Effects of Precursor Type on the CVD Growth of Single-Walled Carbon

Article pubs.acs.org/JPCC

Effects of Precursor Type on the CVD Growth of Single-Walled Carbon Nanotubes Diego A. Gómez-Gualdrón,†,‡ Jenni M. Beetge,† Juan C. Burgos,†,‡ and Perla B. Balbuena*,†,‡ †

Artie McFerrin Department of Chemical Engineering ‡Materials Science & Engineering Program Texas A&M University, College Station, Texas 77843, United States S Supporting Information *

ABSTRACT: Single-walled carbon nanotubes (SWCNTs) can be grown using chemical vapor deposition methods consisting of the catalyzed decomposition of a precursor gas on the surface of (usually) metal nanocatalysts. Among the many variables affecting the process, the nature of the precursor gas is particularly interesting because nanotube growth mechanism may be closely dependent on the specific decomposition products. Here we investigate and compare simulated SWCNT growth under two precursor decomposition products: single carbon atoms and dimers. Specifically we evaluate the effect of the precursor type on carbon association within the nanoparticle, nanotube quality, and nanotube chirality. Compared to a synthesis based on carbon atoms as the main dominant species, it is found that when carbon dimers are the main precursor decomposition products, carbon dissolution as well as carbon association inside the nanoparticle are slowed down, and nucleation is accelerated, increasing the risk of carbon encapsulation. In the growth stage, dimers can be incorporated into the nanotube rim without previous splitting. As a consequence, the kinetics of self-healing is altered, favorably affecting the quality of the nascent tube, evaluated through the formation of hexagonal rings and occurrence of bamboo growth. Finally, the dimer precursor product results in a moderate bias toward formation of near-arm-chair chiralities.

1. INTRODUCTION Single-walled carbon nanotubes (SWCNTs) are a particularly attractive material with outstanding electronic and optical properties impacting a number of technological fields, including biomedicine1 and electronics.2,3 Some applications demand nanotubes of either semiconductor or metallic character, wherein the electronic character of SWCNTs is connected to structural features such as chirality.4 Therefore, controlling the nanotube structure/chirality during synthesis to produce selectively either metallic or semiconductor nanotubes is one of the most sought-after goals in nanotechnology. It must be noted, however, that a thorough and comprehensive experimental exploration of conditions that may lead to chiral selectivity is a challenging task due to the notoriously wide parameter space of nanotube growth. Therefore, molecular simulation techniques play a vital role in both helping explore this space through theoretical calculations, as well as guiding a rational exploration of this space through experimental means. Nanoparticle structure has been proposed as one growth parameter potentially playing an important role in strategies to achieve chiral/structural control of the nanotube during chemical vapor deposition (CVD) synthesis.5−10 This hypothesis has been explored both computationally5,6,8,11 and experimentally.9,10,12,13 However, the effect of using different precursor gases has not been examined in detail. Resasco et al.14 showed that the chiral distribution is changed when the precursor gas is changed from carbon monoxide to © 2013 American Chemical Society

methane using a cobalt-based catalyst. Recently, Lehtonen et al.15 reported similar results, wherein decomposing methane instead of carbon monoxide on an iron catalyst increases the number of low chiral angle nanotubes. Moreover, recent efforts on preferential growth of semiconducting nanotubes (>90%) rely on the use of specific precursor gas mixtures such as methanol and ethanol or isopropyl alcohol (IPA).16,17 It is plausible that the use of different precursors varies the relative abundance of various carbon (or chemical) species, (potentially) altering the nanotube growth route. The relative abundance of these species may dictate how the nanotube rim is attacked, or how carbon transport occurs (depending on the ability of each species to undergo surface or bulk diffusion). Furthermore, the latter may in turn alter the nanocatalyst morphology. All these factors may ultimately affect the resulting nanotube chirality. In previous theoretical work,18 we determined that, in a C2-based growth, near-armchair nanotubes are expected to be kinetically favored, in agreement with recent experimental measurements of individual nanotube growth rates using a C(2) precursor (ethylene).19 The association of carbon within the nanoparticle is relevant to carbide formation within the nanoparticle since metal carbides are ordered structures with carbon atoms typically Received: December 19, 2012 Revised: April 29, 2013 Published: May 2, 2013 10397

dx.doi.org/10.1021/jp3125236 | J. Phys. Chem. C 2013, 117, 10397−10409

The Journal of Physical Chemistry C

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dynamics of the metal nanoparticle to dictate how C2 splitting (or diffusion) occurs. This is designated as a C(2) precursor. As discussed above, C2 species may be present when precursors such ethylene are used, according to DFT predictions.28 We use reactive classical molecular dynamics (RMD) to investigate effects related to the use of either a C(1) precursor or a C(2) precursor during simulated CVD nanotube growth on supported nickel nanoparticles. We investigate effects on the association of carbon atoms in the nanoparticle, the quality of the nanotube structures, and the chiral angle. In our simulations we vary the precursor gas type, the nanoparticle size, and the physical interaction with the support.

separated from each other (nonassociated), and located in octahedral cages constituted by six metallic atoms.20 Whether carbide formation occurs during (or is needed for) nanotube growth remains a controversial topic. Results seem to be system-dependent with both research reports supporting21,22 and refuting23,24 carbide formation being found in the literature. Also, it seems that similarities between the structure of the carbide and the metal hinders the distinction between the two through diffraction patterns.25 Additionally, distortion effects due to curvature effects at the nanocatalyst size may further hinder such distinction. It is also possible that nanotube growth can occur both on metal-carbide and metal nanoparticles, albeit with some differences in the growth mechanism (e.g., carbon transport).26 Growth on either type of structure may present distinctive advantages, thus understanding how growth parameters (e.g., precursor gas type) may lead to one or the other being important for fine-tuning growth conditions. Experimental determination of the species involved in nanotube growth is a challenging task. Zhou et al.16 performed mass spectrometry to exhaust gases during IPA-based CVD nanotube synthesis; however, the determined species distribution does not necessarily correspond to the one locally found at the nanocatalyst location. On the other hand, Okasaki et al.27 used a mixture of 13CH4 and 12C2H2 to determine a higher contribution of 12C2 to the final nanotube structure; however, details of intermediate processes were not obtained. On the other hand, modeling efforts have identified the nature of the possible products of decomposition of hydrocarbons on transition metal surfaces. For example, DFT calculations by Rösch et al.28 modeled the decomposition of ethylene on nickel. It was shown that within the sequence of steps C2H4 → C2H2 → C2H → C2 → 2C the splitting of C2 into individual C atoms has the highest activation energy (1.47 eV), and this activation energy is higher than that for dimer diffusion on a nickel surface (0.5 eV calculated by Pasquarello et al.29). In addition, Shibuta et al.30 have investigated methane dissociation via ab initio molecular dynamics simulations, concluding that the decomposition yields isolated C and H atoms. Higher molecular weight species such as propane may decompose in several stages, most likely initially to propene and then to ethylene and radicals suggesting a mixture of C and C2 species may be present at the synthesis conditions. In this work, the carbon feeding process is modeled so that when a precursor gas molecule impinges a free catalyst site, catalysis (liberation of C atoms) occurs according to a conversion factor f (f = 1.0 is used in this study). Competition of C atoms for the various adsorption sites (hollow, bridge, top) is taken into account by our force field as discussed in our previous work.5 We clearly showed the preference of C to be adsorbed in the hollow sites, followed by bridge and top sites, in agreement with the DFT results. The effect of the “conversion factor f ” being equal to 1 implies an accelerated growth process (done to keep the computational times reasonable) that induces the formation of several defects on the nanotube walls. Such defects for the same reason are not healed sufficiently rapidly; thus, the quality of the CNT decreases to a certain extent. If the catalysis reaction is modeled as precursor → C, a single C atom appears at the location where a precursor gas molecule impinges the nanoparticle surface. We designate this as a C(1) precursor. On the other hand, if the catalytic reaction is modeled as precursor → C2 a carbon dimer appears at the location where the precursor molecule impinges the nanoparticle surface, while leaving the

