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 ◽  
...  

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.

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Taher Hajilounezhad ◽  
Rina Bao ◽  
Kannappan Palaniappan ◽  
Filiz Bunyak ◽  
Prasad Calyam ◽  
...  

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.


Nanoscale ◽  
2016 ◽  
Vol 8 (1) ◽  
pp. 162-171 ◽  
Author(s):  
Guohai Chen ◽  
Robert C. Davis ◽  
Don N. Futaba ◽  
Shunsuke Sakurai ◽  
Kazufumi Kobashi ◽  
...  

We report the existence of a SWCNT “sweet spot” in the CNT diameter and spacing domain for highly efficient synthesis, within which SWCNTs possessed a unique set of characteristics.


2016 ◽  
Vol 18 ◽  
pp. 67-70 ◽  
Author(s):  
Masud Rana ◽  
MAsyraf MRazib ◽  
T. Saleh ◽  
Asan G.A. Muthalif

2019 ◽  
Vol 9 (22) ◽  
pp. 1970082 ◽  
Author(s):  
Changyong Cao ◽  
Yihao Zhou ◽  
Stephen Ubnoske ◽  
Jianfeng Zang ◽  
Yunteng Cao ◽  
...  

2010 ◽  
Vol 108 (2) ◽  
pp. 024311 ◽  
Author(s):  
C. Zhang ◽  
F. Yan ◽  
C. S. Allen ◽  
B. C. Bayer ◽  
S. Hofmann ◽  
...  

Author(s):  
Yayong Liu ◽  
Narayan C. Das ◽  
Howard Wang ◽  
Guangneng Zhang ◽  
Junghyun Cho ◽  
...  

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.


ACS Nano ◽  
2010 ◽  
Vol 4 (12) ◽  
pp. 7431-7436 ◽  
Author(s):  
Santiago Esconjauregui ◽  
Martin Fouquet ◽  
Bernhard C. Bayer ◽  
Caterina Ducati ◽  
Rita Smajda ◽  
...  

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