Why Carbon Nanotubes Grow

Acetylene and Ethylene Adsorption during Floating Fe Catalyst Formation at the Onset of Carbon Nanotube Growth and the Effect of Sulfur Poisoning: a DFT Study

Here, we investigated the adsorption of acetylene and ethylene on iron clusters and nanoparticles, which is a crucial aspect in the nascent phase of carbon nanotube growth by floating catalyst chemical vapor deposition (FCCVD). The effect of sulfur on adsorption was also studied due to its indispensable role in the process and its commonly known impact on metal catalyst poisoning. We performed systematic density functional theory (DFT) computations, considering numerous adsorption configurations and iron particles of various sizes (Fen, n = 3-10, 13, 55). We found that acetylene binds significantly more strongly than ethylene and prefers different adsorption sites. The presence of sulfur decreased the adsorption strength only in the immediate proximity of the adsorbate, suggesting that the effect of sulfur is mainly of steric origin while electronic effects play only a minor role. Higher sulfur coverage of the catalyst surface significantly weakened the binding of acetylene or ethylene. To further investigate this interaction, Bader's atoms in molecules (AIM) analysis and charge density difference (CDD) were used, which showed electron transfer from iron clusters or nanoparticles to the adsorbate molecules. The charge transfer exhibited a decreasing trend as sulfur coverage increased. These results can also contribute to the understanding of other iron-based catalytic processes involving hydrocarbons and sulfur, such as the Fischer-Tropsch synthesis.

Dynamics of growing carbon nanotube interfaces probed by machine learning-enabled molecular simulations

Carbon nanotubes (CNTs), hollow cylinders of carbon, hold great promise for advanced technologies, provided their structure remains uniform throughout their length. Their growth takes place at high temperatures across a tube-catalyst interface. Structural defects formed during growth alter CNT properties. These defects are believed to form and heal at the tube-catalyst interface but an understanding of these mechanisms at the atomic-level is lacking. Here we present DeepCNT-22, a machine learning force field (MLFF) to drive molecular dynamics simulations through which we unveil the mechanisms of CNT formation, from nucleation to growth including defect formation and healing. We find the tube-catalyst interface to be highly dynamic, with large fluctuations in the chiral structure of the CNT-edge. This does not support continuous spiral growth as a general mechanism, instead, at these growth conditions, the growing tube edge exhibits significant configurational entropy. We demonstrate that defects form stochastically at the tube-catalyst interface, but under low growth rates and high temperatures, these heal before becoming incorporated in the tube wall, allowing CNTs to grow defect-free to seemingly unlimited lengths. These insights, not readily available through experiments, demonstrate the remarkable power of MLFF-driven simulations and fill long-standing gaps in our understanding of CNT growth mechanisms.

Growth of Single Crystalline 2D Materials beyond Graphene on Non-metallic Substrates

The advent of 2D materials has ushered in the exploration of their synthesis, characterization and application. While plenty of 2D materials have been synthesized on various metallic substrates, interfacial interaction significantly affects their intrinsic electronic properties. Additionally, the complex transfer process presents further challenges. In this context, experimental efforts are devoted to the direct growth on technologically important semiconductor/insulator substrates. This review aims to uncover the effects of substrate on the growth of 2D materials. The focus is on non-metallic substrate used for epitaxial growth and how this highlights the necessity for phase engineering and advanced characterization at atomic scale. Special attention is paid to monoelemental 2D structures with topological properties. The conclusion is drawn through a discussion of the requirements for integrating 2D materials with current semiconductor-based technology and the unique properties of heterostructures based on 2D materials. Overall, this review describes how 2D materials can be fabricated directly on non-metallic substrates and the exploration of growth mechanism at atomic scale.