2. COMPUTATIONAL METHODS Nanotube growth was simulated for 5.0 ns using our reactive classical molecular dynamics code, SIMCAT.31 A 0.5 fs time step was used in our simulations, with configurations collected every 0.5 ps, thus producing simulation trajectories of 10 000 frames. A particular simulation contains a nickel nanoparticle of a given size supported on a model graphene-like support. The metal/support (MS) interaction, i.e., Eadh, was artificially varied among simulations through the parameter α in eq 1, thus readily representing physical interactions with different supports. Realistic substrates may differ from our model substrate in two aspects: (1) Chemical effects between the support and the nanoparticle, and (2) epitaxial effects due to specific geometric patterns. Effects related to (1) may affect the process by which the nanoparticle is formed, and its final chemical state. Here we assume that the nanoparticle is in the metallic reduced state as reported in many works,14,32 and use our support model to describe and focus on the physical interaction between support and nanoparticle. Five Eadh values have been represented here: −0.16 eV, −0.26 eV, −0.43 eV, −0.70 eV, and −1.39 eV. Association of these values with experimentally and theoretically estimated values of Eadh have been reported elsewhere.5 Based on the resulting nanoparticle behavior, the Eadh values used here covers the range from weak to strong metal/support interactions. Three nickel cluster sizes were used, namely, Ni32, Ni80, and Ni160. For each cluster, the diameter varies depending on Eadh. Ni32 corresponds to 1.0−1.3 nm, Ni80 to 1.3−1.6 nm, and Ni160 to 1.6−2.0 nm diameters. Eij = αVrepulsion(rij) − α1.1Vattraction(rij)

(1)

The general eq 1 also describes metal/carbon (MC) interactions through a reactive bond order (REBO) potential. While electronic effects are not explicitly considered, they are implicitly included in the density functional theory (DFT)derived parameters. Thus effects such as screening of CC interactions inside the nanoparticle are modeled by the force field. A detailed description and parametrization of this force field are described in ref 31. The adjustable parameters of our force field have been proven useful for phase-space exploration to understand how factors such as temperature, nanocatalyst size, nanotube/catalyst interaction (Wad), and metal/support interaction (Eadh) interplay to enable nanotube growth.33,34 Finally, metal/metal (MM) interactions are described by the Sutton Chen potential,35 and carbon/carbon (CC) interactions by a modified Brenner potential.31 A unified atom scheme is used to model the precursor gas molecules that fill the simulation box according to the set gas pressure. This model emulates typical CVD conditions where precursor gas molecules surround the supported catalyst 10398



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Figure 1. (a) Exemplary colored charts for Ni32, Ni80, and Ni160 based on probability distribution histograms of the number of C1, C2, C3, C4, and C5 chains inside the nanocatalyst during simulated nanotube growth (1000 K). The empirical probability P(n) is estimated as the ratio between the absolute f requency that n units of Ci were detected and the total number of observations. The number in each square corresponds to the most probable number n of Ci chains (i.e., the mode) for a given Eadh, wherein the color correspond to the probability associated with that n. (b) Exemplary bar diagram showing the maximum number n of Ci chains detected inside Ni32, Ni80, and Ni160 nanoparticle during simulated nanotube growth (1000 K). (c) Exemplary cross-sectional scanning showing slices of the nanoparticle structure (Ni80, Eadh = −0.43 eV) along the direction normal to the support. Carbon, nickel, and support atoms are green, blue, and black, respectively. The large carbon ‘chain’ surrounding the particle belongs to the nanotube structure.

carbon atom with five or less MNN are classified as nondissolved atoms.

nanoparticle. The initial velocity distribution of the precursor gas is assigned according to the Maxwell velocity distribution at the simulation temperature. The movement of the precursor molecules (vapor phase) and metal and carbon atoms (condensed phase) is dictated by Langevin dynamics.36 A different Langevin friction factor is used for the vapor and condensed phase to account for the faster dynamics of the former. In this work, the simulation temperature was set at 1000 K, and the precursor gas pressure was set to either Pgas ∼ 11 atm or Pgas ∼ 5 atm. The selected pressures are in the range of typical pressures for CVD processes: 1−10 atm in the CoMoCAT process14 to ∼30−50 atm in the HiPCO process.37 For the implementation of the C(2) precursor, the velocity vectors of both newly created dimer atoms point initially in the same direction with a magnitude consistent with the instantaneous mean velocity of the condensed phase at the particular configuration. To determine the association of carbon atoms in the form of C2, C3, C4, and C5 chains, a hierarchical clustering algorithm is used as follows. An initial carbon atom is assigned the class 1. Using a cutoff of 1.7 Å, the number and identity (index) of carbon atoms bonding the class 1 atom is determined and assigned the class 2, then the number and identity of carbon atoms bonding the class 2 atoms are determined, and assigned the class 3, and so forth, until no more atoms bonding other atoms in the previous classes are found. The total of number of atoms associated this way correspond to the ‘length’ i of species Ci. This procedure is repeated for atoms not included in previous iterations, until the number of each species Ci is determined. To distinguish whether a carbon species is within the nanoparticle or on the nanoparticle surface, a metal coordination-based criterion is established. Namely, using a cutoff of 2.5 Å, carbon atoms with more than five metal nearest neighbors (MNN) are classified as dissolved atoms, whereas

3. RESULTS AND DISCUSSION 3.1. Association of Carbon Inside the Nanoparticle. 3.1.1. Observations Common to Both Precursor Types. The hierarchical clustering algorithm described in section 2 was used to analyze the bonding of carbon atoms within the nanoparticle. Accordingly, for each combination of nanoparticle size and metal/support interaction Eadh, the number of C1, C2, C3, C4, and C5 chains inside the nanoparticle was determined at each of the 10 000 configurations that constitute a simulation trajectory (time between configurations is 0.5 ps). At the simulation temperature (1000 K), these carbon species form and break continuously due to available thermal energy, and perturbations due the dynamic evolution of the nanoparticle structure and carbon transport processes. Upon this realization, we used a statistical approach to analyze the association of carbon atoms. We must emphasize, however, that nanotube growth is a nonequilibrium process. Therefore, the histograms presented here do not intend to describe probability distribution of equilibrated systems, but rather to describe the dynamics of constantly evolving systems. We focus on determining the typical distribution of carbon species within the nanocatalyst, understanding that the less carbon atoms are associated with each other inside the nanoparticle, the more plausible it is that metal carbide forms. First, we determine how many carbon species Ci are found in each configuration of a particular simulation trajectory. Thus, we determine the frequency with which a particular n number of Ci chains are found within the nanoparticle. The ratio between this frequency and the number of observations (10 000) produces the empirical probability P(n) of finding n Ci chains within the nanoparticle at a randomly selected 10399



