Argon-Beam-Induced Defects in a Silica-Supported Single-Walled

Nov 5, 2014 - We analyzed the defects produced at several energy levels when one ..... energy levels in the range of 32 eV to 320 keV, and we set the...
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Argon-Beam Induced Defects on SilicaSupported Single Walled Carbon Nanotube Alfredo Douglas Bobadilla, and Jorge M Seminario J. Phys. Chem. C, Just Accepted Manuscript • DOI: 10.1021/jp5098074 • Publication Date (Web): 05 Nov 2014 Downloaded from http://pubs.acs.org on November 14, 2014

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Argon-Beam Induced Defects on Silica-Supported Single Walled Carbon Nanotube Alfredo D. Bobadillaa,b and Jorge M. Seminarioa,b,c a b c

Department of Chemical Engineering

Department of Electrical and Computer Engineering

Materials Science and Engineering Graduate Program Texas A&M University College Station, Texas 77843, USA

ABSTRACT Ion beams can be used to tailor the structure and properties of carbon nanostructures. We explore, by using molecular dynamics simulations, the effects of irradiating silica-supported single walled carbon nanotube with an ion beam. We analyze defects produced at several energy levels when one argon atom collides with a single-walled carbon nanotube. At beam energies higher than 32 keV, generated defects were mainly single-vacancy defects. We find, in addition to vacancy defects, chemisorption on carbon nanotube sidewall, doping of the silica substrate, and covalent cross linking between CNT and the substrate; these types of complex defects have a maximum probability of occurrence around 100 eV and close to null probability around 100 keV.

1.

INTRODUCTION Carbon nanostructures are of high technological importance due to their unique mechanical1-3

and electrical properties,4-7 and rich physics phenomena8-13 encountered in many studies. Electron-beam14-16 and ion-beam irradiation17-20 are standard techniques to fabricate carbonbased nanodevices. Scanning probe microscopy (SPM) techniques can be used to modify carbon nanostructures but they are not adequate for mass production.21-22 Focused ion beam and e-beam lithography systems, on the other hand, can be scanned on carbon structure, orders of magnitudes

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faster than mechanical tips, producing an ion-beam spot of a few nanometers in diameter.23-24 Alternatively, a high-speed stream of glow discharge plasma allows selective etching of carbon nanostructures.25-27 On the other hand, several computational studies28-31 of these materials have been performed as they became an important complement to present experimental studies. Argon ion-irradiation can alter the electrical properties of single-walled carbon nanotubes (CNT),32 for example decreasing the electrical conductivity by generation of double vacancy defects on CNT sidewall33-34 or increasing it by generation of covalent crosslinking in carbon nanotube networks.35-36 Further studies are needed to precisely tailor the electronic structure of carbon nanotubes. The most common defects produced upon exposure to ion irradiation are vacancy defects. Magnetism has been associated with single and triple vacancy defects in single walled carbon nanotubes, this due mainly to the presence of dangling bonds in the most stable structure for these types of defect,37-38 enabling possible scenarios for spin-based electronics.39-40 Imaging, at the atomistic detail, of defects produced in carbon nanostructures by particle beam irradiation is very difficult to obtain from experiments.41-46 Irradiation-induced vacancy defects appear as bright spots in atomically resolved STM images.32,47-49

Molecular dynamics

simulations allow predicting structural configurations adopted by carbon nanostructures after exposure to a particle beam.50-54 The effect of argon ion irradiation on carbon nanotube structure has been studied mainly in the energy range from 100 eV to 3 keV.32,47-48,55-59 The effects of ion irradiation on suspended graphene have been reported for the energy range from 10 eV to 10 MeV by molecular dynamics with a mixed Ziegler-Biersack-Littmark/Brenner potential and an adaptive time step with minimum timescale of attoseconds.60-61 In the present work, we perform molecular dynamics simulations to analyze defects induced by an argon atom accelerated against a single-walled carbon nanotube supported on a silicon dioxide substrate. The argon beam energy range studied is from 32 eV to 320 keV.