Synthesis and Characterization of SiO2-Based Graphene Nanoballs Using Copper-Vapor-Assisted APCVD for Thermoelectric Application

This study describes a method by which to synthesize SiO2-based graphene nanoballs (SGB) using atmospheric pressure chemical vapor deposition (APCVD) with copper vapor assistance. This method should solve the contamination, damage, and high costs associated with silica-based indirect graphene synthesis. The SGB was synthesized using APCVD, which was optimized using the Taguchi method. Multiple synthesis factors were optimized and investigated to find the ideal synthesis condition to grow SGB for thermoelectric (TE) applications. Raman spectra and FESEM-EDX reveal that the graphene formed on the silicon nanoparticles (SNP) is free from copper. The prepared SGB has excellent electrical conductivity (75.0 S/cm), which shows better results than the previous report. Furthermore, the SGB nanofillers in bismuth telluride (Bi2Te3) nanocomposites as TE materials exhibit a significant increment in Seebeck coefficients (S) compared to the pure Bi2Te3 sample from 109 to 170 μV/K at 400 K, as well as electrical resistivity decrement. This approach would offer a simple strategy to improve the TE performance of commercially available TE materials, which is critical for large-scale industrial applications.

Addressing the Trade-Off between Crystallinity and Yield in Single-Walled Carbon Nanotube Forest Synthesis Using Machine Learning

Synthetic trade-offs exist in the synthesis of single-walled carbon nanotube (SWCNT) forests, as growing certain desired properties can often come at the expense of other desirable characteristics such as the case of crystallinity and growth efficiency. Simultaneously achieving mutually exclusive properties in the growth of SWCNT forests is a significant accomplishment, as it requires overcoming these trade-offs and balancing competing mechanisms. To address this, we trained a machine-learning regression model with a set of 585 "real" experimental synthesis data, which were taken using an automatic synthesis reactor. Subsequently, 16000 exploratory "virtual" experiments were performed by our trained model to examine potential routes toward addressing the current crystallinity-height trade-off limitation, and suggestions on growth conditions were predicted. Importantly, additional validation using "real" experimental syntheses showed good agreement with the predictions as well as a 48% increase in growth efficiency while maintaining the high crystallinity (G/D-ratio). This highlighted the effectiveness and accuracy of the predictive capability of our machine-learning model, which achieved improved results in less than 50 validation tests. Furthermore, the trained model revealed the surprising importance of the nature of the carbon feedstock, particularly the reactivity and concentration, as a route for overcoming the trade-off between the SWCNT crystallinity and growth efficiency. These results of the high-efficiency synthesis of highly crystalline SWCNT forests represent a significant advance in overcoming synthetic trade-off barriers for complex multivariable systems.

Extending conducting channels in Fe-N-C by interfacial growth of CNTs with minimal metal loss for efficient ORR electrocatalysis

Achieving a high electrocatalytic performance using a completely metal-free electrocatalyst, preferably based on only carbonaceous materials, remains a challenge. Alternatively, an efficient composite of a carbon nanostructure and a non-noble metal with minimum dependence on a metal holds immense potential. Although single-atom catalysis brings superior performance, its complex synthetic strategy limits its large-scale implementation. Previous investigation has shown that atomic dispersion (Fe-Nx-C) is accompanied by higher metal-loss compared to nanoparticle formation (Fe-NPs-N-C). Therefore, to achieve minimum metal loss, we first incorporated iron nanoparticles (Fe NPs) to N-doped carbon (N-C) and then exposed them to a cheap carbon source, melamine at high temperature, resulting in the growth of carbon nanotubes (CNTs) catalysed by those Fe NPs loaded on N-C (Fe-NPs-N-C). Thermogravimetric analysis showed that the metal-retention in the composite is higher than that in the bare carbon nanotube and even the atomically dispersed Fe-active sites on N-C. The composite material (Fe-NPs-N-C/CNT) shows a high half-wave potential (0.89 V vs. RHE) which is superior to that of commercial Pt/C towards the oxygen reduction reaction (ORR). The enhanced activity is attributed to the synergistic effect of high conductivity of CNTs and active Fe-sites as the composite exceeds the individual electrocatalytic performance shown by Fe-CNTs & Fe-NPs-N-C, and even that of atomically dispersed Fe-active sites on N-C.

