(4) D. Zhao, V. G-Pinto, Andrew M. Jimenez, L. Zhao, J. Jestin, S. K. Kumar, B. Kuei, E. D. Gomez, A.S. Prasa, L .S. Schadler,M. M. Khani, C. Benicewicz, ACS Cent. Sci. 2017 (37) 751.   

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Despite the broad commercial utility of nanoparticle reinforced rubbers, connections between microscale filler behavior and macroscale performance are not well understood. XPCS allows us to probe the microscale breakdown and reformation of the filler network, which are responsible for the fuel efficiency and traction of tire tread compounds. We report results of dynamic strain XPCS experiments on two silica-filled rubbers, one with a silane coupling agent and one without. The silane coupling agent suppresses filler breakdown and pushes the onset of irreversible agglomerate breakdown towards larger strains. For a high surface area silica, we find that filler network breakdown alone cannot explain energy dissipation. We can explain this behavior by considering the structure and debonding of bound rubber layers. These results show that the combination of XPCS and DMA is a powerful method for understanding how the microscopic filler behavior dictates the dynamic mechanical properties of rubber.   

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The dispersion of nanoparticles in viscous polymers is dictated by kinetics; particle interaction potential; and interfacial compatibility between the matrix and dispersed phases. An analogy can been made between thermally dispersed colloids and kinetically dispersed nanoparticles for cases where weak interactions exist between particles allowing for a mean field description under the Ginzburg criterion such as for surface-modified silica and carbon black nanocomposites. However, this approach fails for precipitated/fumed fillers with extensive surface charge which results in strong correlations. Dispersion in these systems is quantified using a pseudo-second virial coefficient and the resulting correlations are accounted through a combined semi-empirical function based on the Born-Green theory and a distribution function that accounts for the non-uniform accumulated strain in nanocomposites. The dependence of the amount of surface charge, concentration and dielectric nature of the polymer on the emergence of correlated structures is explored.   

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Surface modification is an effective way to improve the dispersion of nanoparticles in polymer matrix, which is critical to the applications of polymer nanocomposites for enhanced mechanical, electrical, and thermal properties. Navigating the large parameter space available when designing commercial grade materials, is a formidable task that benefits from a fundamental understanding of polymer-surface interactions. In this work, we present an investigation of silane modification effects on polymer interfacial properties using atomistic simulations. Contrasting systems of amorphous silica substrates modified by two types of silane agents at two grafting densities to the bare substrate case, we show that silane grafting can effectively modulate chain configurations near the silica surface, including trains, loops, and tails, highlighting the individual effect of silane grafting density and chain length. We analyze the variation of polymer dynamics in adsorption, relaxation, and diffusion, and further demonstrate the correspondence with the distribution of train configuration. This detailed study offers detailed information of silane effects at microscopic level.   

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Polymer nanocomposites (PNC) enhance the mechanical, optical, and electrical properties relative to the host polymer and are already widely applied and studied because of their potentials in tunability, responsiveness, and functionality. However, sufficient understanding of the structure-dynamics-property relationship of these materials are lacking. Here, silica nanoparticles (SiO₂) in various shapes and sizes are investigated as model attractive PNC using scattering methods and rheology to connect the molecular level structure and dynamics to the macroscopic property. Small angle scattering techniques provide information on the dispersion of the nanoparticles as well as the conformation of the polymer chains. Dynamic neutron scattering techniques, such as backscattering and spin echo, are used to measure the effect of the different nanoparticles on the Rouse dynamics and entanglement density. Data are interpreted in terms of standard theories developed for bulk polymers providing a mean to connect the microscopic dynamics to the observed rheological behavior. The results provide insights into the role of surface, volume and nanoparticle dispersion in polymer matrix and shed light on how polymer dynamics is related to mechanical reinforcement of PNC.   

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In a highly loaded, silica-filled, crosslinked model rubber closely mimicking commercial materials, the filler network structure and dynamics of the silica filler particles change when the silica surface is modified with silane coupling agents. Reduction in size scales characteristic of the structure are quantified using ultra-small-angle X-ray scattering (USAXS) measurements and the filler particle microdynamics after step strain probed with X-ray photon correlation spectroscopy (XPCS). The evolution of filler particle dynamics depends on the chemical functionality at the silica surface and observing these differences suggests a way of thinking about the origins of hysteresis in reinforced rubbers. These microscopic filler dynamics are correlated with the macroscopic stress relaxation of the materials. The combination of static and dynamic X-ray scattering techniques with rheological measurements is a promising approach for elucidating the microscopic mechanisms of rubber reinforcement.   

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In polymeric systems such as natural rubber, nanoscale fillers are widely employed as cheap and effective additions for enhanced properties and functionality. Such nanocomposites may contain fillers of varying miscibility, such as carbon black, silica, pigments, metal oxides and/or various combinations thereof. In such systems, a complex partitioning of the components often results from the rich thermodynamics and kinetic history. The state of dispersion of the polymers and fillers is challenging and crucial to the behavior of nanocomposites. In this research, we perform Dissipative Particle Dynamics (DPD) simulation of these blends, varying polymer-polymer interaction energy, to understand the hierarchical structure and dispersion over multiple length and timescales. These results are validated against small-angle x-ray scattering to bridge a significant gap in our understanding of how complex hierarchical structure develops. Additionally, our results demonstrate the role of concentration on the clustering of fillers, investigated via the cluster size, radius of gyration, and the population distribution. It also demonstrates the formation of large percolating aggregates and can be mapped onto the macroscopic percolation characterized by rheology.   

