Carbon Nanotube Triggered Self‐Assembly of Oligo(p‐phenylene vinylene)s to Stable Hybrid π‐Gels

2008 ◽  
Vol 47 (31) ◽  
pp. 5746-5749 ◽  
Author(s):  
Sampath Srinivasan ◽  
Sukumaran S. Babu ◽  
Vakayil K. Praveen ◽  
Ayyappanpillai Ajayaghosh
Keyword(s):  
Carbon Nanotube ◽  
Self Assembly ◽  
Phenylene Vinylene ◽  
Stable Hybrid

Carbon Nanotube Triggered Self‐Assembly of Oligo(p‐phenylene vinylene)s to Stable Hybrid π‐Gels

2008 ◽  
Vol 120 (31) ◽  
pp. 5830-5833 ◽  
Author(s):  
Sampath Srinivasan ◽  
Sukumaran S. Babu ◽  
Vakayil K. Praveen ◽  
Ayyappanpillai Ajayaghosh
Keyword(s):  
Carbon Nanotube ◽  
Self Assembly ◽  
Phenylene Vinylene ◽  
Stable Hybrid

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.

Self-assembly of single-walled carbon nanotube based on diazoresin

2002 ◽  
Vol 132 (1) ◽  
pp. 5-8 ◽  
Author(s):  
Bo Li ◽  
Tingbing Cao ◽  
Weixiao Cao ◽  
Zujin Shi ◽  
Zhennan Gu
Keyword(s):  
Carbon Nanotube ◽  
Self Assembly ◽  
Single Walled Carbon Nanotube ◽  
Single Walled Carbon

scholarly journals Thermo-Driven Evaporation Self-Assembly and Dynamic Analysis of Homocentric Carbon Nanotube Rings

Small ◽  
2016 ◽  
Vol 13 (8) ◽  
pp. 1603642 ◽  
Author(s):  
Hu Li ◽  
Han Ouyang ◽  
Min Yu ◽  
Nan Wu ◽  
Xinxin Wang ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Dynamic Analysis ◽  
Self Assembly

Self-assembly of dendritic-lamellar MXene/Carbon nanotube conductive films for wearable tactile sensors and artificial skin

Carbon ◽  
2020 ◽  
Vol 164 ◽  
pp. 111-120 ◽  
Author(s):  
Mengmeng Chen ◽  
Xiaoyu Hu ◽  
Kun Li ◽  
Jinkun Sun ◽  
Zhuangjian Liu ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Self Assembly ◽  
Artificial Skin ◽  
Tactile Sensors ◽  
Conductive Films

Field emission from self-assembly structure of carbon-nanotube films

2005 ◽  
Vol 250 (1-4) ◽  
pp. 9-13 ◽  
Author(s):  
L.M. Sheng ◽  
M. Liu ◽  
P. Liu ◽  
Y. Wei ◽  
L. Liu ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Field Emission ◽  
Self Assembly ◽  
Assembly Structure ◽  
Nanotube Films
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