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a mass basis. For instance, for Ni160 and Eadh (using the mass of one carbon atom as a mass unit), the modes for C1, C2, and C3 correspond to 20, 22, and 18 mass units, respectively. This demonstrates that carbon monomers, dimers, and trimers are the most relevant carbon species. For species C1 through C3, increasing the particle size from Ni32 to Ni160 decreases the probabilities of the corresponding mode values. In Figure 1a, the probability for the mode of C1 is in the 30−40% range for Ni32, 15−25% range for Ni80, and 10− 15% for Ni160. Similar numbers/trends can be obtained for C2 and C3. This correlates well with the probability distribution of C1 through C3 species becoming broader and flatter as the particle size changes from Ni32 to Ni160. This indicates that the dynamics (formation and breaking) of C1 through C3 species increases as the nanocatalyst size increases. On the contrary, for species C4 and C5, increasing the particle size increases the probabilities of the corresponding mode values. The probability for the mode of C5 is in the 5−20% range for Ni32, 10−40% for Ni80, and 35−50% for Ni160. Thus, the dynamics (formation and breaking) of C4 and C5 species decreases as the nanocatalyst size increases. This is explained on the basis of the larger volume, and number of carbon atoms available (solubility increases from Ni32 to Ni160) to form larger chains. We did not find a clear correlation between metal/support interaction Eadh and the association of carbon within the nanoparticle. This occurs because the effect of the mobility of nanocatalyst atoms (which is altered by the value of Eadh) on the formation and breaking of CC bonds is not clear. On one hand, the higher metal atom mobility that results from a weak metal/support interaction facilitates carbon diffusion within the nanoparticle, increasing the probability of carbon atoms encountering to form a CC bond. However, the higher metal atom mobility also increases the probability of such CC bonds breaking due to nanoparticle structural/shape fluctuations. On the other hand, the lower metal atom mobility that results from a strong metal/support interaction decreases the probability of two carbon atoms encountering to form a CC bond, but also hampers CC bond breaking due to higher structural stability. Elsewhere,39 we have reported density profiles suggesting a layered nanoparticle structure consisting of intertwined metal and carbon layers parallel to the support. To explore directly the appearance of these layers, we performed a visualization exercise scanning the nanoparticle structure along the direction perpendicular to the support. Specifically, the nanoparticle is cut in slices parallel to the support. An example of our observation is presented in Figure 1c, which shows a scan to the final structure of Ni80 nanoparticle (Eadh = −0.43) exposed to C(1) precursor gas. The thickness of each slice was selected to capture a single metal layer, and the subsequent carbon layer on top. (It can be said that scan for metal and carbon layers are ∼1.0 Å ‘out-of-phase’.) For instance, the left-most panel in Figure 1c shows the metal contact layer corresponding to a metal density profile peak centered at ∼2.0 Å, and the subsequent carbon layer corresponding to a carbon density profile peak centered at ∼3.0 Å. Since the metal−metal interlayer separation was determined to be 2.0 Å, the position of each metal slice is approximately shifted 2.0 Å with respect to adjacent metal slices. Figure 1c (bottom) includes a schematic illustrating how the scan was performed. If one focuses on the structure of the four metal layers presented in Figure 1c, it is apparent that the metal layers appear less ordered as their distance to the support increases. The contact layer (left-most panel), although somewhat

configuration. The most probable number (excluding zero) of Ci chainsor modeand the maximum number of Ci chains within the nanoparticle are also readily determined from the obtained probability distributions. The charts/diagrams in Figure 1a,b summarize the above information for combinations of nanoparticle size (Ni32, Ni80, and Ni160), and metal/support interaction (|Eadh| = 0.16−1.39 eV). Only the cases using the C(1) precursor were presented in Figure 1, because no outstanding differences were captured with this type of analysis between the C(1) and the C(2) precursor cases. In Figure 1a, the number in each cell (which represents a particle size/metal−support interaction combination) corresponds to the mode value as previously explained, and the color of each cell corresponds to the probability of such mode value according to the side color scale. For instance, for Ni160 at Eadh = −0.16, it is most probable to encounter 20 monomers (C1), 11 dimers (C2), six trimers (C3), three tetramers (C4), and one pentamer (C5). The probabilities are P(20) = 15% for C1, P(11) = 15% for C2, P(6) = 20% for C3, P(3) = 30% for C4, and P(1) = 40% for C5. Notice that the probabilities for each species are calculated independently from each other. The information in Figure 1a is complemented with that in Figure 1b. The latter bar diagram lists the maximum number (Nmax) of each species C1 through C5 that can be found within the nanoparticle. The variable Eadh is not included in Figure 1b, because Nmax did not show any dependence on the metal/ support interaction. For each species, comparison between Nmax and the mode reflects how broad the probability distribution for each species Ci is. Considering Ni160 for Eadh = −0.16 eV once again, it is seen that for C1 the mode is 20 and Nmax is 38, which is a broad distribution compared to that for C5, wherein the mode is one and Nmax is five. The relative breadth of these two distributions partly explains why the probability of the mode value for C5 is higher than for C1. As already mentioned, the trends observed for both C(1) and C(2) precursor cases are similar, and are discussed as follows. The most apparent observation/trend is that the probability for the mode values for species C2 through C5 is not zero. Therefore indicating that association of carbon atoms within the nanoparticle occurs at a signif icant extent. This observation agrees with radial distribution functions (RDFs) calculated for the CC pairs (not shown), which in all cases feature a welldefined peak centered at 1.25 Å. The location of that peak denotes the existence of CC bonds within the nanoparticle. Furthermore, comparison between the maximum number of nonassociated carbon atoms (C1) and the corresponding solubility value for each case leads to the same conclusion. These solubility values have been reported elsewhere,38 corresponding to the order of 10 carbon atoms for Ni32 (∼6 wt %), 40 atoms for Ni80 (∼9 wt %), and 100 atoms for Ni160 (∼11 wt %). Since Figure 1b indicates that Nmax for C1 is 5 for Ni32, 17 for Ni80, and 38 for Ni160, then at least 5, 27, and 62 carbon atoms are associated into chains of different lengths for Ni32, Ni80, and Ni160, respectively. From Figure 1a is apparent that the mode values for species Ci decrease as the chain length i increases. This an expected result considering that carbon atoms enter the nanoparticle in monomer form (C1), and then have to diffuse through the nanoparticle to find each to form CC bonds, and associate into larger chainsa process that is hindered due to the screening of CC interactions within the nanoparticle. However, notice that at least for C1 through C3 the differences are not as significant on 10400



