Vectorial Crystal Growth of Oriented Vertically Aligned Carbon

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Vectorial Crystal Growth of Oriented Vertically Aligned Carbon Nanotubes using statistical analysis Amin T. Yousefi, Hirofumi Tanaka, samira Bagheri, Fawzi Elfghi, Mohamad R. Mahmood, and Shoichiro Ikeda Cryst. Growth Des., Just Accepted Manuscript • DOI: 10.1021/acs.cgd.5b00534 • Publication Date (Web): 29 May 2015 Downloaded from http://pubs.acs.org on June 2, 2015

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Crystal Growth & Design

Vectorial crystal growth of oriented vertically aligned carbon nanotubes using statistical analysis Amin T.Yousefi,1,* Hirofumi Tanaka,2 Samira Bagheri,3 Fawzi Elfghi,1 Mohammad R. Mahmood,4 Shoichiro Ikeda1 1

ChECA IKohza, Dept. Environmental & Green Technology (EGT), Malaysia Japan

International Institute of Technology (MJIIT), University Technology Malaysia (UTM), Kuala Lumpur, Malaysia. 2

Department of Human Intelligence Systems, Graduate School of Life Science and Systems Engineering, Kyushu Institute of Technology (Kyutech), Kitakyushu 808-0196, Japan.

3

Nanotechnology & Catalysis Research Centre (NANOCAT), IPS Building, University Malaya, 50603 Kuala Lumpur, Malaysia.

4

NANO-SciTech Centre, Institute of Science, Universiti Teknologi MARA (UiTM), Shah Alam, Selangor, Malaysia.

ABSTRACT: In this present work, crystalline growth condition of oriented carbon nanotubes based on chemical vapor deposition (CVD) was optimized. The crystallinity and degree of alignment of the grown carbon nanotubes (CNTs) were characterized by field emission scanning

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electron microscopy, transmission electron microscopy, and Raman spectroscopy. The effects of four variables, namely deposition time, deposition temperature, annealing process, and concentration of the precursor on the crystallinity of the CNTs were explored. Furthermore, the correlation of parameters with the growth mechanism was examined using Response Surface Methodology in an attempt to determine the complex interactions between the variables. A total of 30 runs, including predicting and consolidation runs to confirm the results, were required for screening the effect of the parameters on the growth of the CNTs. Based on the investigated model, it was found that the crystallinity of the CNTs grown by the CVD method can be controlled via restriction of the effective parameters.

Introduction An experimental program to quantify the correlation between the parameters affecting the growth mechanism and the synthetic conditions of nanostructures is important in terms of their scientific and technical applications.1 Carbon nanotubes (CNTs) have been considered for many different technological applications because of their unique electrical and mechanical properties.2 Recently, the interest of growing CNTs with high crystallinity and low defects in a controlled orientation is increasing due to its defect free nature. However, optimizing the performance has often been more challenging than constructing the system.3 Homing a set of techniques to find the optimum condition of the growth process beyond the available surface resources of experiments is the new strategy being employed to gain a better understanding of the synthesizing condition of low-defect CNTs.

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Two major strong points of these statistical

strategies5 are (i) the amount of necessary experiments being decreased using a few carefully selected considerable points for each parameter.6, 7 Thus, the smallest number of experiments can


