ARTICLE pubs.acs.org/JPCC
Optimization of the Catalytic Chemical Vapor Deposition Synthesis of Multiwall Carbon Nanotubes on FeCo(Ni)/SiO2 Aerogel Catalysts by Statistical Design of Experiments Laszlo Vanyorek,† Danilo Loche,‡ Hajnalka Katona,† Maria Francesca Casula,‡ Anna Corrias,*,‡ kos Kukovecz,† and Imre Kiricsi† Zoltan Konya,† A † ‡
Department of Applied and Environmental Chemistry, University of Szeged, Rerrich Bela ter 1, H-6720 Szeged, Hungary Dipartimento di Scienze Chimiche and INSTM, Universita di Cagliari, I-09042 Monserrato (Cagliari), Italy
bS Supporting Information ABSTRACT: We report on optimizing the catalytic chemical vapor deposition synthesis of multiwall carbon nanotubes (MWCNTs) from ethene over supported transition metal SiO2 nanocomposite aerogels using the statistical design of experiments (DOE) approach. DOE allowed us to test 19 different catalysts in a total of 49 reactions instead of testing 27 catalysts in 729 runs as required by a three-level full factorial design. Both catalyst-related and process-related variables were optimized; in particular varied parameters were Fe þ Co loading, Fe/Co ratio, Ni loading, C2H4 flow rate, temperature, and duration of the reaction. The results of the optimization indicate that a good catalyst should contain a high overall loading (10 wt %) of iron and cobalt in similar amount, should be free of nickel and should be operated at a relatively low temperature (650-700 °C) at high carbon source space velocity for optimum performance. The uniqueness of this work is that we demonstrated that catalyst-related and process-related variables can be optimized simultaneously in the DOE of MWCNT synthesis.
’ INTRODUCTION Catalytic chemical vapor deposition (CCVD), which involves the catalytic decomposition of hydrocarbon or alcohol gas on supported metal nanoparticles, is widely acknowledged as the most effective approach for the large-scale production of multiwalled carbon nanotubes (MWCNT).1-3 In order to decrease production costs, high yields accompanied by a good selectivity (types of carbonaceous deposit, number of walls, etc.) must be obtained in the CCVD process. Both yield and selectivity depend strongly on using a catalyst with suitable features (composition, microstructure, porosity) as well as on the carbon deposition conditions (carbonaceous source, gas flow, deposition temperature, and time). Due to the high number of the variables, despite the extensive research that has been carried out so far, it is not easy to optimize the CNT production. A statistical design of experiment (DOE) approach offers enormous advantages in the optimization of a catalytic problem, since in this approach, the number of experiments required to investigate all of the variables is largely reduced compared to conventional optimization methods. The DOE approach has already been proposed for the optimization of the production of nanotubes;4 however, so far only process parameters were optimized without paying attention to the optimization of the catalyst, which plays a key role in the success of the r 2011 American Chemical Society