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octahedral positions, and reduction in the association of carbon atoms within the nanoparticle would be consistent with a potential carbide formation. Thus, we now proceed to examine what would be thermodynamically expected in this aspect using a DFT-derived force field.31 As a simple initial approximation, we discuss this in terms of the regular solution model approximation.44 The basic idea behind the regular solution model is that a AB binary system will tend to minimize energy by favoring the formation of bonds that are most stable (criterion is bond energy ε), and this determines the segregation, randomness, or ordering of the phase(s). This prediction is based on the comparison between (εAA + εBB) and (2εAB). Considering values for MM, MC, and CC interactions (εMM ∼ 0.70 eV, εCC ∼ 5.0 eV, and εMC ∼ 2.3 eV) calculated via DFT in previous work,6 then εNiNi + εCC ∼ 2εNiC and some CC association might be expected due to the predicted random character of the metal−carbon phase. However, the used εCC corresponds to CC bonds in the nanotube cap and/or wall. Inside the nanoparticle, the surroundings of C atoms vary, and metallic atoms electronically screen CC interactions. DFT calculations31 estimate a weaker CC interaction (εCC)in of ∼0.4 eV; an effect captured by our force field. Accordingly, εNiNi + (εCC)in < 2εNiC. Notice that MC (2.3 eV), followed by MM (0.7 eV), are the most favorable bonds; consequently, coordination of metal atoms around carbon atoms should be favored. Therefore, according to this thermodynamic model, nanoscopic phase separation should not be expected in our simulations. We note that the regular solution model was originally developed for substitutional solutions. We include a more general model as Supporting Information, and show that metal coordination around carbon atoms will be energetically favored no matter whether our calculations are based on substitutional or interstitial models. 3.1.2. Differences Arising from a Different Precursor Type. The occurrence of nanoscopic separation showcases the nonequilibrium nature of the nanotube growth process, suggesting that the evolution of catalytic systems for nanotube growth is particularly susceptible to kinetic factors. Using different precursor gases may alter reaction pathways, and characteristic timeframes for a variety of processes occurring during nanotube growth. In this subsection, we discuss differences in the carbon association within the nanoparticle, with the use of either a C(1) or C(2) precursors. We continue using a statistical approach, now to determine the preferred type of carbon association within the nanoparticle, i.e., the most abundant or dominant species (C1, C2, C3, C4, or C5). For a particular configuration in our simulations, the dominant species is that one that is found in higher quantity. Accordingly, we collected information determining the dominant species in each of 10 000 configurations that constitute a simulation trajectory. Then we define the dominance probability DP(i) for a Ci species to be the dominant species in a randomly selected configuration. For instance, for a simulation case, DP(2) is defined as the ratio between the number of configurations in which C2 dimers are found to be the dominant species and the total (10 000) number of configurations. The resulting probability distributions are presented in Figure 2 for Ni32 using C(1) or C(2) precursors (all other distributions are shown in Supporting Information Figure S3). This type of histogram showcases differences in the state of carbon association within the nanoparticle due to different

distorted, retains the characteristics of a (111) fcc crystallographic plane. In the subsequent layers, the (111) pattern becomes less noticeable due to reduced order, and ‘vacancies’ or holes in the structure become common. Since carbon atoms occupy these holes (not captured in the panel because the scan for metal and carbon layers is out-of-phase as explained in the previous paragraph), this indicates that distortion of the metal lattice occurs due to the association of carbon atoms, more notably farther from the support. These observations hold true (qualitatively) for all our simulations (with either C(1) or C(2) precursors). If one focuses on the structure of the carbon layers, the association of carbon atoms within the nanoparticle stands out. For the example presented in Figure 1c, the final structure of the carbon layer closest to the support (left-most panel) contains three dimers (C2), two tetramers (C4), one trimer (C3), and one monomer (C1). These types of carbon species are also observed in the subsequent carbon layers presented in the other panels in Figure 1c, in agreement with the statistical analysis presented in Figure 1a,b. Small holes observed in the metal layers are to be occupied by C1 and C2 (probably C3 as well) species, whereas larger holes are to be occupied by larger species such as C4 and C5. The observed association gives each slice an appearance similar to that observed in microphase separation in block copolymers,40 and it will be referred to here as nanoscopic phase separation. This nanoscopic phase separation is consistent with, and may be responsible for, the layered nanoparticle structure suggested by nanoparticle density profile analyses presented in ref 39. In the latter report, some nanoparticle features consistent with conditions favorable for carbide formation (or at least harbingers of such formation) such as the establishment of a core (metal−carbon) shell (metal) nanoparticle structure, and particle metal−carbon stoichiometry consistent with known carbides were observed. The reported layered structure may be considered an indication of order in the nanoparticle and a harbinger of carbide formation, but the carbon association observed here indicates that metal carbide has not yet formed. It may be possible that carbide formation will not happen, but it is also possible that spontaneous carbide formation needs simulation times larger than typical simulation times for nanotube growth (5.0 ns in this work). As a comparison, spontaneous formation of ordered phases such as gas clathrates from a disordered phase (water + methane) of the right composition needs simulations on the order of microseconds.41 Diarra et al.42 reported results of tight-binding Monte Carlo simulations regarding carbon solubility as a function of particle size. Their analyses indicate that solubility increases with particle size, which is the opposite trend to that found in our recent work38 and in previous literature.43 However, Diarra et al.’s model overpredicts the melting temperature (for example, the melting temperature reported for a particle of 405 atoms is ∼1628 K). Therefore, it is possible that the largest calculated particles are only surface-melted at 1000 K, thus yielding reduced solubility. On the other hand, these authors also reported the existence of dimers in the subsurface and moderate segregation tendency which is in agreement with our current findings. Another factor is that fine-tuning our force field aiming to reproduce metal carbide structures may facilitate their spontaneous formation, although extended simulations are probably still needed. As previously discussed, metal carbides are ordered structures with isolated carbon atoms (C1) in 10401



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the relative values of DP(1) and DP(2) depend on the metal/ support interaction Eadh. Figure 2 (right-panel) shows that the dominance probability of C1, DP(1) is correlated to the strength of Eadh; and the dominance probability of C2, DP(2), is inversely correlated to the strength of Eadh. This results in C2 being the most probable dominant species for |Eadh| ≤ 0.26, and C1 correspondingly for |Eadh| ≥ 0.43 eV. Notably, for larger nanoparticle sizes, i.e., Ni80 and Ni160, use of a C(2) precursor always results in C1 as the most probable dominant species independently of Eadh (see Figure S3). With allusion to kinetic factors, the previously discussed trends (Figure 2) can be explained on the basis of the carbon species that is generated during the catalysis of a C(1) or C(2) precursor. As discussed in section 2, the catalysis of C(1) precursor gas generates a monomer C1 on the surface, whereas a C(2) precursor generates a dimer C2 on the surface. Carbon atoms in monomer form only need to overcome the bulkdiffusion energy barrier to dissolve, whereas (to dissolve) carbon atoms in dimers have to overcome the dimer-splitting energy barrier (reported as 1.47 eV28) before overcoming the bulk-diffusion energy barrier. Therefore, the dissolution process can be expected to occur more rapidly in the C(1) precursor case, which seemingly facilitates the association of carbon within the nanoparticle. This explains the frequent dominance of carbon species larger than C1 (especially the dimer C2) when the said precursor type is used. The hypothesis that faster dissolution favors carbon association within the nanoparticle, while slow dissolution hinders it, can also be used to explain the trends observed with Eadh. Weak metal/support interactions (here |Eadh| ≤ 0.26 eV)