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achieve the complete amount of information on real systems, and (ii) the new method allows for more insight into the physical behavior of the system by adequately choosing a range for each parameter.8, 9 Chemical vapor deposition (CVD) is one of the most promising methods used to strategically optimize CNTs by offering a versatile control and the possibility of scaling up.10 It has been shown that the CVD method is amenable for CNTs’ growth on different patterned surfaces that are appropriate for various bio transducer applications, such as electrochemical, optical, and piezoelectric biosensors.11 Defects and disorders of CNTs are most significant, as they can dominate the physical property of measurements .Numerous studies have reported the use of correlation between the quantity and the defects of synthesized aligned CNTs in the CVD growth process.12 The flow rate and flow type, deposition and annealing process, catalyst, and precursor are some of the important variables that were optimized to synthesize low-defect CNTs.13 Previous reports on the optimization of the growth conditions suggest a unified theory on the relationship between crystallinity and growth temperature.14, 15 In most cases, the behavior of the measured response is governed by a specific factor in the experiments. However, to the best of our knowledge, this study is the first statistical strategy intended to optimize CVD based on the deterministic relationship between the set of relevant factors affecting the crystallinity of CNTs. Research reports on the utilization of response surface methodology (RSM) provided a model16 to synthesize low-defect CVD carbon nanotubes by designing a set of experiments to understand the overall response of each effective factor. Regression analyses, coupled with Analysis of Variance (ANOVA) table, were used to construct a mathematical model to obtain the correlation between crystallinity and other influential factors. However, to keep our discussion compact, four effective explanatory variables on the crystallinity of CNTs during the CVD process was


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studied and denoted by the deposition temperature, deposition time, annealing temperature, and the concentration of the precursor. The results not only provide a theoretical basis for synthesizing highly crystalline CNTs via the RSM method, but also establish a foundation for large-scale production and the systematic utilization of CNT-based electronic devices.

Design of Experimental Matrix A complete description of any process of chemical reaction behavior requires either a quadratic or higher order polynomial model.17 Hence, the full quadratic models were established by using the method of least squares, which includes all linear and interaction terms, to calculate the predicted response.18 The quadratic model is usually sufficient for industrial applications. For n-factors, the full quadratic model is shown in Eq. (1).

Y = bo + ∑ biXi + ∑ bijXiXj (i,j = 1,2,3,…..,k),

(1)

where Y is the predicted response or dependent variable, Xi and Xj are the independent variables, and bi and bj are constants.19 In this case, the number of independent factors is four, and therefore, k = 4 and Eq. (1) becomes Eq. (2):

Yu = β0+ β1X1+ β2X2 + β3X3 + β4X4 + β12X1X2 + β13X1X3 + β14X1X4 + β24X2X4 + β34X3X4 + β11X12 + β22X22 + β33X32 + β44X42 (2)

with Y being the predicted response, and X1, X2, X3, and X4 are the coded form of the input variables for deposition time, vaporization time, deposition temperature, and concentration of precursor, respectively. The term β0 is the intercept term; β1, β2, β3, and β4 are the linear terms;


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β11, β22, β33, and β44 are the squared terms; β12, β13, β23, β14, β24, and β34 are the interaction terms between the four variables. The deposition temperature, deposition time, annealing temperature, and concentration of the precursor were selected as the influencing factors. The selections of these variables with their defined experimental ranges were carefully done based on prescreening tests before start the optimization that are often described in the literature.20, 21 The lowest and highest levels of variables were coded as (-1) and (+1), respectively, and are given in Table 1, including axial star points of (- α and + α), where α is the distance of the axial points from the center, making the design rotatable. Table 1. Independent variables and their coded and actual values

Coded levels Independent variable

Symbol -α

-1

0.00

+1

+α

Deposition Temperature (oC)

X1

700

750

825

900

950

Vaporization time (min)

X2

10

15

45

60

80

Annealing time (min)

X3

5

15

30

45

55

Concentration of precursor (mL)

X4

2

5

15

25

28

where: −/+α, star point value; (−1), low value; (+1), high value; (0.00), center value. In this study, the value of α was calculated using Eq. (3), and was fixed at 0.5 (rotatable).