CNT production, affecting the yield; selectivity (types of carbonaceous deposit, number of walls etc), ordering, length, and diameter of the CNTs.5 However, DOE has been used to optimize the synthetic parameters of aerogel materials with improved textural features.6 In this work, we provide the first example of MWCNT growth optimization using DOE when the aerogel catalyst composition and the process parameters are both simultaneously optimized. In order to be able to exploit a DOE approach to optimize the catalyst characteristics, a very versatile and well-controlled preparation route should be available. We have recently developed a versatile sol-gel preparation route leading to highly porous nanocomposite aerogels.7 As opposite to nanocomposite catalysts obtained by impregnation or ion-exchange procedures on a preformed porous sol-gel matrix, we make use of cohydrolysis and cogelation of the dispersed phase and of the matrix: as a result, the dispersed phase is homogeneously distributed within the porous matrix. In addition, the composition of the nanocomposite (i.e., the dispersed phase loading) can be finely adjusted and the post-treatment conditions can be used to carefully tune the local surroundings of the transition metal components.8 Received: December 14, 2010 Revised: February 11, 2011 Published: March 10, 2011 5894
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Table 1. Summary of All 49 Runs Performed during the Optimization of the System Using a Box-Behnken Designa
a
Fe þ Co weight%
Fe/Co ratio
Ni weight %
TCVD (°C)
C2H4 flow (cm3 3 min-1)
tCVD (min)
1
1.0
0.1
0.25
650
20
60
0.0
0
2
10.0
0.1
0.25
650
20
60
0.0
0
3
3
1.0
0.9
0.25
650
20
60
1.5
2
4
4
10.0
0.9
0.25
650
20
60
73.6
2
5
1
1.0
0.1
0.25
750
20
60
12.0
1
6
2
10.0
0.1
0.25
750
20
60
242.2
2
7
3
1.0
0.9
0.25
750
20
60
4.7
1
8 9
4 5
10.0 5.5
0.9 0.1
0.25 0.00
750 700
20 10
60 60
45.6 0.0
1 0
10
6
5.5
0.9
0.00
700
10
60
12.2
1
11
7
5.5
0.1
0.50
700
10
60
55.7
2
12
8
5.5
0.9
0.50
700
10
60
0.0
1
13
5
5.5
0.1
0.00
700
30
60
0.0
0
14
6
5.5
0.9
0.00
700
30
60
49.4
1
15
7
5.5
0.1
0.50
700
30
60
85.0
2
16 17
8 9
5.5 5.5
0.9 0.5
0.50 0.00
700 650
30 20
60 20
55.2 63.5
1 1
18
10
5.5
0.5
0.50
650
20
20
0.0
0
19
9
5.5
0.5
0.00
750
20
20
42.6
1
20
10
5.5
0.5
0.50
750
20
20
35.9
1
21
9
5.5
0.5
0.00
650
20
100
5.2
0
22
10
5.5
0.5
0.50
650
20
100
0.0
0
23
9
5.5
0.5
0.00
750
20
100
116.9
1
24 25
10 11
5.5 1.0
0.5 0.5
0.50 0.25
750 650
20 10
100 60
125.1 3.8
1 0
26
12
10.0
0.5
0.25
650
10
60
0.0
0
27
11
1.0
0.5
0.25
750
10
60
14.3
0
28
12
10.0
0.5
0.25
750
10
60
79.9
1
29
11
1.0
0.5
0.25
650
30
60
0.0
0
30
12
10.0
0.5
0.25
650
30
60
535.3
3
31
11
1.0
0.5
0.25
750
30
60
44.8
0
32 33
12 13
10.0 5.5
0.5 0.1
0.25 0.25
750 700
30 10
60 20
254.4 0.0
2 0
34
14
5.5
0.9
0.25
700
10
20
30.9
2
35
13
5.5
0.1
0.25
700
30
20
0.0
0
36
14
5.5
0.9
0.25
700
30
20
43.5
1
37
13
5.5
0.1
0.25
700
10
100
17.1
0
38
14
5.5
0.9
0.25
700
10
100
20.7
1
39
13
5.5
0.1
0.25
700
30
100
53.2
0
40 41
14 15
5.5 1.0
0.9 0.5
0.25 0.00
700 700
30 20
100 20
76.1 0.0
1 1
42
16
10.0
0.5
0.00
700
20
20
189.3
3
43
17
1.0
0.5
0.50
700
20
20
5.3
0
44
18
10.0
0.5
0.50
700
20
20
197.1
3
45
15
1.0
0.5
0.00
700
20
100
16.0
0
46
16
10.0
0.5
0.00
700
20
100
652.8
1
47
17
1.0
0.5
0.50
700
20
100
6.7
1
48 49
18 19
10.0 5.5
0.5 0.5
0.50 0.25
700 700
20 20
100 60
338.0 79.7
1 1
run no.
catalyst
1 2
C%
TEM score
The values in the C% and TEM score columns give the experimental results of a particular experiment.