Figure 2. Representative empirical dominance probability (%) of species Cn within the nickel nanoparticle (DP(n)) calculated for catalytic decomposition of either a C(1)- or C(2)-type precursor varying the strength of the metal/support interaction (Eadh). Results shown correspond to Ni32, and Pgas = 11 atm. DP(n) corresponds to the ratio between the absolute f requency at which Cn is found to be the most abundant carbon species within the nanoparticle and the total number of observations.

precursors that are not captured in the statistical analysis presented in Figure 1. For Ni32, Figure 2 shows that carbon dimers C2 (red bar) have the highest DP when a C(1) precursor is used (independently of Eadh). Furthermore, larger species such as C3, C4, and C5 have a moderate probability of being the dominant species. On the other hand, when a C(2) precursor is used, these larger species (C3, C4, and C5) have a rather negligible probability of being the dominant species. Therefore, the dominant species tend to be the short C1 or C2 species. For this combination of particle size and precursor gas,

Figure 3. (a) Sequence demonstrating the typical precursor catalysis and subsequent carbon dissolution with a C(l) precursor gas. (b) Sequence demonstrating the typical precursor catalysis and subsequent carbon dissolution with a C(2) precursor gas. For (a) and (b), nickel and support atoms are colored blue and black. Carbon atoms are gray, but those of interest are colored in red. (c) Representative dissolution (carbon inside nanoparticle) and growth (carbon outside nanoparticle) curves for a C(1) precursor (top) and a C(2) precursor (bottom). (d) Bar diagram showing the catalysis rate (Rc) during the nucleation stage for simulated nanotube growth on Ni32, using either C(1) or C(2) precursor gas. (e) Bar diagram showing the catalysis rate (Rc) during the growth stage for simulated nanotube growth on Ni32, using either C(1) or C(2) precursor gas. (f) Bar diagram showing the ratio of carbon outside the nanoparticle to carbon dissolved at the moment of nanoparticle saturation during simulated nanotube growth on Ni32 using either C(1) or C(2) precursor gas. (Additional bar diagrams for other particle sizes can be found in Supporting Information Figure S4). 10402



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increase the mobility of the nanocatalyst atoms, which facilitates the splitting of dimers (and other species as discussed in ref 5). This increases the number of monomers C1 on the surface and speeds up carbon dissolution relative to cases with strong metal/support interaction (here |Eadh| ≥ 0.43 eV). Accordingly, the combined factors of precursor type and metal/support interaction can explain the relative abundances (DP) of C1 and C2 observed in Figure 2 for growth on Ni32 using the C(2) precursor. Namely, higher DP for monomers |Eadh| ≥ 0.43 eV, and higher DP for dimers when |Eadh| ≤ 0.26 eV are found, respectively. Furthermore, using a C(1) precursor gas on Ni80 with a weak (−0.16 eV) Eadh (faster dissolution), C3 has the highest DP; whereas with a strong (−0.26 eV) Eadh (slower dissolution) C1 has the highest DP (Figure S1). (In all other C(1) precursor cases, C2 has the highest DP.) Now we proceed to explore further the hypothesis of a slower dissolution rate when a C(2) precursor is used. The purpose of Figure 3a,b is to illustrate typical differences in the timeframes of dissolution between C(1) and C(2) precursor cases. Particularly, Figure 3a,b shows the sequence of events leading to the dissolution of the carbon atoms generated upon the f irst catalytic event of a precursor molecule (Ni32, Eadh = −0.26 eV). At such moment, the nanoparticle carbon content is zero, and the dissolution driving force is at its maximum. For the C(1) precursor case, the carbon atom generated (red) readily accommodates on a (111) surface hollow site. Then, the (111) adsorption site changed to a (100) conf iguration preceding the dissolution of the carbon atom. All these events occurred in a time frame of ∼10.0 ps, and the carbon atom is not found to diffuse on the nanoparticle surface. For the C(2) precursor case, the generated dimer (red) readily accommodates so the two carbon atoms occupy (111) hollow sites. The dimer ‘diffuses’ around the surface for 19.0 ps, and accommodates on a new site again with the carbon atoms occupying (111) hollow sites. Then, the dimer splits into two carbon atoms. Interestingly, once this happens, each of the (111) adsorption sites changes to a (100) conf iguration preceding carbon dissolution. All these events occurred in a time frame of ∼30.0 ps (three-times as slow as for the C(1) case). Whereas quantitatively typical timeframes may change, the described comparative behavior between carbon generated via C(1)- and C(2)-precursor decomposition was maintained throughout induction/nucleation (i.e., dissolution occurring more slowly in C(2)-precursor cases). It is noteworthy that the carbon dimer being able to diffuse along the nanoparticle surface before splitting agrees well with the relative DFTestimated barrier for dimer surface diffusion (∼0.5 eV)29 and dimer splitting (1.47 eV).28 Also, the change of the nanoparticle surface from (111) to (100) preceding dissolution events may facilitate carbon dissolution since the dissolution energy barriers on the former have been experimentally estimated45 in 1.92 eV on (111) and 1.45 eV on (100). This also agrees with the slower dissolution rate observed when metal/support interactions are strong (|Eadh| ≥ 0.47 eV), because reduced atom mobility hinders (or slows down) surface reconstruction/ evolution. The competition between surface diffusion and dissolution ultimately affects the relative rates of dissolution and nucleation. The latter refers to formation of carbon structures (eventually carbon cap/nanotube) on the nanoparticle surface. Indeed, if dissolution is hindered, nucleation is expected to outpace dissolution. Accordingly, we investigate how this

