Melt-State Viscoelastic Properties of POSS-Containing Polyethylene Nanocomposites

Mj, Hato; Hato, Mpitloane J.; Sk, Pillay; Ray, Suprakas Sinha; As, L van; Luyt, Adriaan S.

doi:10.1166/asl.2011.1911

Cited by 8 publications

(9 citation statements)

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“…Hato et al . showed that the addition of octamethyl‐POSS into LLDPE matrix led to an increase in molten state viscosity . Similar results were presented by Huang et al .…”

Section: Introductionsupporting

confidence: 86%

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Thermo‐rheological properties and miscibility of linear low‐density polyethylene‐silsesquioxane nanocomposites

Barczewski

Sterzyński

Dutkiewicz

2015

J of Applied Polymer Sci

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The influence of octakis(n‐hexadecyldimethylsiloxy)octasilsesquioxane (hdPOSS) on rheological and thermal properties of linear low‐density polyethylene was investigated. Rheological evaluations realized by two different measuring techniques (rotational and extensional rheometry) allowed to obtain a wide range of information concerning an influence of silsesquioxanes on rheological properties of polymeric matrix. The studies were complemented with Differential Scanning Calorimetry measurements. In order to evaluate the miscibility of thermoplastic matrix and hybrid organic‐inorganic nanosized filler, rheological investigations, Dynamic Mechanical Thermal Analysis and Scanning Electron Microscopy were applied. Simultaneously, based on the rheological and thermal investigations, the mechanism of interactions between polymeric matrix and silsesquioxanes was considered. © 2015 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2015, 132, 42825.

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“…Hato et al . showed that the addition of octamethyl‐POSS into LLDPE matrix led to an increase in molten state viscosity . Similar results were presented by Huang et al .…”

Section: Introductionsupporting

confidence: 86%

“…Hato et al . revealed that this effect was caused by a strong phase separation between silsesquioxanes and polyethylene . In case of 10 wt % hdPOSS addition, nonlinear behavior in the range of 0.01 and 5% in storage modulus vs. strain plot occurred.…”

Section: Resultsmentioning

confidence: 96%

Thermo‐rheological properties and miscibility of linear low‐density polyethylene‐silsesquioxane nanocomposites

Barczewski

Sterzyński

Dutkiewicz

2015

J of Applied Polymer Sci

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“…[1,2] Similarly, recent advances in microfabrication allow the generation of engineered networks of reaction units, e.g., with labon-chip microdroplets, [3] electrode arrays, [4,5] BZ beads, [6] or microelectrofusion. [7] The interplays between local reaction kinetics (nodes), the physical processes that create coupling (link), and the architecture of the network in such systems can lead to a wealth of self-organized phenomena, including synchronization, [4,6,8] stationary Turing and oscillatory patterns, [9,10,11,12,13] or excitation waves. [14,15,16] Stationary patterns generated via the Turing [17] mechanism have been observed in experiments for both continuous [18] and networked [19] systems.…”

mentioning

confidence: 99%

Self‐Organized Stationary Patterns in Networks of Bistable Chemical Reactions

et al. 2016

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Experiments with a network of bistable electrochemical reactions, organized in regular and non-regular tree networks, are presented to confirm an alternative to Turing mechanism for formation selforganized stationary patterns. The results show that the pattern formation can be described by identification of domains that can be activated individually or in combinations. The method was also demonstrated to localization of chemical reactions to network substructures and identification of critical sites whose activation results in complete activation of the system. While the experiments were performed with a specific nickel electrodissolution system, they reproduce all the salient dynamical behavior of a general network model with a single nonlinearity parameter. This indicates that the considered pattern formation mechanism is very robust and similar behavior can thus be expected in other natural or engineered networked systems, which exhibit, at least locally, a tree-like structure.In biological context, many chemical reactions take place in discrete units, e.g., in cells, that form a complex network. The interplays between local reaction kinetics (nodes), the physical processes that create coupling (link), and the architecture of the network in such systems can lead to a wealth of self-organized phenomena, including synchronization, [4,6,8] stationary Turing and oscillatory patterns, [9,10,11,12,13] or excitation waves. [14,15,16] Stationary patterns generated via the Turing[17] mechanism have been observed in experiments for both continuous [18] and networked [19] systems. Here, an alternative mechanism for emergence of stationary patterns in networks is experimentally explored. We focus on network-organized systems of bistable elements with diffusive connections between them. Bistable elements can be found in a broad class of chemical reactions (e.g., with autocatalysis [20,21]) but also in cellular [22] and engineered systems.[23] Such elements can have local or diffusive connections between them. For regular lattices and linear chains (i.e., for relatively simple networks), it is known that, under sufficiently weak coupling, the fronts fail to propagate, and thus stationary domains can be formed, [24,25,26,27] whereas at strong coupling the fronts spread [27,28] and a uniform state is eventually established. Recently, analogues phenomena were theoretically investigated for complex 1

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“…Diffusion processes on networks are of fundamental importance for spreading of infectious diseases [7][8][9][10], and dispersal connections between ecological habitats may significantly affect the dynamics and stability of a metapopulation [11,12] (see also [13][14][15]). When modeling such phenomena, it is usually assumed that probabilities of transitions between the nodes are not correlated and, in principle, they can be arbitrarily assigned.…”

mentioning

confidence: 99%

“…Localization has previously been considered for network diffusion processes, where eigenvectors of the Laplacian matrix play an important role and the localization is determined by degrees of the nodes [12,13,20]. In contrast to this, localization of eigenvectors of the advection matrix is determined by the flows passing through network nodes, and generally, flow hubs are different from network hubs.…”

mentioning

confidence: 99%

Advection of passive particles over flow networks

2014

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The problem of stochastic advection of passive particles by circulating conserved flows on networks is formulated and investigated. The particles undergo transitions between the nodes, with the transition rates determined by the flows passing through the links. Such stochastic advection processes lead to mixing of particles in the network and, in the final equilibrium state, concentration of particles in all nodes becomes equal. As we find, equilibration begins in the subset of nodes, representing flow hubs, and extends to the periphery nodes with weak flows. This behavior is related to the effect of localization of the eigenvectors of the advection matrix for considered networks. Applications of the results to problems involving spreading of infections or pollutants by traffic networks are discussed.

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Melt-State Viscoelastic Properties of POSS-Containing Polyethylene Nanocomposites

Cited by 8 publications

References 0 publications

Thermo‐rheological properties and miscibility of linear low‐density polyethylene‐silsesquioxane nanocomposites

Thermo‐rheological properties and miscibility of linear low‐density polyethylene‐silsesquioxane nanocomposites

Self‐Organized Stationary Patterns in Networks of Bistable Chemical Reactions

Advection of passive particles over flow networks

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