2.

METHODOLOGY C-C, Si-C and Si-Si interactions are modeled with the three-body Tersoff potential,62-63 which

smoothly connects to a Ziegler-Biersack-Littmark (ZBL) repulsive potential.64 The standard universal ZBL potential fit to reasonably accurate quantum calculations of interatomic potentials

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in the repulsive region and can be easily evaluated for any atom pair.65 Ion beam bombardment with argon involves no chemical reaction since it is a noble gas; therefore, argon-carbon interactions are modeled with the ZBL potential. Electrostatic interactions between atoms due to nuclear charges are taken into account in the ZBL potential.

In previous works on ion

bombardment of carbon nanotubes, it was necessary applying a constraint to carbon nanotube edges to avoid reflection of pressure waves; in our case, the silicon dioxide substrate plays an important role on the dissipation of heat generated during ion collision.

Our choice of

interatomic potentials for CNT-SiO2 does not take into account long-range Coulombic interactions since interfacial thermal transport depends only on the vdW interaction between the CNT and substrate atoms.

2.1 The Tersoff/ZBL potential The total potential energy, E, is represented by an empirical interatomic potential Vij,

 = ∑ ∑   

(1)

where Vij includes contributions from ZBL and Tersoff potentials (Table 1),

 = 1 −    +   

(2)

The fF term is a Fermi-like function

  =





 !"  # 

(3)

which allows a smooth transition between ZBL and Tersoff potentials. Two parameters from Equation 3 allow fitting the transition between potentials; AF controls how sharp is the transition between ZBL and Tersoff potential, and rc represents a cutoff distance for the ZBL potential. We use the Tersoff/ZBL force field as implemented in the program LAMMPS (vide infra).66-68

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 =



$%&'

! " ( ) !"

!"

∅  +

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(4)

The first factor in the ZBL potential is the Coulomb repulsive term; Zi and Zj are the number of protons in each nucleus, q is the electron charge, and ε0 as the permittivity of vacuum. The second factor is the ZBL universal screening function Ø(x),

0.18182 34.5 + 0.50992 38.9$45 + 0.28022 38.$895 + 0.028172 38.8>?$+'

!'.)@ "'.)@

(6)

and ao is the Bohr radius. The Tersoff potential considers two-body (fR) and three-body (fA), repulsive and attractive, respectively, interactions,





= A  B C   + D E  F

(7)

The summations in Equation 1 are over all neighbors of atom i within a cutoff distance R + D,

1 ;  < K − L

A ,. = G − sin  

 

% 3C  P

 ; K − L <  < K + L

0 ;  > K + L

(8)

The fC term corresponds to smooth cutoff functions, which decrease from 1 to 0 in the range R - D < r < R + D.

C ,. = R2 3ST

(9)

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and

E ,. = U2 3S)

(10)

The Tersoff potential is based on the concept of bond order; the strength of a bond between two atoms is not constant, but depends on the local environment. The bond order for the bond joining atoms i and j is represented by the parameter bij,

D = 1 +

T

3 V W XW  )Y

(11)

and it is a decreasing function of a coordination parameter ζij assigned to the bond. The bond ij is weakened by the presence of other bonds ik involving atom i, and the amount of weakening is determined depending on where the other bonds are placed. The term bij, thus, accentuates or diminishes the attractive force relative to the repulsive force, according to the environment. The effective coordination number of atom i is defined by ζij; it represents the number of nearest neighbors, taking into account the relative distance of two neighbors (rij and rik) and the bondangle θ.

X = ∑Z A ,Z .[\Z 2

_

3]S@@ !" 3!^  `

(12)

Different chemical effects affecting the strength of the covalent bonding interaction, such as coordination numbers, bond angles, and conjugation effects, are all accounted for in the bij term (Equation 11). The function g(θ) has a minimum for cos(θ) = cos(θ0), and the parameter d determines how sharp the dependence on angle is, and c expresses the strength of the angular effect.