competition is affected by using either a C(1)- or C(2)precursor. Figure 3c shows nucleation/growth curves (red ‘nondissolved carbon vs time’ curve) and dissolution curves (blue ‘dissolved carbon vs time’ curve) for the cases presented in Figure 3a,b (Ni32, Eadh = −0.26 eV using either a C(1)- or C(2)-precursor). It is clear from Figure 3c that use of the C(2) precursor makes nucleation outpace dissolution. With the C(1) precursor, the growth curve runs below the dissolution curve until the time of saturation. At that moment, there are 12 carbon atoms dissolved, and 9 atoms on the nanoparticle surface, which corresponds to a Cout/Cin ratio of 0.75. With the C(2) precursor, the growth curve runs above the dissolution curve, which corresponds to a Cout/Cin of 3.60. Thus the Cout/ Cin ratio at the time of saturation reflects the relative behavior of the growth and dissolution curves. Based on this observation, we show the Cout/Cin ratios (at the time of saturation) for all other Ni32 cases in Figure 3f to illustrate how the behavior of the growth and dissolution curves is affected by metal/support interaction Eadh, precursor type, and gas pressure. Since a catalytic event with the C(2) precursor produces twice as many carbon atoms as one with the C(1) precursor, we also performed simulations with the former, but using a gas pressure reduced in half (i.e., Pgas ∼ 5.5 atm). This setup attempts to reduce differences in the carbon catalysis rate Rc (C atoms/ps), and (approximately) consider only the effect of a different carbon species being generated on the surface. In discussing Figure 2, we argued that increasing the strength of the metal support interaction Eadh, the dissolution rate decreases. This is illustrated by the increase in the Cout/Cin ratio with the strength of Eadh (for all cases) observed in Figure 3f. The observed trend for the Cout/Cin ratio also illustrates how increases in Eadh strength favor nucleation over dissolution. Similarly, it is clear that use of a C(2) precursor (for both gas pressures tested) also increases the preference of nucleation over dissolution. For this precursor type, the Cout/Cin ratio is always larger than 1.0; more markedly so when the gas pressure Pgas is ∼11.0 atm instead of ∼5.5 atm. The latter observation demonstrates that increasing the catalysis rate favors nucleation over dissolution as well (see Figure 3d). Nevertheless, too large catalysis rates may lead to nanocatalyst encapsulation.33 From the above-described trends, it is apparent that nucleation is favored when carbon atoms can stay on the surface long enough to find and bond other carbon atoms. Increasing the gas pressure (hence, catalysis rate RC) increases the amount of carbon on the surface and the probability of carbon atoms finding each other during their characteristic surface residence time τs. On the other hand, increasing the strength of Eadh, or using a C(2) precursor (which generates dimers C2), increases τs. Regarding the latter, notice that the sole use of a C(2) precursor increases the Cout/Cin ratio. In particular, Figure 3f shows that the Cout/Cin ratio with the C(2) precursor at Pgas ∼5.5 atm is larger than with the C(1) precursor at Pgas ∼11.0 atm, despite the latter resulting in higher catalytic rates RC during nucleation as illustrated in Figure 3d. This further supports the idea of an inherently higher τs when a C(2) precursor is used. It must be noted that the use of a C(2) precursor favors nucleation and reduces carbon association within the nanoparticle, but also increases the risk of encapsulation as illustrated by the case Ni32 with Eadh = −0.16 eV. Encapsulation has been normally ascribed to excessively high catalytic rates. However, for the latter case, the use of C(2) precursor at Pgas ∼ 5.5 atm resulted in encapsulation (as evidenced by the negligible RC shown in 10403



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Figure 4. Representative sequential incorporation (panels a through h) of carbon dimers (generated through catalysis of a C(2) precursor) into the nascent nanotube structure. Each carbon dimer is identified with a color. Nickel atoms are blue, “regular” carbon atoms are gray, and support atoms are black.

rim. Figure 4 shows the behavior of twelve consecutively generated C2 dimers through catalysis of a C(2) precursor on Ni160 illustrating the typical behavior of C2 dimers. Each of these twelve dimers is identified with a color, with the carbon atoms retaining the color of the parental dimer. Panels a−h capture the incorporation of eight of these dimers to the rim, with the dimer most recently incorporated to the rim encircled. In some cases, the addition of a dimer does complete a hexagonal ring as illustrated in panels g and h, whereas in other cases the addition of the dimer creates a dangling chain (panels a, c, and e), or completes a nonhexagonal ring. Due to the continuous healing of the nanotube rim, the two atoms originally forming the dimer usually ‘split’ once they incorporate into the rim. Therefore, when the addition of the dimer creates a relatively stable local rim structure, the dimer atoms have a higher probability of remaining bonded (at least, for a longer time). Instances of relatively stable local structures are the completion of hexagonal rings by the green dimer in panel d, red and cyan dimers in panel g, and gray dimer in panel h. These dimers can still remain unbroken in the respective subsequent panel. On the other hand, other dimers seem to split rather easily once incorporated into the rim to facilitate healing. For instance, notice in panel g (Figure 4) how annealing led to the formation of concatenated hexagonal rings numbered 1 through 4, but with the orange, pink, and black dimers splitting. None of the 12 followed dimers (i.e., 24 carbon atoms) splits or dissolves before reaching the rim, while at equivalent growth stages, we reported38 that using a C(1) precursor on Ni160, 4 out of 20 carbon atoms dissolved. This agrees with an increase in surface diffusion due to the use of a C(2) precursor. It is apparent that the attack to the rim occurs in stepwise fashion. Therefore, the time it takes for generated carbon to reach the rim τrim is shorter than the time in between catalytic events τC, which for our simulations have been reported38 to be in the range 16.2−61.3 ps. It is noteworthy that a stepwise attack to the rim (hence, stepwise evolution of the nanotube rim) by a selected carbon species is usually considered in theoretical (e.g., DFT) investigations of the growth mechanism, and its implications on chirality selection.18,46,47,49,50 The typically irregular metal/carbon interface observed in the

Figure 3e) despite the lowest RC during nucleation (Figure 3d). This demonstrates the need to fine-tune reaction conditions for a given precursor to improve growth. 3.2. Precursor Effects on Carbon Transport, Nanotube Quality, and Chirality. 3.2.1. Carbon Transport and Growth Mechanism. We have reported38 that using a C(1) precursor the contribution of surface diffusion to nanotube growth gradually takes over that of bulk-diffusion as growth progresses. This happens due to a decrease in carbon dissolution driving forces once the nanocatalyst is saturated. The dominance of surface diffusion was less pronounced when the nanoparticle size was increased (i.e., Ni32 and Ni160). We have shown in Figure 3 that use of a C(2) precursor slows down carbon dissolution. Thus, expectedly, the dominance of surface dif f usion increases with the use of a C(2) precursor. As discussed in section 3.1.2, the relative energy barriers for C2 splitting and diffusion (1.47 eV28 and 0.50,29 respectively) allow the dimer to diffuse along the particle surface, before splitting. On a clean nanoparticle surface, the dimer unavoidably splits (and dissolves). Therefore, during induction/nucleation a number of dimers are observed to split. However, during the growth stage, generated C2 dimers seldom split or dissolve before incorporating into the rim of the nascent nanotube structure. The explanation for this is that the characteristic surface residence time for the dimer τs (section 3.1.2) is larger than the time needed for the generated dimer to reach the nanotube rim τrim. This occurs because (unless the support participates in the catalytic process) the nanocatalyst atoms provide support for the growing nanotube structure as well as sites for catalysis. Therefore, once a cap/nanotube is formed, the generation of the C2 dimer occurs close enough to the rim (at least with nanoparticle sizes used in this work), so τrim < τs. Thus, typically the dimer survives long enough to reach the nanotube rim without splitting. This observation agrees with a growth mechanism based on the systematic addition/attack of carbon dimers to the nanotube rim.18,46,47 However, the rim structure does not look as regular as depicted in some other work.18,46−49 Typically, formation of hexagonal rings occurs through continuous healing of the defective metal/carbon interface, and not necessarily directly from the addition of a dimer to the 10404



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Figure 5. Representative plots showing the number of hexagons formed vs the number of carbon atoms outside the nanoparticle for Ni32, Ni80, and Ni160.