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Nanoparticles assemble into clusters, aggregates and agglomerates or networks depending on their affinity in a matrix, transport coefficients, and temperature or accumulated strain. Using an equilibrium model, the free energy change associated with clustering can be determined (Vogtt 2019, Mulderig 2019). Coupling the free energy change with a calculation of size-dependent transport, the details of nano-topology can be simulated using a Metropolis Monte Carlo approach. Through successive application of this method to four hierarchical levels predictions can be made for the formation of micron-scale networks responsible for many properties of nanocomposites. This approach is demonstrated for organic pigment nanocomposites.

Vogtt, K. et al., Phys. Rev. Research 1 (2019)
Mulderig, A. et al. Langmuir 35 13100-13109 (2019).   

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Epoxy resins are among the best matrix materials for many fiber composites. A coarse-grained (CG) model is developed to allow large-scale molecular dynamics simulations of epoxy network polymers based on EPON resin 862 and diethyltoluenediamine (DETDA). An all-atom model is first built for a small network of EPON chains crosslinked with DETDA crosslinkers. A systematic protocol based on chemistry-informed grouping of atoms, derivation of bond and angle interaction by fitting bond and angle distributions to Gaussian functions, and parameterization of nonbonded interactions by potential of mean force (PMF) calculations, is used to construct the CG model. An entropic correction term is introduced to the PMFs, which enables the resulting CG model to capture the thermal expansion property of the polymer and makes it transferable temperature-wise below the glass transition temperature. The CG model has been applied to explore the mechanical and structural properties of large epoxy networks and the results agree well with those from the all-atom model and experiments. Based on this CG model, a multi-cellular model is further developed to enable the modeling of epoxy network polymers at the micron size scale.   

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The role of self-assembled nanoparticle (NP) structures on the crystallization kinetics of semicrystalline polymers is systematically investigated. A bimodal brush of polystyrene and poly(methyl methacrylate) is tethered to the surface of silica NPs, which are dispersed into the poly(ethylene oxide) (PEO) matrix. This provides access to a range of self-assembled structures by exploiting the surfactancy of the NPs. While the addition of NPs slows down the spherulite growth rate, it was found that the NP self-assembly does not change the temperature dependence of the spherulite growth rate. Instead, growth rate retardation is mainly attributed to the increase of the PEO melt viscosity and tracks well with the rheologic measurements. Surprisingly, two “universal” trends are observed when measuring the relative spherulite growth reduction as a function of confinement. As long as the self-assembled NP structures are on the nanometer scale, the NPs appear to follow an indistinguishable trend in retarding the spherulitic growth kinetics. However, larger micron-sized aggregates and bare NPs show a weaker effect on the spherulitic growth.   

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Quantification of nano-particle dispersion in polymer nanocomposites is challenging since a statistically relevant measure of a nano-property must be averaged over macroscopic sample dimensions. For the ternary system presented by semi-crystalline nanocomposites the situation is even more complex. Polymer crystals in the thickness and potentially lateral dimensions are of similar size to the reinforcing filler so it could be expected that some interaction between filler and crystals is to be expected. It is believed that adequate dispersion of nano-additives is required to maximize properties. In this study, dispersion is quantified through the use of x-ray scattering on high density polylethylene (HDPE)/N110 carbon black composites produced by different melt mixing techniques. An analog of the second virial coefficient, which describes binary interactions, is compared with other dispersion measurement techniques. A comprehensive presentation of the crystalline morphology (thickness, width, stacking order, and disorder), carbon black structure (primary size, branch content, tortuosity, aggregate size), as well as a quantitative measure of dispersion are obtained as a function of thermal history and concentration of filler.   

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Polymer nanocomposites are often composed of ramified aggregates in a polymer matrix, for example silica reinforced elastomers, carbon black reinforced polyethylene for pipes, lithium salt composites with PEO for solid electrolytes. The design and modeling of these nanocomposites requires visualization of these complex 3d structures and their emergent multi-hierarchies. It is possible to parameterize these nanocomposites using small-angle scattering, reflecting an average of the hierarchical structures. Due to the loss of phase information, a direct transform to a 3d visualization is not possible. However, within the constraints of a hierarchical aggregate structure, it is possible to simulate 3d aggregates that match the degree of branching, convolution, tortuosity, degree of aggregation, mass, and hierarchical sizes of those in nanocomposites. This process can be conducted dynamically, such as during drying of ink or paint, to visualize the growth of emergent complex hierarchical structures. Verification of the visualizations is demonstrated using cryo-TEM. The 3d visualizations are used as a starting point for simulations of multi-hierarchical assembly and structural emergence in nanocomposites such as reinforced elastomers and paints.   

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Polymer nanocomposites (PNCs) with nanorods as fillers have access to a variety of morphologies not accessible to PNCs with nanoparticles. Different morphologies promise unique property enhancements to the PNC in different fields e.g. mechanical, optical, electronic, and transport properties. Even though past simulation studies have provided strong fundamental understanding on the PNC morphology obtainable with nanorods with homogeneous functionalization, studies focused on the morphology of nanorods with patchy functionalization are lacking. In this talk, we will address this knowledge gap. We will present coarse-grained molecular simulations of PNCs with homogeneously functionalized nanorods as fillers modeled with isotropic attractions between nanorods or PNCs with patchy functionalized nanorods as fillers modeled with directional attractions between nanorods. Through a systematic variation of the parameters (e.g., nanorod size, matrix chain length, strength of pair-wise attraction) in both aforementioned systems, we are able to compare and contrast the morphological implications of PNCs with nanorods of different type of functionalization. These results will serve as valuable design rules for a broad range of soft materials comprised of anisotropic particles.