[,\. = aZ 1 +

− |c) )

b)

c

b)

,be3be' .) |



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Table 1. Tersoff/ZBL parameters for carbon, oxygen and silicon. For the Fermi-like function, AF and rC values are 14 Å-1 and 0.95 Å respectively. Argon atom interaction with silica and carbon atoms is modeled with a ZBL potential. Tersoff parameter

C

O 3

1.8308×103

A (eV)

1.3936×10

B (eV)

3.467×102

2.18787×102

4.7118×102

λ (Å-1)

3.4879

4.17108

2.4799

µ (Å )

2.2119

2.35692

1.7322

β

1.5724×10-7

1.1632×10-7

1.1×10-6

n

7.2751×10-1

1.04968

7.8734×10-1

c

3.8049×104

6.46921×104

1.0039×105

d

4.384

4.11127

1.6217×101

h

-5.7058×10-1

-8.45922×10-1

-5.9825×10-1

R (Å)

1.8

1.7

2.7

S (Å)

2.1

2.0

3.0

Zi

6

8

14

-1

1.88255×10

Si 3

The argon atom is positioned such that it will collide with carbon atoms around the 10nm length gray zone (Figure 1) of CNT; therefore, interaction between this gray carbon atoms and SiO2 is modeled with a Tersoff/ZBL potential since the argon atom causes the breaking and formation of chemical bonds when travelling through this zone. Although there is not a systematic validation of materials related force field, we found in the recent literature that the Tersoff/ZBL and related derived potentials are consistent with several experiments. For instance, Prskalo et al.69-70 found good agreement with experiment of their simulations of the sputtering of SiC and Si3N4 by argon atoms and of the sputtering of silicon and the homoepitexial growth of a Si coating on Si; on the other hand, Ziebert et al.71 found that the sputter yield of a SiC target, using the Tersoff/ZBL force field, was a function, in the low energy range, of the incident Ar atom energies. In addition Tersoff and derived potentials have been widely used to compare to other experiments, for instance, Lajevardipour et al.72 found the room temperature Young modulus of graphene of 350 Nm-1 in close proximity to the experimental value of 340 Nm-1; Lin and Fichthorn73 found that the equilibrium surface of GaAs is in agreement with experimental scanning-tunneling microscopy study; Ruthi et al.74 found the structural characterization of several III-IV zinc blende compound semiconductors in excellent agreement with that determined from experiments; Xie et al.75 found that both simulation and

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experiment results showed that the structure of the ZrxCu100-x varies from crystalline to amorphous as a function of the elemental composition; Yoon et al.76 showed that annealing temperature at which the graphene structure went into view at ~1200K is unambiguously predicted and close to the experimentally observed pit formation at 1298 K within which the graphene nucleates; Samadikhah et al.77 in a multiscale modeling of nanoindentation were able to show the effects of mechanical calibration for cases in which experimental data was available; Wang et al.78 was able to interpret macroscopic experiments indicating that boron doped diamond films showed lower friction coefficients than undoped ones although boron doped diamond films have larger grain size and rougher surface; Su et al.79 performed simulations of shock induced ejection on fused silica surface finding several mechanical properties consistent with experiments; and Mogni et al.80 found that simulations are consistent with shear stress relief provided directly by the shock-induce phase transition itself, without intermediate state of plastic deformation of the cubic diamond phase but the onset of inelastic behavior within the simulations still occurs at considerably higher stresses than found in experiments. By all means this list of references is not complete as there are dozens of papers in the field comparing experiments to calculations with Tersoff and ZBL potentials.

2.2 The Lennard-Jones potential Interactions between SiO2 and green carbon atoms (Figure 1.) are modeled with van der Waals forces using the 12-6 Lennard-Jones potential. i 

,. = 4g h  

i
m (5 as) is chosen to

simulate the ion-CNT collision. During collision time, creation and breaking of bonds occur.