Thermal Expansion and Thermal Conductivity of Ni/Graphene Composite: Molecular Dynamics Simulation

In the present work, the thermal conductivity and thermal expansion coefficients of a new morphology of Ni/graphene composites are studied by molecular dynamics. The matrix of the considered composite is crumpled graphene, which is composed of crumpled graphene flakes of 2-4 nm size connected by van der Waals force. Pores of the crumpled graphene matrix were filled with small Ni nanoparticles. Three composite structures with different sizes of Ni nanoparticles (or different Ni content-8, 16, and 24 at.% Ni) were considered. The thermal conductivity of Ni/graphene composite was associated with the formation of a crumpled graphene structure (with a high density of wrinkles) during the composite fabrication and with the formation of a contact boundary between the Ni and graphene network. It was found that, the greater the Ni content in the composite, the higher the thermal conductivity. For example, at 300 K, λ = 40 W/(mK) for 8 at.% Ni, λ = 50 W/(mK) for 16 at.% Ni, and λ = 60 W/(mK) for 24 at.% Ni. However, it was shown that thermal conductivity slightly depends on the temperature in a range between 100 and 600 K. The increase in the thermal expansion coefficient from 5 × 10-6 K-1, with an increase in the Ni content, to 8 × 10-6 K-1 is explained by the fact that pure Ni has high thermal conductivity. The results obtained on thermal properties combined with the high mechanical properties of Ni/graphene composites allow us to predict its application for the fabrication of new flexible electronics, supercapacitors, and Li-ion batteries.

Bioinspired inhibition of aggregation in metal-organic frameworks (MOFs)

Different from traditional procedures of using solid stabilizers like polymers and surfactants, here we demonstrate that water, as a very "soft" matter, could function as a "spacer" to prevent the aggregation of metal-organic frameworks (MOFs) in aqueous dispersions. Our theoretical calculations reveal in case of an excess of positively charged metal nodes of MOFs, where water molecules are ligated to metal nodes that greatly enhance MOFs' solution dispersibility through electrostatic stabilization. This discovery has motivated us to develop a facile experimental approach for producing a category of "clean" MOF dispersions without foreign additives. Potential application has been demonstrated for the size fractionation of MOFs, which results in small-size MOFs (50-80 nm) characteristic of superior electrocatalytic oxygen evolution activities (256 mV at 10 mA cm^-2, Tafel slope of 49 mV dec^-1 and durability >30 h). This work would provide new clues for aqueous processing of MOFs for many emerging applications.

Tunable growth of one-dimensional graphitic materials: graphene nanoribbons, carbon nanotubes, and nanoribbon/nanotube junctions

Graphene nanoribbons (GNRs) and carbon nanotubes (CNTs), two representative one-dimensional (1D) graphitic materials, have attracted tremendous research interests due to their promising applications for future high-performance nanoelectronics. Although various methods have been developed for fabrication of GNRs or CNTs, a unified method allowing controllable synthesis of both of them, as well as their heterojunctions, which could largely benefit their nano-electronic applications, is still lacking. Here, we report on a generic growth of 1D carbon using nanoparticles catalyzed chemical vapor deposition (CVD) on atomically flat hexagonal boron nitride (h-BN) substrates. Relative ratio of the yielded GNRs and CNTs is able to be arbitrarily tuned by varying the growth temperature or feeding gas pressures. The tunability of the generic growth is quantitatively explained by a competing nucleation theory: nucleation into either GNRs or CNTs by the catalysts is determined by the free energy of their formation, which is controlled by the growth conditions. Under the guidance of the theory, we further realized growth of GNR/CNT intramolecular junctions through changing H₂ partial pressure during a single growth process. Our study provides not only a universal and controllable method for growing 1D carbon nanostructures, but also a deep understanding of their growth mechanism, which would largely benefit future carbon-based electronics and optoelectronics.