We have set up the synthesis for the preparation of silica based nanocomposite aerogels with different loadings and several dispersed nanophases, including ferrites and metal alloys.9,10 In
order to promote the formation of well-defined nanocrystalline dispersed phase, a specific post synthesis thermal treatment has to be performed. In particular, in order to promote the formation 5895
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The Journal of Physical Chemistry C of a metal or alloy phase, a heat treatment under reducing atmosphere is performed. In this work, we make use of FeCo(Ni)-SiO2 nanocomposite aerogels as catalyst for CNT production. Whereas many reports make use of catalysts where the metal/alloy phase is assumed to form in situ in the CVD reactor due to the reducing environment;11-14 in this case, the formation of the metal/alloy nanophase is performed prior to CVD deposition, so that the metal nanoparticles which are known to be fundamental for the MWCNT growth are characterized in detail prior to their use in the CVD process. This aspect was dictated by the optimization in a DOE approach of three different catalyst-related MWCNT synthesis variables (metal loading, Fe:Co ratio, and Ni content) together with three process-related MWCNT synthesis variables (CVD, temperature, CVD time, and flow of reactive gas). Moreover, the CVD parameter space is sampled systematically by DOE to ensure maximum information gain with a limited number of experiments. The carbon yield and the quality of the MWCNT were used as response variables to judge the CVD results.
’ EXPERIMENTAL SECTION Materials. Tetraethyl-orthosilicate (Si(OC2H5)4, 98%, TEOS); iron(III), cobalt(II), and nickel(II) nitrates (Fe(NO3)3 3 9H2O, 98%, Co(NO3)2 3 6H2O, 98%, Ni(NO3)2 3 6H2O), and Carbamide (NH2CONH2, >99.0%) were purchased from Sigma-Aldrich; absolute ethanol (C2H5OH, 99%) and nitric acid (HNO3, 65%) were purchased from Fluka and Carlo Erba, respectively. Catalyst Preparation. The DOE approach used in this study required the preparation of a total of 19 catalyst samples for the response surface exploration as detailed in Table 1. In particular, catalyst composition was optimized through three parameters: total amount of iron and cobalt, iron to cobalt ratio, and nickel content. In a typical synthesis, 3 cm3 ethanol and 7.9 cm3 tetraethylorthosilicate (TEOS) were mixed in a flask. To this end, we added in 30 min 3.965 cm3 of an ethanol-nitric acid-water mixture dropwise, mixed the solution for 30 min at 50 °C under reflux and cooled it back to room temperature. The appropriate quantities of Co(NO3)2 3 6 H2O, Fe(NO3)3 3 9 H2O, and Ni(NO3)2 3 6H2O were dissolved in 7.5 cm3 ethanol and added to the mixture which was stirred for 10 min. For example, in the synthesis of sample 16, which does not contain Ni, 0.3140 g Co(NO3)2 3 6 H2O and 0.4359 g Fe(NO3)3 3 9 H2O were used while 0.0104 g Ni(NO3)2 3 6H2O was also added in the synthesis of sample 18. The pH of the mixture was 0.6 before adding the gelation initiator solution which consisted of 3.513 g carbamide dissolved in 9 cm3 ethanol and 4.92 cm3 water. After 10 min of mixing the pH rose to 2.2 and the mixing was continued at 85 °C under reflux until a visible increase in the viscosity was observed. At this point, the material was transferred into vessels closed by Parafilm M and kept at 40 °C for 40 h in a laboratory oven. Supercritical drying was performed in a 300 cm3 Parr autoclave containing 70 cm3 ethanol and the sample. The unit was flushed with N2, closed and heated to 250 °C at a rate of 5 °C 3 min-1 then to 330 at 1 °C 3 min-1. The final pressure of 70 bar was maintained for 10 min and then the pressure was allowed to relax to ambient while keeping the temperature at 330 °C. The sample was finally ground and heat treated at 450 °C for 1 h in air to remove any organic residues.
ARTICLE
Figure 1. A typical TEM image of a silica aerogel catalyst (#16 in Table 1.) loaded with 10 weight% FeCo nanoalloy. The inset shows the characteristic XRD profile of the sample.