α = (F) ¼

(3)

where F is the number of points in the cube section of the design (F = 2k), and k is the number of factors. As we have four factors α equal to 2. Therefore, the total number of experimental


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combinations should be conducted based on the similar concept of central composite rotatable design5 by applying Eq. (4)

Total number of experiments =2k+2k + no

(4)

where k is the number of independent variables, and no is the number of experiments repeated at the center point22. In our case, no = 6 and k = 4, making the total number of runs 30. Thus, a matrix of 30 experiments with four factors was generated using the software package Design of Expert version 6.0.6. The six center points were used to determine the experimental error and the reproducibility of the data. Table 2 tabulates the complete design matrix of the experiments performed, together with the results obtained. The responses were used to develop an empirical model for the crystallinity of the CNTs.

Table 2. Central Composite Design Matrix and experimental results

X1

X2

X3

X4

Y

RUN

Deposition temperature

Vaporization time

Annealing time

Concentration of precursor

IG/ID

1

750

60

45

5

1.09

2

900

60

15

5

1.096

3

750

15

45

5

1.133

4

900

15

15

5

1.15

5

900

15

45

5

1.115

6

750

15

15

5

1.05


6

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7

825

37.5

30

10

1.25

8

825

26.25

30

15

1.19

9

825

37.5

22.5

15

1.22

10

825

37.5

30

15

1.21

11

825

37.5

30

15

1.21

12

862.5

37.5

30

15

1.2

13

825

37.5

30

15

1.2

14

825

37.5

37.5

15

1.185

15

825

48.75

30

15

1.185

16

787.5

37.5

30

15

1.155

17

825

37.5

30

15

1.21

18

825

37.5

30

15

1.21

19

825

37.5

30

15

1.16

20

825

37.5

30

20

1.185

21

900

15

45

25

1.065

22

750

15

45

25

1.015

23

750

15

15

25

1.152

24

750

60

15

25

1.03

25

750

60

45

25

1.043

26

900

60

15

25

1.085

27

900

15

15

25

1.125

28

900

60

45

25

1.105

29

750

60

45

5

1.09

30

900

60

15

5

1.096

After implementing the experimental design, the experimental data were interpreted and analyzed using ANOVA at a 5% level of significance using the Fisher F-test. The F-test is a


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simple arithmetical method which sorts the components of variation in a given set of data and provides the test for significance.23

Experimental Methodology The total number of required experiments to screen the effect of the parameters on carbon nanotubes was 30, of which 24 were prediction runs, and 6 were consolidation runs to confirm the results. Typical experiments were performed in a horizontal quartz tube with an outer diameter of 35 mm using a two-stage furnace. In each run, 0.06 g of ferrocene was used to produce Fe catalyst particles for seeding nanotube growth in the presence of camphor oil as a precursor. The explanatory variables were adjusted to refine the growth process of vertically aligned carbon nanotubes (VACNTs). The flow rate was adjusted to 500 standard cubic centimeters per minute (sccm) to pump the feedstock to the second furnace. As indicated in Table 2, the deposition temperature of the growth process was performed in the range 750−900 ºC, and was restricted between 15 min and 1 h. The concentration of the camphor oil as a precursor was confined within a range of 5-25 mL. The annealing process was also done within 15−45 min, and subsequently, the carrier gas was changed to oxygen during the post-annealing process. Then, the CVD was cooled down for 5 min, followed by exposure to oxygen gas.14 The CVD process was completed with the removal of the grown CNTs from the quartz tube at 400 °C to neutralize the intrinsic disorders constructed by the mobile surface contaminants on the structure of carbon nanotubes. The synthesized CNTs were characterized by FESEM (ZEISS Supra 40VP) operated at 5 kV to evaluate the structure and the aspect ratio of the sample. The Raman spectra were obtained using a micro-Raman spectroscope (Horiba Jobin Yvon-DU420AOE-325) with Ar+ ions (wavelength 514.5 nm) to determine the adsorption, desorption, and


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surface area of the samples. TEM (Hitachi H-9500) equipped with electron diffraction analysis, was used for the chemical characterization of the specimens, as well as surface imaging.

