Prediction of Young’s modulus of single wall carbon nanotubes by molecular-mechanics based finite element modelling

2006 ◽  
Vol 66 (11-12) ◽  
pp. 1597-1605 ◽  
Author(s):  
M MEO ◽  
M ROSSI
Keyword(s):  
Carbon Nanotubes ◽  
Finite Element ◽  
Molecular Mechanics ◽  
Young’S Modulus ◽  
Finite Element Modelling ◽  
Young's Modulus ◽  
Single Wall Carbon Nanotubes ◽  
Element Modelling ◽  
Single Wall Carbon

scholarly journals EFFECT OF HELICITY ON THE BUCKLING BEHAVIOR OF SINGLE-WALL CARBON NANOTUBES

2008 ◽  
Vol 22 (31n32) ◽  
pp. 5872-5877 ◽  
Author(s):  
JEEHYANG HUH ◽  
HOON HUH
Keyword(s):  
Carbon Nanotubes ◽  
Carbon Nanotube ◽  
Young’S Modulus ◽  
Interatomic Potential ◽  
Young's Modulus ◽  
Compressive Force ◽  
Buckling Load ◽  
Single Wall Carbon Nanotubes ◽  
Single Wall Carbon ◽  
Buckling Behavior

Simulations of single-wall carbon nanotube(SWCNT)s having a different chiral vector under axial compression were carried out based on molecular dynamics to investigate the effect of the helicity on the buckling behavior. Calculation was performed at room temperature for (8,8) armchair, (14,0) zigzag and (6,10) chiral single-wall carbon nanotubes. The Tersoff potential was used as the interatomic potential since it describes the C - C bonds in carbon nanotubes reliably. A conjugate gradient (CG) method was used to obtain the equilibrium configuration. Compressive force was applied at the top of a nanotube by moving the top-most atoms downward with the constant velocity of 10m/s. The buckling load, the critical strain, and the Young's modulus were calculated from the result of MD simulation. A zigzag carbon nanotube has the largest Young's modulus and buckling load, while a chiral carbon nonotube has the lowest values.

Material Characterization and Modeling of Single-Wall Carbon Nanotube/Polyelectrolyte Multilayer Nanocomposites

2006 ◽  
Vol 73 (5) ◽  
pp. 737-744 ◽  
Author(s):  
Gang Huang ◽  
Bo Wang ◽  
Hongbing Lu ◽  
Arif Mamedov ◽  
Sachin Gupta
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Young’S Modulus ◽  
Young's Modulus ◽  
Relaxation Modulus ◽  
Nanocomposite Films ◽  
Single Wall Carbon Nanotubes ◽  
Element Analysis ◽  
Single Wall Carbon ◽  
Volume Fractions

Strong single-wall carbon nanotubes (SWNTs) possess very high stiffness and strength. They have potential for use to tailor the material design to reach desired mechanical properties through SWNT nanocomposites. Layer-by-layer (LBL) assembly technique is an effective method to fabricate SWNT/polyelectrolyte nanocomposite films. To determine the relationship between the constituents of the SWNT/polymer nanocomposites made by LBL technique, a method has been developed to extend the recent work by Liu and Chen (Mech. Mater., 35, pp. 69–81, 2003) for the calculation of the effective Young’s modulus. The work by Liu and Chen on the mixture model is evaluated by finite element analysis of nanocomposites with SWNT volume fraction between 0% and 5%. An equivalent length coefficient is introduced and determined from finite element analysis. A formula is presented using this coefficient to determine the effective Young’s modulus. It is identified that the current work can be applied to SWNT loadings between 0% and 5%, while Liu and Chen’s approach is appropriate for relatively high SWNT volume fractions, close to 5%, but is not appropriate for relatively low SWNT volume fractions. The results obtained from this method are used to determine the effective Young’s modulus of SWNT/polyelectrolyte nanocomposite with 4.7% SWNT loading. The material properties are characterized using both nanoindentation and tensile tests. Nanoindentation results indicate that both the in-plane relaxation modulus and the through-thickness relaxation modulus of SWNT nanocomposites are very close to each other, despite the orientation preference of the SWNTs in the nanocomposites. The steady state in-plane Young’s relaxation modulus compares well with the tensile modulus, and measurement results are compared with Young’s modulus determined from the method presented.

Inferring Physical Parameters from Images of Vibrating Carbon Nanotubes

2000 ◽  
Vol 6 (4) ◽  
pp. 317-323 ◽  
Author(s):  
M.M.J. Treacy ◽  
A. Krishnan ◽  
P.N. Yianilos
Keyword(s):  
Carbon Nanotubes ◽  
Young’S Modulus ◽  
Shot Noise ◽  
Vibration Amplitude ◽  
Young's Modulus ◽  
Single Wall Carbon Nanotubes ◽  
Physical Parameters ◽  
Average Value ◽  
Length Width ◽  
Hidden Parameter

Abstract We describe a hidden parameter inferencing algorithm for deducing the length, width, and vibration profile from images of thermally excited single-wall carbon nanotubes. With accurate estimates of these parameters, the Young’s modulus can be deduced. The algorithm is sensitive to shot noise in the image, primarily because of the low nanotube image contrast. Noise causes the nanotube length and width to be overestimated, and the vibration amplitude to be underestimated. After correcting for shot noise, we infer an average value of the Young’s modulus of 〈Y〉= 1.20±0.20 TPa, which is larger than the currently accepted value for graphite.

Hyperelastic finite element model for single wall carbon nanotubes in tension

2011 ◽  
Vol 50 (3) ◽  
pp. 1083-1087 ◽  
Author(s):  
E.I. Saavedra Flores ◽  
S. Adhikari ◽  
M.I. Friswell ◽  
F. Scarpa
Keyword(s):  
Carbon Nanotubes ◽  
Finite Element ◽  
Finite Element Model ◽  
Element Model ◽  
Single Wall Carbon Nanotubes ◽  
Single Wall Carbon

Inferring Physical Parameters from Images of Vibrating Carbon Nanotubes

2000 ◽  
Vol 6 (4) ◽  
pp. 317-323 ◽  
Author(s):  
M.M.J. Treacy ◽  
A. Krishnan ◽  
P.N. Yianilos
Keyword(s):  
Carbon Nanotubes ◽  
Young’S Modulus ◽  
Shot Noise ◽  
Vibration Amplitude ◽  
Young's Modulus ◽  
Single Wall Carbon Nanotubes ◽  
Physical Parameters ◽  
Average Value ◽  
Length Width ◽  
Hidden Parameter

AbstractWe describe a hidden parameter inferencing algorithm for deducing the length, width, and vibration profile from images of thermally excited single-wall carbon nanotubes. With accurate estimates of these parameters, the Young’s modulus can be deduced. The algorithm is sensitive to shot noise in the image, primarily because of the low nanotube image contrast. Noise causes the nanotube length and width to be overestimated, and the vibration amplitude to be underestimated. After correcting for shot noise, we infer an average value of the Young’s modulus of 〈Y〉= 1.20±0.20 TPa, which is larger than the currently accepted value for graphite.

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