Catalyst preparation was concluded by reducing the samples at 800 °C for 2 h under a H2 flow of 80 cm3 3 min-1. The temperature was ramped from room temperature to 800 at 10 °C 3 min-1 and the system was allowed to cool down to 25 °C by convection cooling in Ar flow. Carbon Nanotube Synthesis. The DOE approach used in this study takes into account the optimization of three synthesis parameters: the temperature of the CVD reaction, the C2H4 flow rate and the duration of the reaction. Several combinations were tested as requested by the experimental design as detailed in Table 1. In a typical synthesis, we measured 0.1 g catalyst into a quartz boat placed into a tube furnace. The tube was preheated to the desired reaction temperature and flushed by N2 prior to the reaction. A constant N2 flow of 150 cm3 3 min-1 was maintained during the reaction. The amount of the carbon deposit was determined in accordance with eq 1, then the silica support and the accessible catalyst particles were removed by ultrasonication in HF. The advantages of this nonoxidative purification method are that (i) it does not introduce artifacts into the optimization because it leaves the original MWCNT: amorphous carbon ratio intact and (ii) it does not generate hydrophilic functional groups on the nanotube surface.15 Characterization Methods. Powder X-ray diffraction (XRD) profiles were measured on an X3000 Seifert instrument operating with Cu KR radiation. Transmission electron microscopy (TEM) was done on samples drop coated from ethanolic suspension onto copper grid mounted holey carbon films, on a JEOL 200 CX instrument running at 200 kV and a Philips CM10 operating at 80 kV for the catalysts and the carbon nanotubes, respectively. Nitrogen adsorption isotherms were measured on a Fisons Instruments Sorptomatic 1990 system at -196 °C on samples degassed at 200 °C in a vacuum better than 10-1 Pa for 12 h.
’ RESULTS AND DISCUSSION Catalyst Characterization. In Figure 1, we present a typical TEM image and XRD profile of sample 16 (see Table 1) reduced at 800 °C, before the use of the catalyst in the MWCNT production by CCVD. Sample 16 was chosen as a typical example of the studied catalysts since they all featured very similar characteristics, including those containing Ni. The broad reflection at 2Θ = 22° in Figure 1a indicates the presence of 5896
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Figure 2. Representative TEM images of carbon nanotube samples evaluated to a quality score of 0 (a), 1 (b), 2 (c), and 3 (d).
amorphous silica, whereas the peaks at 2Θ = 44 and 65° are characteristic of a bcc FeCo alloy phase.16,17 From the width of the 44° peak, we can estimate the average diameter of the FeCo nanoparticles d = 8.6 ( 0.3 nm which agrees well with the value, d = 9.2 ( 0.6 nm, determined by averaging 50 diameter readings on the corresponding TEM images. The TEM images also indicate that the silica matrix is highly porous and the spatial distribution of the FeCo nanoparticles is homogeneous. The specific surface area of this particular sample was 317 m2 3 g-1 as determined by the BET method and the specific surface area of all catalysts prepared fell into the 300-450 m2 3 g-1 range. Summarizing, the aerogel catalysts maintained their high specific surface area and preserved both the size and the spatial distribution of the FeCo nanoparticles after the reducing treatment at 800 °C. This remarkable stability indicates that FeCoSiO2 aerogels do have a potential for carbon nanotube synthesis under industrial circumstances. Nanotube Growth Optimization. CCVD carbon nanotube synthesis is a typical example of multivariate optimization problems defined in a nonorthogonal multidimensional parameter space which is known to require 3N experiments for optimizing N parameters, even when a simple quadratic response surface function is assumed. Because of the inconvenience of such a rapidly increasing number of experiments with N it is very uncommon to do such a full factorial optimization. Rather, researchers tend to fix k experimental parameters based on their previous experience and optimize only an N-k dimensional subset of the original variables. Even worse is the so-called COST (Change One Separate factor over Time) approach when all variables but one are fixed at predetermined values and the response of the system is studied as a function of the remaining variable. Each variable is scanned this way and the combination of their optimum values is accepted as the global optimum. Actually,
this “optimum” is neither global nor optimum in most cases since COST only works for fully orthogonal variables. Statistical design of experiments (DOE) is the science of obtaining the largest possible amount of information about a system with the smallest number of experiments.18 It tries to make the best trade-offs between the amount of information and the number of runs required to collect it. DOE is rapidly gaining popularity in heterogeneous catalysis and materials science.19-21 Recently, Kuo et al. have established a relationship between MWCNT outer diameter and CVD process parameters using fractional factorial design,22 Porro and co-workers have used the Taguchi method23 to optimize oriented nanotube carpets and Nourbakhsh et al. combined fractional factorial and Box-Behnken designs to optimize certain morphological features of MWCNTs.24 While these predecessor works have focused on optimizing solely CVD process conditions, in the present study we optimize three catalyst-related and three process-related synthesis variables. This task is only possible with our precise control over the catalyst composition achieved by the catalyst synthesis method described above. A full factorial design would require a rather impractical 3(3þ3) = 729 runs to accomplish this task. In our DOE approach, we use a Box-Behnken design with one centerpoint and map the response surface with 49 runs. This is an independent quadratic design in which parameter combinations are at the center and at the midpoints of edges of the process space. The success of the optimization depends largely on the adequate definition of the response variables. In carbon nanotube synthesis, the two most plausible ones are the total carbon yield and the product quality. These varied on a broad scale as we proceeded with the DOE plan. The carbon yield of each catalytic reaction was assessed by calculating the carbon deposit % 5897
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Figure 3. Main effects plots revealing the influence of the process parameters on the C% value (a) and TEM score (b).
