Network junction dynamics

The dynamic origin of the adsorption layer which provides physical network junctions... 

Finally, the NMR and the dynamic mechanical study show that two regions are present in filled silicone rubbers above the Tg, which differ significantly in local chain mobility immobilized chain units adsorbed at the filler surface and mobile chain units outside the adsorption layer. The local chain motions outside the adsorption layer are similar to those for unfilled rubbers. Chain motions in the adsorption layer however are strongly restricted. The frequency of chain motions in the adsorption layer at 300 K is comparable to the fi-equency of chain motions in a crosslinked PDMS containing 3-4 elementary chain units between network junctions [26]. 

A large number of macroscopic properties of elastomer networks are closely related to the density of network junctions and the extent of their fluctuations. Qualitatively, any increase of network density causes an increase in stress, whereas fluctuations of network junctions leads to a decreasing stress. It is generally believed that a formation of additional network junctions resulting fi-om the presence of filler particles in the elastomer matrix is one of the reasons for the improvement of mechanical properties of filled elastomers. However, the application of macroscopic techniques does not provide reliable results for the network structure in filled elastomers. Furthermore, a lack of information exists on the dynamic behavior of adsorption junctions. The present study fills the gap of knowledge in this area. 

FIGURE 5.19 Plot of the coupling parameter of junction dynamics nj determined from experimental data by Shi et al. (Shi et al., 1993) for four polymer networks with different molecular weights between crosslinks, Mq. Solid inverted triangles and open circles are from NMR data taken using cross (CP) and direct (DP) polarization, respectively. The solid square is for a swollen sample with Me = 650. The lines are drawn to guide the eyes. 

Fig. 6 An elementary cell of a topologically-regular cubic network cross-linked from Rouse chains. One of the network chains between two cross-links (junctions) is shown in detail. Note that the dynamic problem of such a network can be exactly solved only under the condition that the friction constant of each network junction, un, is three times larger than that of a chain bead, see text for details...

Complementary to the study of the effects of cross-linking on the local segmental relaxation is the investigation of the dynamics of the network junctions of a... 

As indicated earlier the ER particle may form a network structure instead of the nbrillaled chain structure once the particle volume fraction exceeds the critical volume fraction 162,83], due to the percolation transition. Under a large amplitude shear field the formation and destruction of network junctions may happen one after the other, and thus the type III LAOS behavior may best describe ER suspensions. Figure 43 shows the simulated storage and loss moduli vs. strain amplitude at different frequencies, using an idealized electrostatic polarization model of ER fluids that was implemented in the particle-level dynamics simulation. Tlie storage modulus G and loss modulus G" remain constant up to a certain strain amplitude (/() 0.4), which defines the linear response region. With further increase... 

ABSTRACT A flow network with multiple inflow and outflow points, and a series-paraUel-reducible structure is considered. It is composed of independent components with constant failure and repair rates. The author has defined indices that characterize the dynamically changing throughput between inflow points and a given internal point. He has also constructed an algorithm to compute these indices. The algorithm is based on the stepwise aggregation of the network structure, it operates on integer numbers, and has relatively low numerical complexity. A crucial role in the computations is played by the notions of c-importance of components and c-avaUability at network junctions, introduced by the author. The presented results can be applied in the reliabiUty analysis of various types of flow networks, e.g. water supply systems, gas and/or oil pipelines, power transmission and/or distribution networks, etc. 

Recent experimental advances reinforce the idea that the microscopic motions and the elastic properties can be usefully interrelated. For example, quasielastic neutron scattering measurements probe the motions of the network junctions [53,54]. Molecular dynamics simulations have also added new insight, for example, by demonstrating the existence of local constraints on the network chains at strand lengths less than the molecular weight necessary for chain entanglements [55]. Recently P NMR spin-lattice relaxation experiments on crosslinked rubber have been used to monitor specifically the dynamics of the network junctions [52,56]. 

Other PDMS—sihca-based hybrids have been reported (16,17) and related to the ceramer hybrids (10—12,17). Using differential scanning calorimetry, dynamic mechanical analysis, and saxs, the microstmcture of these PDMS hybrids was determined to be microphase-separated, in that the polysiUcate domains (of ca 3 nm in diameter) behave as network cross-link junctions dispersed within the PDMS oligomer-rich phase. The distance between these... 

We have investigated the static and dynamic mechanical properties of networks of different chemical and topological structures ( 19,20). In a previous paper, we reported results obtained on networks with crosslink functionality four (21). In the present study, we investigated the effect of the structure of junctions on the mechanical behaviour of PDMS. Rather uncommon networks with comb-like crosslinks were employed, intending that these would be most challenging to theoretical predictions. 

The implications of individual neuron dynamics on neuronal network synchronization is evident. In Fig. 7.9 (from Schneider et al., unpublished data) this is demonstrated with network simulations (10 x 10 neurons) of nearest neighbor gap-junction coupling. It is illustrated in quite a simple form which, in a similar way, can also be experimentally used with the local mean field potential (LFP). In the simulations LFP simply is the mean potential value of all neurons. In the nonsynchronized state LFP shows tiny, random fluctuations. In the completely in-phase synchronized states the spikes should peak out to their full height... 

For a more detailed analysis of the neuronal dynamics in the course of synchronization we reduced the network model to only two bidirectionally gap-junction... 

Epithelial cells are interconnected at the apical (mucosal) side by a complex network of proteins, called the tight junctions (TJ). First thought to have merely a static role in restricting access of compounds present in the luminal fluid to the underlying subepithelial tissue and systemic circulation by the paracellular pathway, TJ are known today to be dynamic structures involved in cellular differentiation, cell signaling (Harder and Margolis 2008), polarized vesicle trafficking, and protein synthesis. 

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