Polymer Physics: From Suspensions to Nanocomposites and Beyond

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Open Access. Historical Collection. You do not have JavaScript enabled. Please enable JavaScript to access the full features of the site or access our non-JavaScript page. Issue 25, Previous Article Next Article. From the journal: Soft Matter. Nanoparticle self-assembly: from interactions in suspension to polymer nanocomposites. You have access to this article. Please wait while we load your content High-Temperature Levitated Materials.

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Some of the benefits are controllable particle morphology [ 69 ], good interfacial adhesion of the nanofillers [ 70 ] and high transparency [ 71 , 72 ]. Usage of machine learning is still in its infancy, and many interesting challenges remain unexplored. The filler particle shape was icosahedral with interaction sites assigned at the vertices, at four equidistant sites along each edge, and at six symmetric sites on the interior of each face of the icosahedron. Our ever expanding ability to computationally interrogate idealized yet complex mathematical descriptions of disordered matter such as glasses, poly-disperse colloidal aggregates, amorphous polymers, and granular packings has provided novel means to understand their underlying physics. We want to give an overview on the models have been used to detect statistically significant structural patterns in real-world networks and to reconstruct the network structure in cases of incomplete information.

Inkjet Printing on Fabric. Wendy Cotterill. Surface Science. Kurt W. Dong Zhang. Xiao Feng Pang. Principles of Polymer Processing. Zehev Tadmor. Terry Tritt.

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Roshan Shishoo. Handbook of Polymer Crystallization. Ewa Piorkowska. George Hasegawa. Nuclear and Radiochemistry. Jens-Volker Kratz. On-Surface Synthesis. James E. Functional Materials. Tamara K. The Properties of Gases and Liquids 5E. Bruce E.

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Multiscale Molecular Simulations of Polymer-Matrix Nanocomposites

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Smart Hydrogel Modelling. Hua Li. Victor Starov. Organic Optoelectronic Materials. Yongfang Li. Two interconnected levels of representation were employed. The smoother effective potential energy hypersurface of the coarse-grained representation permitted its equilibration at all length scales by using powerful connectivity-altering Monte Carlo algorithms [ ].

Coarse-graining and reverse-mapping between the two levels of representation was accomplished in a manner that preserved tacticity and respected the detailed conformational distribution of chains [ ]. Their results are presented in Fig.

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As far as the nanocomposite polystyrene systems are concerned, the presence of the nanoparticles affected the root- mean-square radius of gyration only slightly. Root mean squared radius of gyration of the coarse-grained chains of neat and nanocomposite polystyrene systems as a function of molar mass, M , in the melt at K red , green and magenta rhomboid symbols. Neutron scattering measurements [ ] for high molar mass PS are also included blue rhomboid symbols.

Color figure online Reproduced from Ref. SANS measurements show a clear scattering signature of a polymer bound layer around the particles, which arises due to a scattering length density different from the bulk polymer matrix material, either due to H or D enrichment or a modification of the polymer density in the bound layer compared to the surrounding polymer matrix [ , ]. The measurements of Jouault et al. Then, as observed by Jiang et al.


One can estimate the thickness of the bound polymer layer around 2 nm. However, this thickness value is a simplification because it does not completely describe the complex chain behavior, some aspects of which will be analyzed below. The local density of the polymer in the proximity of the surface of a filler is often employed as an indication of the strength of polymer—surface interactions and a decrease of the first peak of the radial density distribution is expected with curvature [ ]. At this point we resort to the detailed analysis of Pandey and Doxastakis [ ] concerning a polyethylene layer close to a filler surface Fig.

These authors coupled the application of preferential sampling techniques [ ] with connectivity-altering Monte Carlo algorithms [ , ] in order to explore the configurational characteristics of a polyethylene melt in proximity to a silica surface or around a nanoparticles and the changes induced by high curvature when the particle radius is comparable to the polymer Kuhn segment length.

The decomposition into tails, trains and loops is carried out following Scheutjens and Fleer [ ]. The inset provides profiles for selected systems. Reprinted from [ ], with the permission of AIP Publishing. The inset to Fig. To investigate further the concentration of adsorbed monomers, these authors followed the use of a simple distance criterion adsorbed polymer chains have an atom within 0. Tails are the segments which are hinged to the surface at one end while the other end is dangling freely into the bulk polymer.

Train segments consist of monomers consecutively adsorbed on the surface.