Reinforcement of Rubber and Filler Network Dynamics at Small Strains

2016 ◽  
pp. 71-102 ◽  
Author(s):  
Lewis B. Tunnicliffe ◽  
James J. C. Busfield
Keyword(s):  
Network Dynamics ◽  
Small Strains ◽  
Filler Network

Dynamics of Nanoparticle Chain Aggregates of Carbon Under Tension

2003 ◽  
Vol 778 ◽  
Author(s):  
Rajdip Bandyopadhyaya ◽  
Weizhi Rong ◽  
Yong J. Suh ◽  
Sheldon K. Friedlander
Keyword(s):  
Mechanical Properties ◽  
Carbon Black ◽  
Polymer Film ◽  
Elastic Behavior ◽  
Small Strains ◽  
Transmission Electron ◽  
Chain Aggregates ◽  
Nanoparticle Chains ◽  
Reinforcing Filler

AbstractCarbon black in the form of nanoparticle chains is used as a reinforcing filler in elastomers. However, the dynamics of the filler particles under tension and their role in the improvement of the mechanical properties of rubber are not well understood. We have studied experimentally the dynamics of isolated nanoparticle chain aggregates (NCAs) of carbon made by laser ablation, and also that of carbon black embedded in a polymer film. In situ studies of stretching and contraction of such chains in the transmission electron microscope (TEM) were conducted under different maximum values of strain. Stretching causes initially folded NCA to reorganize into a straight, taut configuration. Further stretching leads to either plastic deformation and breakage (at 37.4% strain) or to a partial elastic behavior of the chain at small strains (e.g. 2.3% strain). For all cases the chains were very flexible under tension. Similar reorientation and stretching was observed for carbon black chains embedded in a polymer film. Such flexible and elastic nature of NCAs point towards a possible mechanism of reinforcement of rubber by carbon black fillers.

scholarly journals Transient neural network dynamics in cognitive ageing

2021 ◽  
Author(s):  
Roni Tibon ◽  
Kamen A. Tsvetanov ◽  
Darren Price ◽  
David Nesbitt ◽  
Cam CAN ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Dynamics ◽  
Cognitive Ageing ◽  
Neural Network Dynamics

scholarly journals A Generalized Approach for Evaluating the Mechanical Properties of Polymer Nanocomposites Reinforced with Spherical Fillers

Nanomaterials ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 830
Author(s):  
Julio Cesar Martinez-Garcia ◽  
Alexandre Serraïma-Ferrer ◽  
Aitor Lopeandía-Fernández ◽  
Marco Lattuada ◽  
Janak Sapkota ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Polymeric Nanocomposites ◽  
Mechanical Reinforcement ◽  
Future Studies ◽  
Three Phase ◽  
Theoretical Predictions ◽  
Filler Network ◽  
New Research ◽  
Good Agreement ◽  
Research Avenue

In this work, the effective mechanical reinforcement of polymeric nanocomposites containing spherical particle fillers is predicted based on a generalized analytical three-phase-series-parallel model, considering the concepts of percolation and the interfacial glassy region. While the concept of percolation is solely taken as a contribution of the filler-network, we herein show that the glassy interphase between filler and matrix, which is often in the nanometers range, is also to be considered while interpreting enhanced mechanical properties of particulate filled polymeric nanocomposites. To demonstrate the relevance of the proposed generalized equation, we have fitted several experimental results which show a good agreement with theoretical predictions. Thus, the approach presented here can be valuable to elucidate new possible conceptual routes for the creation of new materials with fundamental technological applications and can open a new research avenue for future studies.

scholarly journals Common Origin of Filler Network Related Contributions to Reinforcement and Dissipation in Rubber Composites

Polymers ◽  
2021 ◽  
Vol 13 (15) ◽  
pp. 2534
Author(s):  
Sriharish Malebennur Nagaraja ◽  
Sven Henning ◽  
Sybill Ilisch ◽  
Mario Beiner
Keyword(s):  
Network Topology ◽  
Filler Content ◽  
Matrix Composition ◽  
Rubber Matrix ◽  
Butadiene Rubber ◽  
Rubber Composites ◽  
Different Temperatures ◽  
Filler Network ◽  
General Perspective ◽  
Rubber Filler

A comparative study focusing on the visco–elastic properties of two series of carbon black filled composites with natural rubber (NR) and its blends with butadiene rubber (NR-BR) as matrices is reported. Strain sweeps at different temperatures are performed. Filler network-related contributions to reinforcement (ΔG′) are quantified by the classical Kraus equation while a modified Kraus equation is used to quantify different contributions to dissipation (ΔGD″, ΔGF″). Results indicate that the filler network is visco-elastic in nature and that it is causing a major part of the composite dissipation at small and intermediate strain amplitudes. The temperature dependence of filler network-related reinforcement and dissipation contributions is found to depend significantly on the rubber matrix composition. We propose that this is due to differences in the chemical composition of the glassy rubber bridges connecting filler particles since the filler network topology is seemingly not significantly influenced by the rubber matrix for a given filler content. The underlying physical picture explains effects in both dissipation and reinforcement. It predicts that these glassy rubber bridges will soften sequentially at temperatures much higher than the bulk Tg of the corresponding rubber. This is hypothetically due to rubber–filler interactions at interfaces resulting in an increased packing density in the glassy rubber related to the reduction of free volume. From a general perspective, this study provides deeper insights towards the molecular origin of reinforcement and dissipation in rubber composites.

scholarly journals Data-driven control of complex networks

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Giacomo Baggio ◽  
Danielle S. Bassett ◽  
Fabio Pasqualetti
Keyword(s):  
Complex Networks ◽  
Network Dynamics ◽  
Noisy Data ◽  
Data Driven ◽  
Control Algorithms ◽  
Optimal Controls ◽  
Network Systems ◽  
The Past ◽  
Efficient Control ◽  
Finite Set

AbstractOur ability to manipulate the behavior of complex networks depends on the design of efficient control algorithms and, critically, on the availability of an accurate and tractable model of the network dynamics. While the design of control algorithms for network systems has seen notable advances in the past few years, knowledge of the network dynamics is a ubiquitous assumption that is difficult to satisfy in practice. In this paper we overcome this limitation, and develop a data-driven framework to control a complex network optimally and without any knowledge of the network dynamics. Our optimal controls are constructed using a finite set of data, where the unknown network is stimulated with arbitrary and possibly random inputs. Although our controls are provably correct for networks with linear dynamics, we also characterize their performance against noisy data and in the presence of nonlinear dynamics, as they arise in power grid and brain networks.

scholarly journals Neural network dynamics underlying gamma synchronization deficits in schizophrenia

2021 ◽  
Vol 107 ◽  
pp. 110224
Author(s):  
Daisuke Koshiyama ◽  
Makoto Miyakoshi ◽  
Yash B. Joshi ◽  
Juan L. Molina ◽  
Kumiko Tanaka-Koshiyama ◽  
...  
Keyword(s):  
Neural Network ◽  
Network Dynamics ◽  
Neural Network Dynamics
Sign in / Sign up

Export Citation Format

Share Document