After the collision time, the nanotube experience mainly bending-mode vibrations, and to speed up the simulation the time-step is changed to 5 l 103n m (50 as), performing a 20 ps simulation time with this time-step. For the energy range from 3.2 keV to 320 keV, a time-step of 1l

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103> m (1 as) is chosen to simulate the ion-CNT collision. After collision, the time-step is

changed to 1l 103n m (10 as).

Figure 3. Sidewall of single walled carbon nanotube. Gray circles represent chosen points where an argon atom collides with CNT wall. An argon atom travels along X direction. Initial position of Argon atom is 20 Å from CNT sidewall. For every chosen collision point, ten energy levels are tested, 32 eV, 100 eV, 214 eV, 457 eV, 1 keV, 3.2 keV, 10 keV, 32 keV, 100 keV and 320 keV.

2.5 Defects probability We perform 430 simulations for each value of incident energy. We obtain the rates of

interactions by using probability theory as follows. The variable o

,p.

becomes 1 if the result of

the i-th simulation is a defect of type ′r′ and 0 if the outcome of the i-th simulation is different: o

,p.

=s

1, if defect is of type r 0, if not type r

(15)

The mean of the results from the first simulation to the nth simulation is defined by  ,p. ,p. o}W ≡ W ∑W o

(16)

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,p. By the central limit theorem, when € → ∞, o}∞ coincides with the defect probability. Number

of simulations is finite, but our n value is big enough so as to make the variance small enough. we define the average number of defects under occurrence as ∑ ƒ! „! ,p. ‚}W ≡ !†T ,…. Y Y

∑!†T „!

,….

(17)

Where ‡ is the number of defects in the i-th simulation. 3.

RESULTS AND DISCUSSION A summary of types of defects and probability of defects produced after argon atom collision

on CNT wall is shown in Figures 4 and 5 respectively, with energy values in logarithmic scale. The probability of defect production on CNT wall (Figure 5) increases quickly as energy approach 100 eV, and is maximum and near to constant (about 0.8) in the range from 214 eV to 3.2 keV, then it decreases almost linearly to 0.42 in the range from 3.2 keV to 100 keV. The probability of defect production on CNT wall is low at very high energies, with 0.23 probabilities at 320 keV (Figure 5).

Figure 4. Several types of defects generated on carbon nanotube wall and silicon dioxide substrate after argon atom collision: single vacancy (purple circle), kink (complex) defect (black circle), carbon chemisorption and doping on SiO2 substrate (orange circle).

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Defect ( probability )

1

0.75

0.5

0.25

0 10

100

1000 10000 Energy ( eV )

100000

Figure 5. Probability of defect occurrence on CNT wall after argon atom collision at different argon beam energy levels. Energy range is in logarithmic scale.

3.1 Silica substrate doping The probability of sputtered carbons shows a similar characteristic to the SiO2 doping (Figure 6a) curve. The probabilities of sputtered C and SiO2 doping are both maximum in the range between 214 eV and 3.2 keV. The higher number of sputtered carbons is around 3.2 keV and the higher number of substrate doping is around 214 eV (Figure 6b). 0.8

0.6 probability

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0.4

0.2

0 10

100

1000 10000 Energy ( eV ) Sputtered C

SiO2 doping

(a)

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2.5 2 average number

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1.5 1 0.5 0 10

100

1000 10000 Energy ( eV ) Sputtered C

100000

SiO2 doping

(b) Figure 6. CNT damage by sputtering and silica substrate damage by doping at different argon beam energy levels. (a) Probability of carbon atom sputtered from CNT and probability of SiO2 doping with carbon atom, (b) average number of carbon atoms sputtered from CNT and average number of carbon atoms doping SiO2.

Probability of vacancy defect is maximum (0.74) at 457 eV and decreases at a slow rate until it reaches 0.67 value at 10 keV. Then, in the range from 10 keV to 320 keV it decreases linearly but at a higher rate reaching 0.19 at 320 keV. The probability of complex defect has a maximum value of 0.28 at 214 eV and is lower than 0.1 for beam energy higher than 10 keV. Most complex defects are due to interstitial defects or Stone-Wales defects. Kink defects (Figure 4) are produced only in the range from 214 eV to 1 keV with probability lower than 0.1. The probability of vacancy defect is higher than 0.65 in the range from 100 eV to 10 keV and the probability of chemisorption has a maximum of 0.58 at 100 eV.