Results and Discussion Crystallinity Model (IG/ID-single-response optimization) Investigations of the best variant of concentration of camphor oil, annealing process, deposition temperature, and deposition time in the growth conditions of CNTs were studied for the purpose of increasing the crystallinity of the CNTs during the CVD process. An empirical relationship, represented as a mathematical model between the crystallinity of the CNTs and the test variables in the coded unit is shown in Eq. (2). Indeed, the empirical model developed in Eq. (5) by applying the multiple regression technique, fitted the experimental results, indicating that the crystallinity model of the CNTs was agreed with the experimental results. YIG/ID = 1.20160602 + 0.014515152 X1 - 0.00530303 X2 - 0.009121212 X3 - 0.019242424 0.101563342 X12 -0.061563342 X22 - 0.001563342 X32 + 0.058436658 X42 + 0.0031875 X1X3 + (5) 0.0039375 X1X4 + 0.0103125 X2X3 - 0.0064375 X2X4 - 0.0121875 X3X4

where YIG/ID is the predicted value for the crystallinity of the CNTs during the CVD process.

A reasonable value of the determination coefficient R2 = 0.8442 estimated the regression coefficients, which provides an indication of an acceptable agreement between the observed and predicted data.24 It is worth mentioning that the determination coefficient R2 is fairly lower than that of the crystallinity model. Thus, the crystallinity model exhibits a better fit than the aromatization activity model. The adequacy of the model was tested with the analysis of


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variance, as shown in Table 3, where the computed F-value of 6.67 exceeded the tabulated Fvalue.

Table 3. ANOVA results table for CNTs Crystallinity

Sources

Sum of squares

Degree of freedom

Mean squares

S.S. Regression

0.098

13

7.548 × 10-3

S.S. Error

0.018

16

1.131 × 10-3

S.S. Total

0.12

29

F value 6.67

Table 4 shows the multiple regression results and significance of each regression coefficient of the crystallinity growth model. The terms of the model were arranged based on the t- and pvalues, signifying the variable effects on the crystallinity of CNTs model. According to lowest pvalues (less than 0.5) and the highest student t-test values, the deposition temperature and concentration of the camphor oil (X1 and X4) are the most influential parameters on the crystallinity growth of CNTs. The quadratic term of deposition temperature, annealing time, and the concentration of the precursor (X12, X32, and X42) also influenced the crystallinity of the CNTs, as well as the interaction term of deposition time with annealing time and the concentration of the precursor (X2 with X3, X4). Table 4. Multiple regression results and sorted significance effect of regression coefficient for crystallinity of CNTs


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Parameter Term

Coefficient

t-value

p-value

β1

X1

0.015

3.62

0.0988

β2

X2

-5.3 × 10-3

-1.28

5.31 × 10-1

β3

X3

-9.12 × 10-3

-2.20

2.87 × 10-1

β4

X4

-0.019

-4.59

0.0336

β13

X1 X3

3.19 × 10-3

7.58 × 10-1

0.7096

β14

X1X4

3.94 × 10-3

9.37 × 10-1

0.6459

β23

X2 X 3

1.00 × 10-2

2.38

0.2378

β24

X2X4

-6.44 × 10-3

-1.53

0.4551

β34

X3X4

-1.20 × 10-2

-2.85

0.1666

β11

X 12

-1.00 × 10-1

-2.44

0.235

β22

X 22

-6.20 × 10-2

1.51

0.4647

β33

X3

2

-1.76 × 10-3

-4.30 × 10-2

0.9832

X4

2

5.80 × 10-2

1.41

0.4901

β44

The graphical representations of three-dimensional (3D) response surface and two-dimensional (2D) contour plots were employed to present the crystallinity of CNTs, which could accomplish a better understanding of the relationship between the responses and each effective variable. Figures 1(a) and 1(b) present the 3D-plot and 2D-plot of CNTs crystallinity for the deposition temperature and annealing time at fixed deposition time and concentration of the precursor, respectively. The maximum predicted crystallinity is indicated by the surface confined in the smallest circle in the contour diagram. The figure revealed a ring of complete round profile, implying an interaction effect between both factors on the response (IG/ID). It is clearly