(denoted as C% from now on) as defined in eq 1: C% ¼
cat mcat af ter - mbef ore 3 100% mcat bef ore
ð1Þ
where mcat after is the mass of the catalyst after reaction (i.e., catalyst þ deposited carbon) and mcat before is the weight of the catalyst introduced into the reactor. The C% defined in this way is a reliable indicator of CCVD catalyst performance as long as results obtained on the same experimental bench are compared.25 Assessing the quality of the nanotube product is not straightforward because there is no standardized measurement of nanotube purity or quality defined in the literature despite the considerable effort invested in the past.26,27 In the case of single-wall carbon nanotubes, it is possible to define numerical quality descriptors based on Raman spectra or fluorescence measurements, but for MWCNTs it is most typical to rely on the subjective evaluation of TEM images. We have evaluated at least 7 TEM images taken at different spots for each sample using a scale of 0, 1, 2, and 3 and defined the “quality” of the sample by taking the rounded average of the individual image TEM scores (zero corresponding to “poor nanotube production/quality” and 3 meaning “only good quality MWCNTs without significant contamination”). In Figure 2, a
characteristic example is given for each TEM score level. Additional TEM images are reported as Supporting Information. Both the quality and the quantity of the carbon deposit varied largely during the 49 experiments. It can be seen in Table 1 that while some catalyst offered consistently poor (No. 5) or good (No. 16) performance, there were also samples (No. 12) whose operation depended on reaction conditions a lot. In order to correctly assess the effect of each optimized parameter on the C% value and the TEM score in Figure 3, we present main effect plots for both target factors. Increasing the Fe þ Co wt% significantly improves both product quantity and quality. However, a longer synthesis time obviously results in more carbon deposit of inferior quality. Concerning synthesis temperature, the average of the TEM scores of samples synthesized at 650 °C is lower than that of samples prepared at 700 and 750 °C. However, there is no significant difference between these latter two temperatures. The amount of carbon deposit increases with the C2H4 flow rate, experiences a maximum as a function of the Fe/Co ratio and is not significantly influenced by the Ni contents of the catalyst. Whereas the effect of Ni wt% is similar on the TEM score as well, in the case of the other two parameters a reverse behavior can be observed: increased nanotube quality improves with Fe/Co ratio and is a maximum type function of C2H4 flow rate. 5898
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Figure 4. Interaction plots showing the combined influence of each process parameter pair on the C% value.
Figure 5. Interaction plots showing the combined influence of each process parameter pair on the TEM score.
