scholarly journals Self-Assembly of Graphene Nanoribbons Induced by the Carbon Nanotube

10.5772/67413 ◽  
2017 ◽  
Author(s):  
Hui Li ◽  
Yifan Li ◽  
Wei Chen
Keyword(s):  
Carbon Nanotube ◽  
Self Assembly ◽  
Graphene Nanoribbons

2D Self-assembly and Electronic Characterization of Oxygen-Boron-Oxygen-Doped Chiral Graphene Nanoribbons

2021 ◽  
Author(s):  
Lei Jin ◽  
Nerea Bilbao ◽  
Yang Lv ◽  
Xiao-Ye Wang ◽  
Soltani Paniz ◽  
...  
Keyword(s):  
Edge Effects ◽  
Self Assembly ◽  
Quantum Confinement ◽  
Graphene Nanoribbons ◽  
One Dimensional ◽  
The Past ◽  
Different Types

Graphene nanoribbons (GNRs), quasi-one-dimensional strips of graphene, exhibit a nonzero bandgap due to quantum confinement and edge effects. In the past decade, different types of GNRs with atomically precise structures...

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.

Carbon nanotube-dependent synthesis of armchair graphene nanoribbons

Nano Research ◽  
2021 ◽  
Author(s):  
Yifan Zhang ◽  
Kecheng Cao ◽  
Takeshi Saito ◽  
Hiromichi Kataura ◽  
Hans Kuzmany ◽  
...  
Keyword(s):  
Carbon Nanotube ◽  
Graphene Nanoribbons ◽  
Armchair Graphene Nanoribbons ◽  
Armchair Graphene

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

Interpolymer Self-Assembly of Bottom-up Graphene Nanoribbons Fabricated from Fluorinated Precursors

2018 ◽  
Vol 10 (37) ◽  
pp. 31623-31630 ◽  
Author(s):  
Manabu Ohtomo ◽  
Hideyuki Jippo ◽  
Hironobu Hayashi ◽  
Junichi Yamaguchi ◽  
Mari Ohfuchi ◽  
...  
Keyword(s):  
Self Assembly ◽  
Graphene Nanoribbons ◽  
Bottom Up

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
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