(n, m) Distribution of Single-Walled Carbon Nanotubes Grown from a Non-Magnetic Palladium Catalyst

Non-magnetic metal nanoparticles have been previously applied for the growth of single-walled carbon nanotubes (SWNTs). However, the activation mechanisms of non-magnetic metal catalysts and chirality distribution of synthesized SWNTs remain unclear. In this work, the activation mechanisms of non-magnetic metal palladium (Pd) particles supported by the magnesia carrier and thermodynamic stabilities of nucleated SWNTs with different (n, m) are evaluated by theoretical simulations. The electronic metal-support interaction between Pd and magnesia upshifts the d-band center of Pd, which promotes the chemisorption and dissociation of carbon precursor molecules on the Pd surface, making the activation of magnesia-supported non-magnetic Pd catalysts for SWNT growth possible. To verify the theoretical results, a porous magnesia supported Pd catalyst is developed for the bulk synthesis of SWNTs by chemical vapor deposition. The chirality distribution of Pd-grown SWNTs is understood by operating both Pd-SWNT interfacial formation energy and SWNT growth kinetics. This work not only helps to gain new insights into the activation of catalysts for growing SWNTs, but also extends the use of non-magnetic metal catalysts for bulk synthesis of SWNTs.

Dual-Silica Template-Mediated Synthesis of Nitrogen-Doped Mesoporous Carbon Nanotubes for Supercapacitor Applications

1D carbon nanotubes have been widely applied in many fields, such as catalysis, sensing and energy storage. However, the long tunnel-like pores and relatively low specific surface area of carbon nanotubes often restrict their performance in certain applications. Herein, a dual-silica template-mediated method to prepare nitrogen-doped mesoporous carbon nanotubes (NMCTs) through co-depositing polydopamine (both carbon and nitrogen precursors) and silica nanoparticles (the porogen for mesopore formation) on a silica nanowire template is proposed. The obtained NMCTs have a hierarchical pore structure of large open mesopores and tubular macropores, a high specific surface area (1037 m² g^-1 ), and homogeneous nitrogen doping. The NMCT-45 (prepared at an interval time of 45 min) shows excellent performance in supercapacitor applications with a high capacitance (373.6 F g^-1 at 1.0 A g-¹ ), excellent rate capability, high energy density (11.6 W h kg^-1 at a power density of 313 W kg^-1 ), and outstanding cycling stability (98.2% capacity retention after 10 000 cycles at 10 A g^-1 ). Owing to the unique tubular morphology, hierarchical porosity and homogeneous N-doping, the NMCT also has tremendous potential in electrochemical catalysis and sensing applications.

Molecular Evolutionary Growth of Ultralong Semiconducting Double-Walled Carbon Nanotubes

The self-assembling preparation accompanied with template auto-catalysis loop and the ability to gather energy, induces the appearance of chirality and entropy reduction in biotic systems. However, an abiotic system with biotic characteristics is of great significance but still missing. Here, it is demonstrated that the molecular evolution is characteristic of ultralong carbon nanotube preparation, revealing the advantage of chiral assembly through template auto-catalysis growth, stepwise-enriched chirality distribution with decreasing entropy, and environmental effects on the evolutionary growth. Specifically, the defective and metallic nanotubes perform inferiority to semiconducting counterparts, among of which the ones with double walls and specific chirality (n, m) are more predominant due to molecular coevolution. An explicit evolutionary trend for tailoring certain layer chirality is presented toward perfect near-(2n, n)-containing semiconducting double-walled nanotubes. These findings extend our conceptual understanding for the template auto-catalysis assembly of abiotic carbon nanotubes, and provide an inspiration for preparing chiral materials with kinetic stability by evolutionary growth.

The promoter role of sulfur in carbon nanotube growth

We investigate the effect of sulfur on the interaction of iron catalyst nanoparticles and carbon nanotubes (CNTs), typically present in a floating catalyst chemical vapor deposition (FCCVD) process. As a reference, the interaction of graphene with the Fe fcc(111) surface is used. In both systems we performed a systematic density functional theory (DFT) study on the interaction with different sulfur contents. We found that the presence of sulfur changes the nature and strength of interaction between graphene and the iron surface from strong chemisorption to weak physisorption. Furthermore, sulfur significantly reduces the CNT-iron binding, indicating a beneficial effect on the CNT growth and its promoter role. We believe that these results induce further experimental studies and optimization of the CNT synthesis process.