Table 2. Linear Coefficients of the Fitted C% and TEM Score Response Surface Functions Given in eqs 2 and 3 i RC%i RTEMi
1
2
3
4
5
6
-58.5 1692.9 -279.4 28.8 45.5 -11.5 -0.2684 24.4271 -6.3472 0.1923 0.396 -0.0251
Even more insight into the behavior of the system can be gained by studying the pairwise interaction plots of the variables for carbon deposit amount (Figure 4) and CNT
quality score (Figure 5). It is particularly interesting to find settings where an effect is only pronounced for one parameter level: compare the 10 wt % Fe þ Co curves to the 1.0 wt % and 5.5 wt % curves in the top row of both figures. Effects can even be reversed. In the Fe/Co ratio vs CVD temperature pair comparison for TEM score (Figure 5), we can see that increasing the reaction temperature is beneficial for product quality if the Fe/Co ratio in the catalyst is low but unfavorable if the Fe/Co ratio is high. 5899
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In order to find the optimal value of all six studied variables, we have fitted the C% and TEM score values reported in Table 1 with quadratic functions. Results following the general scheme defined by eqs 2 and 3 are reported in Tables 2-4. The fitted six
dimensional response surface for the C% value is demonstrated by two-dimensional projections in Figure 6 and is given by the function: 6
C% ¼ - 10554:7 þ Table 3. Quadratic Coefficients of the Fitted C% Response Surface Function Given in eq 2 β
C%
j
ij
i
1
1
3.9
2
2
3
4
5
6
-8.1
-33.1
0.0
1.9
0.4
-394.2
-183.9
-1.7
1.5
-0.4
152.3
0.7 0.0
2.4 -0.1
-1.6 0.0
0.1
0.0
3 4 5 6
βTEMij
j
i
1
2
1
0.0158
-0.0694
0.0000
0.0003
0.0111
-0.0028
0.6944
-5.0000
-0.0313
-0.0156
-0.0078
0.0100
0.0000
0.0188
-0.0001
-0.0005
0.0001
-0.0026
0.0003
2 3 4 5 6
3
2.4444
4
5
6
-0.0001
6
ð2Þ
where a1...a6 denote the studied parameters Fe þ Co wt% [wt%], Fe/Co ratio, Ni wt% [wt%], CVD temp [°C], C2H4 flow rate [cm3 3 min-1], CVD time [min], respectively, and RC%i and βC%ij denote the linear and quadratic coefficients of the fitted response surface. The numerical values of these coefficients are given in Tables 2 and 3, respectively. Similarly, the two-dimensional projections of the TEM score response surface are presented in Figure 7 and the full function is given by the following: 6
TEM ¼ - 79:97 þ
0.0
Table 4. Quadratic Coefficients of the Fitted TEM Score Response Surface Function Given in eq 3
6
∑ RC% ∑ βC% ij ai 3 aj i 3 ai þ i ∑ i¼1 ¼1 j¼i
6
6
∑ RTEM ∑ βTEM ai 3 aj ij i 3 ai þ i ∑ i¼1 ¼1 j¼i
ð3Þ
where a1...a6 denote the studied parameters Fe þ Co wt% [wt%], Fe/Co ratio, Ni wt% [wt%], CVD temp [°C], C2H4 flow rate [cm3 3 min-1], CVD time [min], respectively, and RTEMi and βTEMij denote the linear and quadratic coefficients of the fitted response surface. The numerical values of these coefficients are given in Tables 2, 3 and 4, respectively. A parameter set which is able to maximize either C% or TEM score could be obtained analytically from eqs 2 or 3. However, our goal is to optimize the MWCNT yield and this requires the simultaneous maximization of both responses. This was achieved by linearly mapping both the 0 < C% < 700 and 0 < TEM < 3 regimes to the 0...1 interval. The scaled value was denoted as the “desirability” (D) of a certain parameter set with respect to the
Figure 6. Contour plots of slices of the fitted C% response surface. Each plot demonstrates the behavior of the system as a function of two variables, with the other four variables set to their middle values as follows: Fe þ Co wt% 5.5%, Fe/C ratio 0.5, Ni wt% 0.25, CVD temp 700 °C, C2H4 flow rate 20 cm3 3 min-1, and CVD time 60 min. 5900
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Figure 7. Contour plots of slices of the fitted TEM response surface. Each plot demonstrates the behavior of the system as a function of two variables, with the other four variables set to their middle values as follows: Fe þ Co wt% 5.5%, Fe/C ratio 0.5, Ni wt% 0.25, CVD temp 700 °C, C2H4 flow rate 20 cm3 3 min-1, and CVD time 60 min.
