Aligned Carbon Nanotube Polymer Composites

2007 ◽  
Author(s):  
Yayong Liu ◽  
Narayan C. Das ◽  
Howard Wang ◽  
Guangneng Zhang ◽  
Junghyun Cho ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Polymer Nanocomposites ◽  
Growth Direction ◽  
Unique Combination ◽  
Structure Property ◽  
New Materials ◽  
Electrical Conductivities ◽  
Structure Property Relationship ◽  
Vertically Aligned ◽  
Tube Density

Carbon nanotube (CNT) polymer nanocomposites are promising new materials with a unique combination of mechanical and transport properties. As physical properties such as mechanical behavior, dielectric relaxation, thermal and electrical conductivities depend strongly on morphological structures of composites, we illustrate in this study the structure/property relationship in vertically aligned CNT (VACNT)/polymer nanocomposites. We have prepared VACNT/polystyrene composites and characterized their morphologies and properties. We have reported previously the continuous variation of alignment order along the height of CNT, which remain unaltered upon forming composites as revealed by small angle neutron scattering (SANS). Nanoindentation shows that both the elastic modulus and hardness vary along the CNT growth direction due to the varying tube density, alignment order and entanglement.

Characterization and structure–property relationship of melt-mixed high density polyethylene/multi-walled carbon nanotube composites under extensional deformation

RSC Advances ◽  
2015 ◽  
Vol 5 (59) ◽  
pp. 47555-47568 ◽  
Author(s):  
Dong Xiang ◽  
Eileen Harkin-Jones ◽  
David Linton
Keyword(s):  
Carbon Nanotube ◽  
Strain Rate ◽  
High Density Polyethylene ◽  
Structure Property ◽  
Extensional Deformation ◽  
Multi Walled Carbon Nanotube ◽  
Structure Property Relationship ◽  
Nanotube Composites ◽  
Carbon Nanotube Composites ◽  
Relationship Of

Disentanglement degree of nanotube agglomerates depends on the stretching mode, strain rate and stretching temperatures under extensional deformation.

scholarly journals Multi-Terminal Nanotube Junctions: Modeling and Structure-Property Relationship

2021 ◽  
Vol 8 ◽  
Author(s):  
Sushan Nakarmi ◽  
Vinu U. Unnikrishnan ◽  
Vikas Varshney ◽  
Ajit K. Roy
Keyword(s):  
Carbon Nanotube ◽  
Computer Aided Design ◽  
3D Structure ◽  
Structure Property ◽  
High Concentration ◽  
Property Relationship ◽  
Atomistic Models ◽  
Structure Property Relationship ◽  
Dynamics Simulations ◽  
Aided Design

Carbon nanotube based multi-terminal junction configurations are of great interest because of the potential aerospace and electronic applications. Multi-terminal carbon nanotube junction has more than one carbon nanotube meeting at a point to create a 2D or 3D structure. Accurate atomistic models of such junctions are essential for characterizing their thermal, mechanical and electronic properties via computational studies. In this work, computational methodologies that uses innovative Computer-Aided Design (CAD) based optimization strategies and remeshing techniques are presented for generating such topologically reliable and accurate models of complex multi-terminal junctions (called 3-, 4-, and 6-junctions). This is followed by the prediction of structure-property relationship via study of thermal conductivity and mechanical strength using molecular dynamics simulations. We observed high degradation in the thermal and mechanical properties of the junctions compared to pristine structures which is attributed to high concentration of non-hexagonal defects in the junction. Junctions with fewer defects have better thermal transport capabilities and higher mechanical strengths, suggesting that controlling the number of defects can significantly improve inherent features of the nanostructures.

Mining structure–property relationships in polymer nanocomposites using data driven finite element analysis and multi-task convolutional neural networks

2020 ◽  
Vol 5 (5) ◽  
pp. 962-975
Author(s):  
Yixing Wang ◽  
Min Zhang ◽  
Anqi Lin ◽  
Akshay Iyer ◽  
Aditya Shanker Prasad ◽  
...  
Keyword(s):  
Neural Networks ◽  
Polymer Nanocomposites ◽  
Data Driven ◽  
Learning Approach ◽  
Structure Property ◽  
Element Analysis ◽  
Structure Property Relationships ◽  
Structure Property Relationship ◽  
Using Data ◽  
Relationship Of

In this paper, a data driven and deep learning approach for modeling structure–property relationship of polymer nanocomposites is demonstrated. This method is applicable to understand other material mechanisms and guide the design of material with targeted performance.

scholarly journals Characterization of Vertically Aligned Carbon Nanotube Forests Grown on Stainless Steel Surfaces

Nanomaterials ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 444 ◽  
Author(s):  
Eleftheria Roumeli ◽  
Marianna Diamantopoulou ◽  
Marc Serra-Garcia ◽  
Paul Johanns ◽  
Giulio Parcianello ◽  
...  
Keyword(s):  
Stainless Steel ◽  
Carbon Nanotube ◽  
Mechanical Performance ◽  
High Energy ◽  
Structure Property ◽  
Wetting Properties ◽  
Vertically Aligned ◽  
High Energy Absorption ◽  
Influence Of Substrate ◽  
Carbon Nanotube Forests

Vertically aligned carbon nanotube (CNT) forests are a particularly interesting class of nanomaterials, because they combine multifunctional properties, such as high energy absorption, compressive strength, recoverability, and super-hydrophobicity with light weight. These characteristics make them suitable for application as coating, protective layers, and antifouling substrates for metallic pipelines and blades. Direct growth of CNT forests on metals offers the possibility of transferring the tunable CNT functionalities directly onto the desired substrates. Here, we focus on characterizing the structure and mechanical properties, as well as wettability and adhesion, of CNT forests grown on different types of stainless steel. We investigate the correlations between composition and morphology of the steel substrates with the micro-structure of the CNTs and reveal how the latter ultimately controls the mechanical and wetting properties of the CNT forest. Additionally, we study the influence of substrate morphology on the adhesion of CNTs to their substrate. We highlight that the same structure-property relationships govern the mechanical performance of CNT forests grown on steels and on Si.

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