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Models Networking

Model Networks. Constmction of model networks allows development of quantitative stmcture property relationships and provide the abiUty to test the accuracy of the theories of mbber elasticity (251—254). By definition, model networks have controlled molecular weight between cross-links, controlled cross-link functionahty, and controlled molecular weight distribution of cross-linked chains. Sihcones cross-linked by either condensation or addition reactions are ideally suited for these studies because all of the above parameters can be controlled. A typical condensation-cure model network consists of an a, CO-polydimethylsiloxanediol, tetraethoxysilane (or alkyltrimethoxysilane), and a tin-cure catalyst (255). A typical addition-cure model is composed of a, ffl-vinylpolydimethylsiloxane, tetrakis(dimethylsiloxy)silane, and a platinum-cure catalyst (256—258). [Pg.49]

The purpose of this review is to show how anionic polymerization techniques have successfully contributed to the synthesis of a great variety of tailor-made polymer species Homopolymers of controlled molecular weight, co-functional polymers including macromonomers, cyclic macromolecules, star-shaped polymers and model networks, block copolymers and graft copolymers. [Pg.170]

Microstructure (see also Stereochemistry and Tacticity) 114,115.128.138,139 Miscibility (see also Compatibility) 12, 53, 68 Model networks 163 Modification of a polymer, chemical 154 Mold release 71, 74 Molecular weight, control 147. 154... [Pg.252]

The effect of restricted junction fluctuations on S(x) is to change the scattering function monotonically from that exhibited by a phantom network to that of the fixed junction model. Network unfolding produces the reverse trend, the change of S(x) with x is even less than that exhibited by a phantom network. Figure 6 illustrates how the scattering function is modified by these two opposing influences. [Pg.267]

Parallel to the development of the new theoretical approaches considerable experimental work was done on model networks especially synthesized, to show the effects of pendent chains, loops, distribution of chain length, functionality of crosslinks, etc. on properties (5-21). In some instances, the properties turned out... [Pg.309]

Until recently ( 1 5 ) investigations utilizing model networks had been limited to functionalities of four or less. Networks with higher functionality are predicted by the various theories of rubber elasticity to display unique equilibrium tensile behavior. As such, these multifunctional networks provide insight into the controversy surrounding these theories. The present study addresses the synthesis and equilibrium tensile behavior of endlinked model multifunctional poly(diraethylsilox-ane) (PDMS) networks. [Pg.330]

As is clear from the earlier discussions of pre-gel intramolecular reaction, such reaction in principle always occurs in random polymerisations, although its amount may be reduced by using reactants of higher molar mass, lower functionalities, and stiffer chain structures. Thus, the use of end-linking reactions to produce model networks (for example(35) and references quoted... [Pg.393]

Model networks were prepared using hydroxyl terminated polymer and isocyanates, (a) Bifunctional hydroxyl terminated polybutadiene (Butarez, from Phillips Petroleum) was crosslinkined with tris (p-isocyanatophenyl)-thiophosphate (Desmodur RF, from Mobay Chemical Co.). This crosslinked... [Pg.456]

We appreciate the guidance of Pro . R.W. Lenz in the synthesis of model networks, and the help of Drs. Roberto Russo and Ulku Yilmazer in initial synthesis work. This work was supported in part by a grant from the Center for University of Massachusetts-lndustry Research on Polymers (CUMIRP). We appreciate the donation of the Duragen samples by the General Tire and Rubber Co., the isocyanate samples by the Mobay Chemical Co., and the Butarez samples by the Phillips Petroleum Co. [Pg.479]

Monocyclopentadienyl compounds, 25 116 Monodentate chelants, 5 709 Monodentate ligands, 9 396 Monodisperse model networks, with silicone, 22 570... [Pg.600]

See also Biodegradable polymer networks Filled silicone networks Interpenetrating networks (IPNs) Model silicone networks Monodisperse model networks ... [Pg.616]

Tensile modulus (Young s modulus), 20 177, 29 743, 20 346 monodisperse model networks and, 22 570... [Pg.927]

A. Ciliberto, F. Capuani, and J. J. Tyson2, Modeling networks of coupled enzymatic reactions using the total quasi steady state approximation. PLoS Comput. Biol. 3(3), e45 (2007). [Pg.241]

Note 1 A model network can be prepared using a non-linear polymerization or by crosslinking of existing polymer chains. [Pg.223]

Note 2 A model network is not necessarily a perfect network. If a non-linear polymerization is used to prepare the network, non-stoichiometric amounts of reactants or incomplete reaction can lead to network containing loose ends. If the crosslinking of existing polymer chains is used to prepare the network, then two loose ends per existing polymer chain result. In the absence of chain entanglements, loose ends can never be elastically active network chains. [Pg.223]

Note 3 In addition to loose ends, model networks usually contain ring structures as network imperfections. [Pg.223]


See other pages where Models Networking is mentioned: [Pg.49]    [Pg.159]    [Pg.251]    [Pg.98]    [Pg.214]    [Pg.156]    [Pg.145]    [Pg.163]    [Pg.245]    [Pg.797]    [Pg.60]    [Pg.63]    [Pg.359]    [Pg.329]    [Pg.350]    [Pg.350]    [Pg.453]    [Pg.583]    [Pg.681]    [Pg.680]    [Pg.727]    [Pg.923]    [Pg.932]    [Pg.669]    [Pg.670]    [Pg.218]    [Pg.223]    [Pg.235]    [Pg.157]    [Pg.61]    [Pg.63]   