studied response, and the composite desirability (Dcomp) of a parameter set was defined as the linear combination of the individual D values. This is a general approach to cut the dimensionality of a simultaneous optimization problem to just one: finding the parameter set which maximizes Dcomp. In our case, the optimum was found at Fe þ Co = 10 wt %, Fe/ Co = 0.8922, Ni = 0 wt %, CVD temp = 654 °C, C2H4 flow rate = 30 cm3 3 min-1 and CVD time = 85.6 min. This set gave the highest Dcomp at 0.765 and predicted C% = 488.1, TEM = 2.66 for the optimized responses. These parameters are similar to those used in run no. 30 and the predicted C% and TEM score values are also in line with those measured in run no. 30 which can thus be regarded as the experimental validation of the optimum. It should be pointed out that in the range of compositions studied in this work, the Ni content was found not to be a very important parameter in determining the catalytic outcome. The results obtained indicate that a high yield and quality of the nanotubes can be obtained by the investigated catalysts, although a quantitative comparison of the catalyst features with the performances reported for catalysts of similar composition is not straightforward due both to different growth conditions and preparation routes. In particular, a maximum yield of 200% was obtained by zeolite-supported equiatomic FeCo alloy with a metal loading of 5 wt %,12 and a yield of 60% was achieved by FeCo on mesoporous templated silica catalysts with a 2.5 wt % metal loading.11 Double-walled CNTs are selectively obtained in the latter report: in our study, MWCNTs are obtained as expected based both on the nanoparticle size and the relatively low deposition temperature. Single-walled CNTs (with yields up to 80%) were obtained on nanocomposite aerogels prepared by a cogelation route28 having a Fe/Mo phase dispersed on alumina.
It is noteworthy that in our samples, we only observed carbon nanotubes with variable degrees of uniformity and homogeneity as a function of the catalyst composition and deposition conditions; however, we did not have any evidence by TEM or XRD investigation of significant carbonaceous or graphitic deposits. This result further supports the effectiveness of our catalysts and in particular may be ascribed to the large pores and extended surface area of our catalysts at high temperatures, as silica collapse is often responsible for graphitization.11
’ CONCLUSIONS Nineteen different FeCo(Ni)/SiO2 catalysts were prepared in a controlled fashion that allowed us to explore a six-dimensional parameter subspace of heterogeneous catalytic multiwall carbon nanotube synthesis. By using the statistical design of experiments (DOE) approach, we were able to cut the number of experiments required for optimization from 729 to 49. Summarizing, we have found that a well-designed supported transition metal SiO2 aerogel catalyst for CVD MWCNT growth should contain a high loading of iron and cobalt in similar amount, should be free of nickel and should be operated at a relatively low temperature at high carbon source space velocity for optimum performance. The main result of this work is demonstrating that by combining DOE with precise control over the structure of supported transition metal aerogel catalysts, it is possible to perform complex optimizations in which both catalyst-related and process-related variables are simultaneously optimized. We anticipate that the current expansion of the preformed nanoparticle based heterogeneous catalysis field29-31 will open numerous opportunities for the exploitation of this concept. 5901
dx.doi.org/10.1021/jp111860x |J. Phys. Chem. C 2011, 115, 5894–5902
The Journal of Physical Chemistry C
’ ASSOCIATED CONTENT
bS
Supporting Information. TEM images of several samples. This material is available free of charge via the Internet at http://pubs.acs.org.
’ AUTHOR INFORMATION Corresponding Author
*Phone: þ39 070 6754351; Fax: þ39 070 6754388; E-mail:
[email protected].
’ ACKNOWLEDGMENT This work was supported by the EC FP7 STREP “THEMACNT” (NMP3-SL-2009-228539), the Hungarian Scientific Research Fund (OTKA) through projects NNF-78920 and 73676, and the Italian Institute of Technology (IIT) under the SEED project “NANOCAT”. The NIST/SEMATECH e-Handbook of Statistical Methods, http://www.itl.nist.gov/div898/handbook/ is acknowledged for DoE-related information.
ARTICLE
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