Table 1. Information Regarding the Observation (O) or Not (X) of ‘Bamboo’ Growth for Simulated Growth on Nickel Nanoparticles of Varying Size and Interaction with the Support Eadha −Eadh (eV) Ni80

Ni32 A B C a

Ni160

0.16

0.26

0.43

0.70

1.39

0.16

0.26

0.43

0.70

1.39

0.16

0.26

0.43

0.70

1.39

X X X

X X X

O X X

O O O

O O O

X X X

X X X

X X X

O O O

O O O

X X X

X X X

X X X

X X X

O O X

Conditions: A = [C(1), Pgas = 11 atm]; B = [C(2), Pgas = 5 atm]; C = [C(2), Pgas = 11 atm.

that selection of the precursor may affect nanotube quality for a given system. Figure 5-left shows for a Ni32 case that the use of C(2) precursor results in the formation of an average of 26.5 hexagonal rings when there are 150 atoms in the nanotube, whereas the use of C(1) precursor results in the formation of an average of 20.1 hexagonal rings. For a Ni80 case (Figure 5center), use of C(2) precursor results in an average of 49.6 hexagonal rings when there are 200 atoms in the nanotube, whereas the use of C(1) precursor results in the formation of an average of 35.4 hexagonal rings. As mentioned above, the trend is reversed for Ni160 (Figure 5-right). The use of C(2) precursor results in the formation of an average of 42.9 hexagonal rings when there are 250 atoms in the nanotube, whereas the use of C(1) precursor results in the formation of an average of 54.1 hexagonal rings. It has been noted that the catalytic or carbon feeding rate RC impacts healing, and thus nanotube quality.52,53 As previously discussed, nanotube defects, and the quality ratio of 50−70% of hexagonal rings has been ascribed to accelerated growth. However, the determined efficiencies in hexagonal ring formation are somewhat independent of RC. For instance, on Ni32 or Ni80 similar efficiencies are found with the C(2) precursor with Pgas ∼11.0 atm and ∼5 atm. Furthermore, the C(2) precursor at Pgas ∼11 atm was more efficient than the C(1) precursor at Pgas ∼ 11 atm, although Figure 3e shows that the former almost doubles up the RC of the latter. Considering all cases, no clear correlation between RC values in Figure 3e and efficiencies for formation of hexagonal was found. Therefore, the estimated differences must be due solely to the type of precursor used, which as previously stated may alter the kinetics of reaction/healing pathways. In addition to the formation of heptagonal and pentagonal rings, another type of defect found in some structures is the formation of a transverse carbon membrane in growing nanotube, or ‘bamboo growth’. Table 1 reports for which cases this type of structure was observed (indicated by ‘O’). It is

simulations reported here may occur due to the accelerated simulation scheme (f = 1.0, as discussed in section 1), which enables nanotube growth in reasonable simulation times. Therefore, it is possible that in actual synthesis, carbon species attack a more regular rim structure than the ones reported here. On the other hand, the interface may indeed not be as regular as usually modeled, so the ability to heal/stabilize into a defined chirality may play a role in chirality selection, as reported by Morokuma et al.51 3.2.2. Nanotube Quality and Growth Mechanism. Whether new hexagonal rings are formed through a stepwise attack of carbon species to a regular rim structure, or through healing of a defective metal/carbon interface, it is plausible that depending on which species (i.e., C1, or C2) is systematically incorporated into the growing nanotube, the kinetics of the reaction/healing pathway may be distinctly altered. In our simulations, hexagonal, pentagonal, and heptagonal rings dominate the resulting nanotube structures, with hexagonal rings constituting between 50% and 70% of the total number of rings. Here, we investigate whether the use of a C(1)- or C(2)precursor affects the effectiveness with which hexagonal rings are formed, which can be related to nanotube quality. Figure 5 shows representative plots relating the number of hexagonal rings counted in the nanotube structure with the current number of carbon atoms in the nanotube (estimated by the count of nondissolved carbon atoms). Notice that the number of carbon atoms in the nanotube is a measure of growth progress. However, due to continuous rearrangement/ healing, a particular number n of carbon atoms can be associated with different numbers of hexagonal rings. The plots show that using a C(2) precursor (red and green curves) results in more efficient formation of hexagonal rings on Ni32 or Ni80 nanoparticles. However, in Ni160 nanoparticles, such hexagonal ring formation is more efficient using a C(1) precursor. Although C(1) and C(2) precursor decomposition is represented here in a simplified manner, our results suggests 10405



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contact. This may lead to an inward bending of part of the nanotube wall (Figure 6a,b center-left). Further precursor decomposition around the bent region reconstitutes the nanotube wall and the nanotube/nanoparticle contact. However, the region originally bent inward is maintained and keeps growing sustained by bulk-diffusion (Figure 6a,b center-right). Eventually, this secondary structure evolves into a transverse carbon membrane (Figure 6a,b right). The use of a C(2) precursor tends to increase the carbon coverage of the nanoparticle (as discussed explaining nanocatalyst encapsulation at the end of section 3.1.2). Expectedly, this improves the nanotube/nanoparticle contact with respect to a C(1) precursor, and prevents the inward bending that eventually leads to ‘bamboo’ structures. Thus, for instance, bamboo growth occurs with the use of a C(1) precursor on a Ni32 particle with Eadh = −0.43 eV, but it does not when a C(2) precursor is used instead. Similarly, since a higher RC is expected to increase carbon coverage, it is interesting to note that bamboo growth occurs with the use of a C(2) precursor at Pgas ∼ 5.5 atm on a Ni160 particle with Eadh = −1.39 eV, but it does not when Pgas is increased to 11.0 atm. It is noteworthy that we have observed a similar mechanism in a number of additional simulations resulting in horizontal nanotube growth, wherein we observed similar transverse membranes in horizontally aligned nanotubes. Figure 6c schematizes the observed mechanism. The mechanism is similar to the one schematized in Figure 6b, but with (nanotube wall) growth continuing at the opposite side of the inward bending, and leading to an overall tilting of the growing structure (Figure 6c center-right). This tilting reduces the accessibility of the precursor gas to reconstitute the nanotube wall in the bent region, but favors the growth of the nanotube on the opposite site, further increasing nanotube tilting (Figure 6c right). It is plausible that strong enough support/nanotube (SCSWCNT) interactions are necessary to maintain the horizontal tilting of the nanotube as growth progresses. On the other hand, since the type of precursor may affect the probability of bamboo growth, it may similarly affect the probability of horizontal growth. 3.2.3. Nanotube Chirality. We now use the chiral angle analysis strategy shown in previous work5 to analyze potential effects of the precursor gas on the nanotube chirality, and summarize our results in Figure 7. Given the quality ratio of 50−70%, we only determine whether the chirality is larger than 15° (near-zigzag, red cells) or smaller than 15° (near-armchair,

apparent that three factors increase the probability of bamboo growth: (1) using a C(1) precursor, (2) reducing nanocatalyst size, and (3) increasing the metal support interaction strength Eadh. These three enumerated factors have in common that they reduce the nanotube/nanocatalyst contact. Regarding the latter point, elsewhere39 we have reported that supported nickel nanoparticles have a viscous−solid character, with the solid character increasing with Eadh. Thus, the observed trend with Eadh is consistent with reported connections between ‘bamboo growth’ and solid nanoparticles.26 Figure 6a,b schematizes the mechanism of formation of the transverse ‘carbon membrane’. Initially, the nanotube is