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highlighted that the crystallinity of CNTs reached near to the maximum at a combination of coded level with 800-850 °C, and 30-37.5 min deposition time. Figure 1(c) and Figure 1(d) show the 3-D plot and 2-D plot of the CNTs crystallinity, with the deposition temperature and the concentration of precursor at a zero level of the other variables (fixed annealing and deposition time), respectively. As can be seen, the maximum predicted crystallinity is indicated by the surface confined to the smallest circle in the contour diagram. It is clear that the crystallinity growth reached its maximum at a combination of the coded level of 800−865 °C of the deposition temperature, and 12−18 mL of the precursor. The study was also developed to determine the interaction of annealing and vaporization times. Figures 1(e) and 1(f) show the 3-D plot and 2-D plot of the crystallinity of CNT with the annealing and vaporization time at a zero level of the other variables (fixed concentration of the precursor and deposition temperature), respectively. The maximum predicted crystallinity was indicated by the surface confined to the smallest circle in the contour diagram. It is clear that crystallinity growth approached the maximum at a combination of the coded level 30−37.5 min of vaporization time, and 30 min of annealing time. The model predicted an optimum point for maximum isomerization activity for a vaporization time of 35 min, and an annealing time of 30 min.


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Figure 1. 3-D and 2- D Response surface plot for the design: crystallinity of CNTs as function of independent variables.

Verification of Effects on the Crystallinity Model of CNTs: Morphological and Interfacial Characterization The model was analyzed using statistical strategies to develop an experimental analysis of synthesized CNTs. The optimal performance of the growth CNTs was confirmed by Raman spectroscopy and FESEM, and the effect of each parameter is discussed. Figure 2(a) shows the Raman spectroscopy results of the grown CNTs at different deposition temperatures. All spectra showed two mainly Raman peaks at ~1300−1350 cm-1 (D band) and ~1580−600 cm-1 (G band), and also a second order Raman signal around 2701 cm-1 (G*-band), which corresponds to the overtone mode of the D-band in graphite and CNTs.25 It was shown that by decreasing the growth temperature, the D bands become stronger and broader,26 which means that a low deposition temperature leads to overlapping D and G peaks, resulting in low crystalline CNTs in the form of D being explained as a disorder feature of graphitic sheets27.


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Figure 2. Raman spectra (a) and FESEM (b) of CNTs grown by varying the deposition temperature at 650, 800, and 900 °C. (c), (d) Raman spectra of CNTs grown by varying the concentration of camphor oil and annealing time, respectively. In other words, the values of ID and IG, which are related to the crystallite dimension of CNTs, are directly related to the temperature and the growth of CNTs at higher temperatures, and were shown to lead to a higher intensity ratio of the D and G bands. Furthermore, as shown in Figure 2(b), the FESEM images corresponding to the same experiments indicated that the average diameter of CNTs also increased by enhancing the higher deposition temperature, which leads to the experimental growth of more crystalline CNTs with higher average diameters28. This phenomenon suggests that the catalyst activity in lower temperatures is not enough to support the


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interaction in the CVD process, and the precursor failed to decompose at low temperatures, led to CNTs with lower degrees of crystallinity. According to the growth mechanism of CNTs, carbon atoms diffuse into the iron nanoparticles during the CVD process to form FeC. Therefore, the crystallinity of the CNTs was also significantly affected by the different concentrations of precursor used to grow the CNTs,

