scholarly journals Mapping local microstructure and mechanical performance around carbon nanotube grafted silica fibres: Methodologies for hierarchical composites

Nanoscale ◽  
2011 ◽  
Vol 3 (11) ◽  
pp. 4759 ◽  
Author(s):  
Hui Qian ◽  
Gerhard Kalinka ◽  
K. L. Andrew Chan ◽  
Sergei G. Kazarian ◽  
Emile S. Greenhalgh ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Mechanical Performance ◽  
Hierarchical Composites

scholarly journals Influence of Carbon Nanotube-Pretreatment on the Properties of Polydimethylsiloxane/Carbon Nanotube-Nanocomposites

Polymers ◽  
2021 ◽  
Vol 13 (9) ◽  
pp. 1355
Author(s):  
Astrid Diekmann ◽  
Marvin C. V. Omelan ◽  
Ulrich Giese
Keyword(s):  
Electrical Conductivity ◽  
Carbon Nanotube ◽  
Mechanical Performance ◽  
Softening Effect ◽  
Electrical Percolation ◽  
Optimum Parameters ◽  
Lauryl Ether ◽  
Solvent Residues ◽  
Electrical Percolation Threshold ◽  
Filler Dispersion

Incorporating nanofillers into elastomers leads to composites with an enormous potential regarding their properties. Unfortunately, nanofillers tend to form agglomerates inhibiting adequate filler dispersion. Therefore, different carbon nanotube (CNT) pretreatment methods were analyzed in this study to enhance the filler dispersion in polydimethylsiloxane (PDMS)/CNT-composites. By pre-dispersing CNTs in solvents an increase in electrical conductivity could be observed within the sequence of tetrahydrofuran (THF) > acetone > chloroform. Optimization of the pre-dispersion step results in an AC conductivity of 3.2 × 10−4 S/cm at 1 Hz and 0.5 wt.% of CNTs and the electrical percolation threshold is decreased to 0.1 wt.% of CNTs. Optimum parameters imply the use of an ultrasonic finger for 60 min in THF. However, solvent residues cause a softening effect deteriorating the mechanical performance of these composites. Concerning the pretreatment of CNTs by physical functionalization, the use of surfactants (sodium dodecylbenzenesulfonate (SDBS) and polyoxyethylene lauryl ether (“Brij35”)) leads to no improvement, neither in electrical conductivity nor in mechanical properties. Chemical functionalization enhances the compatibility of PDMS and CNT but damages the carbon nanotubes due to the oxidation process so that the improvement in conductivity and reinforcement is superimposed by the CNT damage even for mild oxidation conditions.

scholarly journals Predicting carbon nanotube forest attributes and mechanical properties using simulated images and deep learning

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taher Hajilounezhad ◽  
Rina Bao ◽  
Kannappan Palaniappan ◽  
Filiz Bunyak ◽  
Prasad Calyam ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Carbon Nanotube ◽  
High Throughput ◽  
Self Assembly ◽  
Mechanical Performance ◽  
Physical Parameters ◽  
Structure Property ◽  
Processing Parameter ◽  
Physics Based Simulation

AbstractUnderstanding and controlling the self-assembly of vertically oriented carbon nanotube (CNT) forests is essential for realizing their potential in myriad applications. The governing process–structure–property mechanisms are poorly understood, and the processing parameter space is far too vast to exhaustively explore experimentally. We overcome these limitations by using a physics-based simulation as a high-throughput virtual laboratory and image-based machine learning to relate CNT forest synthesis attributes to their mechanical performance. Using CNTNet, our image-based deep learning classifier module trained with synthetic imagery, combinations of CNT diameter, density, and population growth rate classes were labeled with an accuracy of >91%. The CNTNet regression module predicted CNT forest stiffness and buckling load properties with a lower root-mean-square error than that of a regression predictor based on CNT physical parameters. These results demonstrate that image-based machine learning trained using only simulated imagery can distinguish subtle CNT forest morphological features to predict physical material properties with high accuracy. CNTNet paves the way to incorporate scanning electron microscope imagery for high-throughput material discovery.

scholarly journals Obtaining high mechanical performance silk fibers by feeding purified carbon nanotube/lignosulfonate composite to silkworms

RSC Advances ◽  
2019 ◽  
Vol 9 (7) ◽  
pp. 3558-3569 ◽  
Author(s):  
Hao Xu ◽  
Wenhui Yi ◽  
Dongfan Li ◽  
Ping Zhang ◽  
Sweejiang Yoo ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Mechanical Performance ◽  
Silk Fibers

Silkworm fibers have attracted widespread attention for their superb glossy texture and promising mechanical performance.

Carbon nanotube-based hierarchical composites: a review

2010 ◽  
Vol 20 (23) ◽  
pp. 4751 ◽  
Author(s):  
Hui Qian ◽  
Emile S. Greenhalgh ◽  
Milo S. P. Shaffer ◽  
Alexander Bismarck
Keyword(s):  
Carbon Nanotube ◽  
Hierarchical Composites

High Mechanical Performance Composite Conductor: Multi-Walled Carbon Nanotube Sheet/Bismaleimide Nanocomposites

2009 ◽  
Vol 19 (20) ◽  
pp. 3219-3225 ◽  
Author(s):  
Qunfeng Cheng ◽  
Jianwen Bao ◽  
JinGyu Park ◽  
Zhiyong Liang ◽  
Chuck Zhang ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Mechanical Performance ◽  
Multi Walled Carbon Nanotube ◽  
Composite Conductor
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