Effect of ammonia on chemical vapour deposition and carbon nanotube nucleation mechanisms

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.

Catalytic CVD synthesis of boron nitride and carbon nanomaterials - synergies between experiment and theory

Low-dimensional carbon and boron nitride nanomaterials - hexagonal boron nitride, graphene, boron nitride nanotubes and carbon nanotubes - remain at the forefront of advanced materials research. Catalytic chemical vapour deposition has become an invaluable technique for reliably and cost-effectively synthesising these materials. In this review, we will emphasise how a synergy between experimental and theoretical methods has enhanced the understanding and optimisation of this synthetic technique. This review examines recent advances in the application of CVD to synthesising boron nitride and carbon nanomaterials and highlights where, in many cases, molecular simulations and quantum chemistry have provided key insights complementary to experimental investigation. This synergy is particularly prominent in the field of carbon nanotube and graphene CVD synthesis, and we propose here it will be the key to future advances in optimisation of CVD synthesis of boron nitride nanomaterials, boron nitride - carbon composite materials, and other nanomaterials generally.