3.2 CNT vacancy defects Probability for Vacancy defect is always higher than those for chemisorption and complex defects (Figure 7); this is consistent with the fact that most sputtered carbon atoms end up doping the silica substrate, according to the trend in Figure 6a.

And for very high energies, the

probability for chemisorption and complex defect is low (probability of less than 0.1 at 320 keV).

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1 Defect ( probability )

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0.75

0.5

0.25

0 10

100

Chemisorption

1000 10000 Energy ( eV ) Vacancy

100000

Complex defect

Figure 7. Types of defects generated on CNT sidewall after argon atom collision at different argon beam energy levels. Probability of silicon, carbon or oxygen chemisorption on CNT wall (red). Probability of CNT vacancy defect (gray), vacancy defect can be single, double or multiple. And probability of complex defect on CNT wall (blue).

The probability of single vacancy defect is almost constant in the range from 100 eV to 3.2 keV, with a value of about 0.6, and then it decreases to 0.16 at 320 keV (Figure 8a). The average number of single vacancies has a peak value of 1.7 at 1 keV (Figure 8b). The probability of double vacancy is always low with a maximum value of 0.33 at 457 eV (Figure 8a) and there is no double vacancy defect at very low energy (32 eV) and very high energy (320 keV). The average number of double vacancies is about 1 in the range from 100 eV to 32 keV, and show a peak value of 1.5 at 100 keV while the average number of single vacancies show a peak value of 1.7 at 1 keV (Figure 8b).

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1 Vacancy ( probability )

0.75

0.5

0.25

0 10

100

1000 10000 Energy ( eV ) Total

SV

100000

DV

(a)

2 Vacancy ( number )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1.5

1

0.5

0 10

100

1000 10000 Energy ( eV ) SV

100000

DV

(b) Figure 8. (a) Probability of single and double vacancy at different argon beam energy levels. (b) Average number of single and double vacancies.

3.3 CNT chemisorption defects The probability of chemisorption defects on the CNT wall (Figure 9) reaches a maximum at about 100 eV and decreases almost linearly in the energy range from 457 eV to 32 keV.

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Chemisorption can occur on the internal or external sides of the CNT wall. The former is always due to a carbon atom while the latter can be due to oxygen, silicon or carbon atoms. Probability of chemisorption on internal side of CNT wall is always very low, maximum value being 0.14 at 1 keV (Figure 9a).

The probability for C chemisorption is always near to the one for O chemisorption in the range from 32 eV to 320 keV (Figure 9b). The probability for C chemisorption shows two peak values, 0.39 at 100 eV and 0.33 at 457 eV, while the probability for O chemisorption has a peak value of 0.28 at 214 eV. For beam energy higher than 1 keV both probabilities decrease, approaching to ~ 0.1 at 3.2 keV and near to zero at 320 keV. The average number of carbon atoms chemisorbed on CNT wall has a minimum value of 0.6 at 10 keV. And the average number of oxygen atoms chemisorbed on CNT wall has a local minimum around 10 keV as well (Figure 9c).

1 Chemisorption ( probability )

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0.75

0.5

0.25

0 10

100

Total

1000 10000 Energy ( eV ) Internal

External

(a)

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1 Chemisorption (probability )

0.75

0.5

0.25

0 10

100

1000 10000 Energy ( eV )

Carbon

Oxygen

100000

Total

(b)

2 Chemisorption ( number )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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1.5

1

0.5

0 10

100

1000 10000 Energy ( eV ) Carbon

100000

Oxygen

(c) Figure 9. (a) Probability of chemisorption on CNT wall, and probability of chemisorption on internal side of CNT wall or external side of CNT wall. (b) Probability of carbon and oxygen chemisorption. (c) Average number of carbon and oxygen atoms chemisorbed on CNT wall at different argon beam energy levels.

