Carbon-nanotube-templated carbon nanofibers with improved mechanical performance

2021 ◽  
Vol 129 (4) ◽  
pp. 044303
Author(s):  
Wei Zhao ◽  
Yongqiang Zhang ◽  
Xiaoguang Wang ◽  
Huanhuan Lu ◽  
Guozhu Liu ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Carbon Nanofibers ◽  
Mechanical Performance ◽  
Templated Carbon

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.

Silicon nanoparticle and carbon nanotube loaded carbon nanofibers for use in lithium-ion battery anodes

2014 ◽  
Vol 198 ◽  
pp. 36-40 ◽  
Author(s):  
Nguyen Trung Hieu ◽  
Jungdon Suk ◽  
Dong Wook Kim ◽  
Ok Hee Chung ◽  
Jun Seo Park ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Lithium Ion Battery ◽  
Carbon Nanofibers ◽  
Lithium Ion ◽  
Silicon Nanoparticle

Improved graphitization and electrical conductivity of suspended carbon nanofibers derived from carbon nanotube/polyacrylonitrile composites by directed electrospinning

Carbon ◽  
2012 ◽  
Vol 50 (5) ◽  
pp. 1753-1761 ◽  
Author(s):  
Tanmoy Maitra ◽  
Swati Sharma ◽  
Alok Srivastava ◽  
Yoon-Kyoung Cho ◽  
Marc Madou ◽  
...  
Keyword(s):  
Electrical Conductivity ◽  
Carbon Nanotube ◽  
Carbon Nanofibers

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.

scholarly journals On the mechanical properties of atomic and 3D printed zeolite-templated carbon nanotube networks

2020 ◽  
pp. 101628
Author(s):  
Rushikesh S. Ambekar ◽  
Eliezer F. Oliveira ◽  
Brijesh Kushwaha ◽  
Varinder Pal ◽  
Leonardo D. Machado ◽  
...  
Keyword(s):  
Mechanical Properties ◽  
Carbon Nanotube ◽  
3D Printed ◽  
Carbon Nanotube Networks ◽  
Templated Carbon
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