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as

shown in Figure 2(c). Using a low amount of catalyst ratio to precursor yields fewer carbon nanotubes with lower crystallinity, resulting in the CNTs growing in random directions. Achieving carbonaceous crusts in small amounts that are not growth-oriented due to the incomplete evaporation process of ferrocene and the precursor during synthesis is also possible. Figure 2(d) shows the Raman spectra of CNTs obtained at different annealing temperatures. As expected, higher annealing temperatures improve the graphitic structure of the CNTs. Improving the IG/ID ratio may not affect the structure of the CNTs, because such effects could only be observed at significantly higher temperatures (>1500 °C), where carbon could alter its crystallinity, but certain defects are initially present in the nanotubes, which are then removed via annealing.14 The TEM results could also be a very useful tool to determine the degree of crystallinity of the grown CNTs, as well as the presence of amorphous carbon coating the outer layers of the tubes. As shown in Figure 3(a), the synthesized CNTs using conversational CVD method displays more diffuse sposts associated with amorphous carbon, which confirms a lower degree of crystallinity than that shown in Figure 3(b), which corresponds to the optimized conditions of predominant factors affecting the CVD process. Furthermore, the TEM results reveal that most of the tubes are closed containing a hollow inside that confirms the tubular structure of the grown CNTs. The TEM image also confirms the complete removal of catalyst particles.


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Figure 3. TEM images of multi walled carbon nanotubes corresponding to the non-optimized (a) and optimized (b) conditions of the predominant factors affecting the CVD process.

Conclusion Sequential experimental strategies based on the central composite design, coupled with RSM, were implemented by varying the four predominant factors affecting the crystallinity of multi walled carbon nanotubes. The highest interaction on the crystallinity of CNTs was found between the deposition temperature and the concentration of camphor oil during the CVD processes, which are influential parameters on the crystallinity growth of CNTs. According to lowest p-values and highest student t-test values, and the quadratic term of the deposition temperature, annealing time, and the concentration of the precursor also showed significant influence on the crystallinity of the CNTs, as well as the interaction term of the deposition time


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and the concentration of the precursor. The counter plat results were confirmed by Raman spectroscopy, indicating that the crystallinity of CNTs was regularly increasing with increasing deposition temperature in the first furnace, and the annealing time in the second furnace, at a fixed chamber pressure when the flow rates of Ar was 500 sccm. The crystallinity of the CNTs also varied from low to high crystallinity via the simultaneous increase in the precursor of up to 15 mL at an optimized level of the other variables.

Corresponding Author Amin TermehYousefi ChECA IKohza, Dept. Environmental & Green Technology (EGT), Malaysia Japan International Institute of Technology (MJIIT), University Technology Malaysia (UTM), Kuala Lumpur, Malaysia E-mail: [email protected]

Author Contributions The manuscript was written through contributions of all authors. All authors have given approval to the final version of the manuscript. ‡These authors contributed equally.

ACKNOWLEDGMENT This work was supported by Grants-in-Aid for Scientific Research (No. 24510150) and for Scientific Research on Innovative Areas (No. 25110002) from the Ministry of Education, Culture, Science, Sports, and Technology (MEXT) of Japan.


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ABBREVIATIONS CNTs, carbon nanotubes; CVD, chemical vapor deposition; FESEM, field emission scanning electron microscopy; TEM, transmission electron microscopy; RSM, response surface methodology; VACNTs, vertically aligned carbon nanotubes; sccm, standard cubic centimeters per minute.

REFERENCES

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Vectorial crystal growth of oriented vertically aligned carbon nanotubes using statistical analysis

Amin T. Yousefi, Hirofumi Tanaka, Samira Bagheri, Fawzi Elfghi, Mohammad R. Mahmood, and Shoichiro Ikeda

Chemical vapor deposition is one of the most promising methods used to synthesis carbon nanotubes. Response surface methodology is the strategy being employed to optimize the synthesizing condition of low-defect CNTs. The investigated model suggests optimization of CVD on the deterministic relationship between the set of the involved factors on the crystallinity of CNTs.


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Vectorial Crystal Growth of Oriented Vertically Aligned Carbon

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