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Actor-network model

Affine network model relationships

Affine network model, rubber elasticity

Application of neural networks to modelling, estimation and control

Artificial Neural Network (ANN) Models

Artificial neural network model

Artificial neural networks based models

Artificial neural networks based models accuracy

Artificial neural networks based models approach, applications

Artificial neural networks based models example

Artificial neural networks based models training

Artificial neural networks based models weighting

Biochemical reaction network modeling

Boolean network modeling

Boolean network modeling generally

Brain neural network model

Calculations with cross-linked network model

Chemotactic networks spatial models

Classification of Supply Network Optimization Models

Cluster network model

Cluster network model of ion

Clustering cluster-network model

Comparison of the Langevin-network model with experiments

Composite networks models

Computational modeling network

Computational neural network predictive modeling

Continuous Random Network Model

Continuous network model

Cyclodextrins as Model Compounds to Study Hydrogen-Bonding Networks

Deformation uniaxially deformed model network

Dual Network Fluoropolymer (DNF) Model

Elastic Network Model

Elastomeric networks affine network model

Electric network models

Elementary reaction network modeling

Empirical models artificial neural networks

Entanglement model elastomeric networks

Entanglement network tube model

Equivalent network model

Eukaryotic chemosensing, signaling networks computational model

Experimental data modeling neural networks

FCC Neural Network Model

Filtration network models

Fishing for Functional Motions with Elastic Network Models

Flory network model

Flow network modeling

Forecasting model, network demand

Genetic regulatory network model

Gierke cluster network model

Heat-exchanger network synthesis MINLP model

Homo-IPNs as Model Networks

Kinetic Models and Networks

Kinetic models / networks reforming

Kinetic network model

Kraus model, filler networking

Long model network

Macroscopic network models

Materials modeling neural networks

Memory, neural network models

Metabolic modeling topological network analysis

Model chemical reaction network

Model enzymatic network

Model for Gene Network Analysis

Model network

Model network

Model networks, preparation

Model of liquid crystal networks

Modeling Dynamic Stress Softening as a Filler Network Effect

Modeling Dynamic Stress Softening as a Filler-Polymer Network Effect

Modeling Specialty Chemicals Production Networks

Modeling network

Modeling of Biochemical Networks and Experimental Design

Modeling of Metabolic Networks

Modeling of Network Formation

Modeling with artificial neural networks

Modeling/simulation neural network models

Modelling Networks in Varying Dimensions

Modelling topological network

Models of transient networks

Models square-lattice network

Modified Random Network model

Multivariate statistical models Neural-network analysis

Nafion cluster-network model

Network Models of Ion Aggregation

Network Models with Some Non-Classical Character

Network affine model

Network design models

Network design models logistics

Network formation modeling

Network junction model

Network junction model development

Network model construction

Network model, random

Network modelling

Network modelling

Network modelling of non-Newtonian fluids in porous media

Network models, silica surfaces

Network of zones model

Network optimization models

Network polymer model

Network structure models

Network, aggregate model

Network-based constitutive model

Network-formation models

Networking Open Systems Interconnect Model

Networks, bimodal short-chain model

Neural Network Based Modelling

Neural Network Model

Neural Network Model for Memory

Neural Network-Based Model Reference Adaptive Control

Neural Networks Used for Modeling of Processes Involving Pharmaceutical Polymers

Neural Networks and Model Inversion

Neural network calibration models

Neural network modeling

Neural network modeling direct

Neural network modeling hybrid

Neural network modeling inverse

Neural network modeling stack

Neural network models, for

Neural networks Hopfield model

Neural networks McCulloch-Pitts model

Neural networks based models

Neural networks model development

Neural networks single-descriptor models

Neural networks stochastic models

Perfluorinated cluster-network model

Phantom network model

Phantom network model relationships

Pipeline network elements modeling

Polymer electrolyte membrane fuel cell pore network modelling

Pore network model

Pore network modelling

Pore network modelling diffusion

Pore network modelling modelled diffusion

Pore network modelling porosity distributions

Pore network modelling space

Pore network modelling steady state

Pore network modelling trapping

Pore phase, stochastic network model

Probabilistic network models

Production systems, neural networks, and hybrid models

Proton transport Random network model

Pseudo-network model

Random Network Model of Membrane Conductivity

Reverse logistics, network design models

Simple electric network models

Simple model network representation

Sources and Computational Approaches for Generating Models of Gene Regulatory Networks

Spatially dependent network model

Statistical models artificial neural network

Statistical network models

Stress, reduced affine network model

Stress, reduced phantom network model

Structural kinetic modeling network analysis

Supply Chain Network Modeling

Surface models Curve network

Synthesis of model networks

Temporary network model

Tests of Theoretical Modulus Values—Model Networks

Tetrafunctional phantom network model

The Model of a Network Polymer

The Phantom Network Model

The Temporary Network Model

Topology, metabolic network modeling

Toxicity, neural network modeling

Transient Network Models for Viscoelastic Properties in the Terminal Zone

Transient double-network model

Transient network model

Typical Calculations with the Network Junction Model

Uniaxially Deformed Model Networks

Water network modeling

Water random network model

Yushu, China earthquake struck area using artificial neural network model

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