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Network effects

Everett concludes that in systems where pore blocking can occur, pore size distribution curves derived from the desorption branch of the isotherm are likely to give a misleading picture of the pore structure in particular the size distribution will appear to be much narrower than it actually is. Thus the adsorption branch is to be preferred unless network effects are known to be absent. [Pg.151]

The persistent and distinctive structural features of the GS network effectively reduce crystal engineering to the last remaining (third) dimension. These features prompted our laboratory to synthesize related materials in which the monosulfonate components were replaced with disulfonates. We surmised that this would lead to a two-fold reduction in the amount of space occupied by the organic residues between the GS sheets, creating frameworks... [Pg.224]

With hysteresis loops of Type HI, the two branches are almost vertical and nearly parallel. Such loops are often associated with porous materials which are known to have very narrow pore size distributions or agglomerates of approximately uniform spheres in fairly regular array. More common are loops of Type H2, where the pore size distribution and shape are not well defined. This is attributed to the difference in adsorption and desorption mechanisms occurring in ink-bottle pores, and network effects. The Type H3 hysteresis loop does not show any limiting adsorption at high relative pressures and is observed in aggregates and macroporous materials. Loops of Type H4 are often associated with narrow... [Pg.19]

As discussed above, hysteresis loops can appear in sorption isotherms as result of different adsorption and desorption mechanisms arising in single pores. A porous material is usually built up of interconnected pores of irregular size and geometry. Even if the adsorption mechanism is reversible, hysteresis can still occur because of network effects which are now widely accepted as being a percolation problem [21, 81] associated with specific pore connectivities. Percolation theory for the description of connectivity-related phenomena was first introduced by Broad-bent et al. [88]. Following this approach, Seaton [89] has proposed a method for the determination of connectivity parameters from nitrogen sorption measurements. [Pg.23]

A typical network effect is illustrated in Fig. 1.15 in the case of three pores of different size. During adsorption the smallest pores are filled first and the largest pores last, resulting in the filling sequence A, B and C. The vapor needed to fill remote pores can be transported either through the liquid or vapor phase. During the desorption process, the order in which the liquid nitrogen would become unstable is C, B and A, i.e. the reverse. [Pg.23]

Communication and co-operation in actors networks supports innovations (especially in vertical communication along the supply chain). Where these factors are missing the result may be (undesired) innovation blockades because a lack of concurrence, differences in interests of the actors involved or the lack of desire to co-operate of individual important players cannot be overcome (negative network effects). [Pg.105]

Also the Quality Community on mineral wool and the industiy regulation on low-chromate cement are examples of a co-operation for hazardous substance substitution that does finally function. The example of UV-drying printing inks, on the other hand, does demonstrate under which combination of negative network effects in the innovation system the diffusion of a technology can also fail (at least provisionally). [Pg.105]

In an economic context, the facts described above, namely that operations in one part of the network effect the conditions in the rest of the network, are referred to as economic externalities. A clear understanding of externalities is important because both producers and buyers may benefit or suffer from such externalities. [Pg.327]

Derouane, E. G., Lefebvre, C. and Nagy, J. B. Channel network effects in ethylene oligomerization and aromatization using HZSM-5, HZSM-11 and HZSM-48 catalysts. An in situ carbon-13 NMR study, J. Mol. Catal., 1986, 38, 387-391. [Pg.137]

The presence of pore network effects and interconnectivity can of course strongly influence the reliability of the data obtained via both techniques. Therefore development of new models describing these effects combined with the measurement of the contact angle will strongly contribute to assess the PSD from MIP measurements. [Pg.97]

H2-type hysteresis corresponds to porous solids where the distribution of pore shape and size is not uniform and network effects (blocking of pores) play a significant role (as with aggregates where particles are not strongly interlinked). [Pg.19]

The non-local density functional theory (NLDFT) with properly chosen parameters of fluid-fluid and fluid-solid intermolecular interactions quantitatively predicts both adsorption and desorption branches of capillary condensation isotherms on MCM-41 materials with the pore sizes from 5 to 10 nm. Both experimental branches can be used for calculating the pore size distributions in this pore size range. However for the samples with smaller pores, the desorption branch has an advantage of being theoretically accurate. Thus, we recommend to use the desorption isotherms for estimating the pore size distributions in mesoporous materials of MCM-41 type, provided that the pore networking effects are absent. [Pg.59]

Nitrogen sorption isotherms at 77 K were calculated by means of the simulated 3D networks. Besides the Kelvin equation, necessary for determining the critical radius of curvature Rc, at which condensation and evaporation would occur, it is also necessary to consider specific menisci interactions and network effects that can influence the sorption phenomena [5, 7]. The existence of an adsorbed layer is indeed of great importance on the outcome of a sorption process, but for simplicity it will not be considered in this treatment. [Pg.128]

These results indicate that the shape of sorption isotherms of pure fluids on MCM-48 silicas, i.e. the occurence of pore condensation and sorption hysteresis as well as details of the hysteresis loop depend on the pore size and temperature. The observed hysteresis loops are of type HI, indicating that networking effects are not dominant for sorption hysteresis in the MCM-48 silica materials studied here, despite the fact that MCM-48 consists of a unique three dimensional pore network. [Pg.266]

Gas adsorption desorption Kelvin (B.E.T. B.J.H.) Cylindrical or slits 2-50 nm Pore size distribution (including dead-end pores). Pore shape information. Specific surface area. Porosity Dry samples. Main problem relationship between the pore geometry eind a model which allows the pore sizes and pore size distribution to be determined from the isotherms. Network effect. [Pg.107]

Liquid permeability Hagen- Poiseuille Kozeny- Carman Cylindrical Voids between spheres 0.1-10 p Pore hydraulic radius Experimental simplicity. Assumptions laminar flow in HP equation, zero wetting angle, no pre-existing agent on the surface. Great influence of pore geometry and tortuosity on the interpretation of results. Network effects. [Pg.109]

In hybrids with ds probes, DeSO4 is often found to give stronger signals, probably due to the enhanced networking effect of this... [Pg.153]

Creep Behavior of Amine-Cured Epoxy Networks Effect of Stoichiometry... [Pg.183]


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See also in sourсe #XX -- [ Pg.23 ]




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Effective network chains

Effects of Network Chain Length Distribution

Elastomeric networks chain length effects

Filler network effect

Filler network effect Dynamic stress softening

Mechanical Field Effects on Liquid-Crystalline Networks

Mechanical field effect, networks

Modeling Dynamic Stress Softening as a Filler Network Effect

Modeling Dynamic Stress Softening as a Filler-Polymer Network Effect

Network Structure in Oil-Extended Rubbers - Effect of Chain Entanglements

Network and Pore Connectivity Effects

Network dissolution effects, leaching

Network structure mechanical property effects

Network theory 136, effects

Network-percolation effect

Neural networks, structural effects

Payne effect, filler networking

Polymer Networks with Shape Memory Effect

Strain Dependent Effects in Networks

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