Figure 6. (a,b) Snapshots and schematics of simulated nanotube growth on a Ni32 nanoparticle (C(1) precursor, Eadh = −0.20 eV) as an example of ‘bamboo’ growth and its mechanism. (c) Related growth mechanism for ‘horizontal’ growth observed in simulated nanotube growth.

supported on the nanoparticle with a good nanotube/ nanoparticle contact (Figure 6a,b left). As growth progresses, the nanotube tries to lift off from the nanoparticle. If the nanoparticle is viscous enough (which is determined by Eadh), it tends to elongate maintaining a good nanotube/nanoparticle contact. However, if the metal/support interaction Eadh is strong enough, the nanoparticle tends to resist elongation, which results in detriment of the nanotube/nanoparticle

Figure 7. Chiral angle analysis after 5.0 ns of simulated growth for different combinations of particle size, interaction with the support, precursor gas type, and gas pressure. Diagonally split cells correspond to cases where the top and bottom parts show distinct chiralities (either a change in chiral angle or diameter). Snapshot of final configuration for “Ni32: Eadh = −0.26 eV: C(2): Pgas = 11 atm” shows an example of the latter with atoms used for chiral angle analysis highlighted in yellow (near armchair section) and red (near zigzag section). 10406



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between catalytic events (τC = 16.2−61.3 ps), thus growth occurs through a stepwise systematic attack of carbon dimers (or carbon monomers in the C(1)-precursor cases) to the rim. This seems to alter the kinetics of healing/reaction pathways resulting in precursor-dependent effects on formation of hexagonal rings, occurrence of bamboo growth, and chirality. Formation of hexagonal rings (an indication of nanotube quality) was found more efficient with decomposition of the C(2) precursor on Ni32 (d = 1.0−1.3 nm) and Ni80 (d = 1.3− 1.6 nm) than with that of the C(1) precursor on Ni160 (d = 1.6−2.0 nm). While occurrence of bamboo growth was strongly connected to the value of metal/support interaction Eadh, use of the C(2) precursor gas seemed to reduce the probability of bamboo growth due to increased carbon coverage, which helps maintain nanotube/nanoparticle contact. Finally, a moderate bias toward near-armchair chiralities was observed with the use of the C(2) precursor (in comparison to the C(1)-precursor cases), encouraging further investigation of the role of the precursor on chirality.

blue cells). A more quantitative approach such as the local chirality proposed by Morokuma et al.54 was not used here due to irregularities in the grown nanotubes that hinder a confident determination of a unique axial direction. Only one simulation is performed for each combination of particle size, interaction with the support, precursor gas type, and gas pressure. Notably, some simulations resulted in rather defined zones with different chiralities (i.e., either change in diameter or change in chiral angle): one at the tip half of the nanotube, and one at the bottom half of the nanotube (Figure 7 and Figure S5). These cases typically occurring with the C(2) precursor are represented with diagonally split cells in Figure 7. Inspection of the simulation trajectories reveal that these changes correlate well with drastic changes in the nanoparticle in the middle of growth, such as sudden increases in support wetting (hence, diameter). For instance, in Figure 7 the tip part of the nanotube is of smaller diameter than the bottom part. The diameter of the nanoparticle was smaller during formation of the tip part (t < 1.5 ns), the nanoparticle then increased its diameter (t ∼ 2.5 ns) so newly catalyzed carbon atoms assembled into a larger diameter nanotube section (see Figure S6). This behavior agrees well with the proposed influence of nanoparticle structure on diameter and chirality. Considering all cases, ∼70% of the cases with comparatively weak interaction with the support (|Eadh| ≤ 0.43 eV) resulted in near-armchair tubes, and ∼55% of the cases with comparatively strong interaction with the support (|Eadh| ≥ 0.70 eV) resulted in near-zigzag tubes. This agrees qualitatively with trends previously reported.5 On the other hand, only considering C(2) precursor cases ∼77% of the weak interaction cases resulted in near-armchair tubes, and ∼50% of the strong interaction cases resulted in near-zigzag tubes. This demonstrates a moderate increase in the tendency to form near-armchair tubes with the use of the C(2) precursor. The exact mechanism by which this occurs is not clear, thus encouraging further exploration about how different carbon species may affect chirality selection.

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ASSOCIATED CONTENT

S Supporting Information *

Figure S1 illustrates a dominance probability (%) of species Cn within the nickel nanoparticle calculated for catalytic decomposition of either a C(1)- or C(2)-type precursor varying the strength of the metal/support interaction. Bar diagrams showing the catalysis rate (Rc) during the nucleation and growth stages for simulated nanotube growth (Figure S2). Figure S3 displays representative dominance probabilities (%) of species Cn within the nickel nanoparticle calculated for catalytic decomposition of either a C(1)- or C(2)-type precursor varying the strength of the metal/support interaction. Bar diagrams showing the catalysis rate during nucleation, growth, and at saturation for simulated nanotube growth on Ni80 and Ni160, using either C(1) or C(2) precursor gas (Figure S4). Snapshots illustrating determination of chirality (Figure S5) and exemplifying changes in nanotube diameter (Figure S6). Discussion on the application of a regular solution model applied to an interstitial solution. This material is available free of charge via the Internet at http://pubs.acs.org.

4. CONCLUSIONS Reactive molecular dynamics (RMD) simulations of nanotube growth on supported nickel nanoparticles varying size and metal/support interaction are used to characterize the precursor decomposition based on either a C(1) precursor or a C(2) precursor. We focused on the effect of precursor type on (1) carbon association within the nanoparticle (relevant to carbide formation), (2) nanotube quality, and (3) nanotube chirality, and these points are discussed from a perspective based on the growth mechanism. Significant carbon association was observed within the nanoparticles, with the dominant species being affected by the choice of precursor gas. Carbon association within the nanoparticle was ascribed to kinetic rather than thermodynamic factors. In comparison to use of a C(1) precursor, use of a C(2) precursor slowed down carbon dissolution, in turn reducing association of dissolved carbon, accelerating nucleation, increasing surface diffusion, and potentially increasing the risk of nanocatalyst encapsulation even at similar carbon production rates (controlled here with Pgas) than a C(1) precursor. In the growth stage, carbon dimers generated through decomposition of C(2) precursor gas diffused along the nanoparticle surface, and reached the nanotube without previously splitting. The typical time needed for the dimer to diffuse from the generation (catalysis) site to the rim (τrim ≤ 20.0 ps) was determined to be shorter than the typical time

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AUTHOR INFORMATION

Corresponding Author

*E-mail: [email protected]. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS Financial support from the Department of Energy, Basic Energy Sciences, grant DE-FG02-06ER15836, is gratefully acknowledged. This research used computational resources of the National Energy Research Scientific Computing Center, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC03-76SF00098, and from TAMU Supercomputer Center, and Brazos HPC Cluster at Texas A&M University.

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REFERENCES

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