3.4 CNT-subtrate crosslinking The probability for CNT-subtrate crosslinking (Figure 10) shows a peak value of 0.53 at beam energy of 100 eV and decreases steadily for higher energy levels (Figure 11a). The average

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number of crosslinkings is maximum at 100 eV and 320 keV with values of 1.43 and 1.5 respectively (Figure 11b).

Figure 10. Crosslink between CNT and SiO2 substrate by an oxygen atom (cyan, highlighted in yellow).

SiO2-CNT crosslinking ( probability )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

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0.75

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0 10

100

1000 10000 Energy ( eV )

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2 SiO2-CNT crosslinking ( number )

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1.5

1

0.5

0 10

100

1000 10000 Energy ( eV )

100000

(b) Figure 11. (a) CNT-SiO2 crosslinking probability and (b) average number of links at different argon beam energy levels.

3.5 Energy transferred to CNT after Argon collision We analyze the energy transferred to CNT after the argon particle beam collides on CNT sidewall at different points labeled from 0 to 42 (Figure 12a) within an hexagonal ring where carbon atom positions correspond to the corners of the hexagon. We observe a tendency for low average number of defects and low probability of defects at high beam energies, either on the carbon nanotube or in the silica substrate. We correlate it with a general tendency for decrease on kinetic energy transferred to carbon nanotube when beam energy is higher than 3.2 keV (Figure 12b,c). At high beam energy, larger than 3.2 keV, a high energy transfer and damage occurs mainly when the ion beam atom collides directly with a carbon nanotube atom which is a low probability event (Figure 12c).

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(a) 1000

Energy (eV)

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100

10

1 0

10

32eV

100eV

20 collision point 214eV

30

456eV

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100000 10000 Energy (eV)

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1000 100 10 1 0

10

3.2keV

10keV

20 collision point 32keV

30

40

100keV

320keV

(c) Figure 12. (a) Ion beam (X,Y) collision points in a benzene ring on CNT sidewall, numbered from 0 to 42; (b,c) Kinetic energy transferred to CNT after ion beam collision. Argon atom travels initially along Z direction and energy transferred to CNT depends on the (X,Y) point of collision on CNT sidewall. Horizontal axis in (b,c) represents the point of collision as labeled in (a). Energy level of Argon atom is color coded (b) from 32 eV to 1 keV and (c) from 3.2 keV to 320 keV.

4.

CONCLUSIONS Molecular dynamics simulations were performed on bombardment of single-walled carbon

nanotube by argon atoms in the range of 32 eV-320 keV. The probability and average number of irradiation induced defects was quantified at different beam energy levels.

We found

crosslinking of silica substrate with the carbon nanotube and doping of the silicon dioxide substrate with higher probabilities around 100 eV and 214 eV, respectively. Oxygen and carbon chemisorption on the nanotube sidewall are events with similar probabilities of occurrence in the whole range of energy analyzed and with probability lower than 0.2 for beam energy higher than 3.2 keV. The possibility of defects on the substrate, CNT sidewall and at the interface has to be considered in the performance analysis of carbon nanotubes engineered by ion beam irradiation. We suggest 10 keV as adequate level of ion beam energy to etch carbon nanotube structure with low level of defects by chemisorption, complex defects, CNT-substrate crosslinking or substrate doping. Besides, above 32 keV, the main type of defect is almost exclusively single vacancy

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defects, therefore argon beam irradiation can allow selective introduction of magnetic impurities, which can be exploited for the development of magnetic devices.

ACKNOWLEDGEMENTS This research was supported by the U.S. Defense Threat Reduction Agency (DTRA) through the U. S. Army Research Office (ARO) under grant number W911NF-06-1-0231, the ARO under a DURIP project number # W91NF-07-1-0199 and under a MURI Project # W911NF-111-0024. We also would like to acknowledge high-performance computing support provided by the Texas A&M Supercomputer Facility and the Texas Advanced Computing Center (TACC).

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