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تسجيل مستخدم جديدTheoretical formation of carbon nanomembranes under realistic conditions using classical molecular dynamics
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Carbon nanomembranes made from aromatic precursor molecules are free standing nanometer thin materials of macroscopic lateral dimensions. Although produced in vario
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New forms of carbon-based materials have received great attention, and the developed materials have found many applications in nanotechnology. Interesting novel carbon structures include the carbon peapods, which are comprised of fullerenes encapsula
ted within carbon nanotubes. Peapod-like nanostructures have been successfully synthesized, and have been used in optical modulation devices, transistors, solar cells, and in other devices. However, the mechanical properties of these structures are not completely elucidated. In this work, we investigated, using fully atomistic molecular dynamics simulations, the deformation of carbon peapods under high-strain rate conditions, which are achieved by shooting the peapods at ultrasonic velocities against a rigid substrate. Our results show that carbon peapods experience large deformation at impact, and undergo multiple fracture pathways, depending primarily on the relative orientation between the peapod and the substrate, and the impact velocity. Observed outcomes include fullerene ejection, carbon nanotube fracture, fullerene, and nanotube coalescence, as well as the formation of amorphous carbon structures.
Free-standing carbon nanomembranes (CNM) with molecular thickness and macroscopic size are fascinating objects both for fundamental reasons and for applications in nanotechnology. Although being made from simple and identical precursors their interna
l structure is not fully known and hard to simulate due to the large system size that is necessary to draw definite conclusions. We performed large-scale classical molecular dynamics investigations of biphenyl-based carbon nanomembranes. We show that one-dimensional graphene-like stripes constitute a highly symmetric quasi one-dimensional ground state. This state does not crosslink. Instead crosslinked structures are formed from highly excited precursors with a sufficient amount of broken phenyls. The internal structure of CNM is very likely a disordered metastable state which is formed in the process of cooling.
The heat flux autocorrelation functions of carbon nanotubes (CNTs) with different radius and lengths is calculated using equilibrium molecular dynamics. The thermal conductance of CNTs is also calculated using the Green-Kubo relation from the linear
response theory. By pointing out the ambiguity in the cross section definition of single wall CNTs, we use the thermal conductance instead of conductivity in calculations and discussions. We find that the thermal conductance of CNTs diverges with the CNT length. After the analysis of vibrational density of states, it can be concluded that more low frequency vibration modes exist in longer CNTs, and they effectively contribute to the divergence of thermal conductance.
By using molecular dynamics simulation, formation mechanisms of amorphous carbon in particular sp${}^3$ rich structure was researched. The problem that reactive empirical bond order potential cannot represent amorphous carbon properly was cleared in
the transition process from graphite to diamond by high pressure and the deposition process of amorphous carbon thin films. Moreover, the new potential model which is based on electron distribution simplified as a point charge was developed by using downfolding method. As a result, the molecular dynamics simulation with the new potential could demonstrate the transition from graphite to diamond at the pressure of 15 GPa corresponding to experiment and the deposition of sp${}^3$ rich amorphous carbon.
Amorphous silicon (a-Si) is a widely studied non-crystalline material, and yet the subtle details of its atomistic structure are still unclear. Here, we show that accurate structural models of a-Si can be obtained by harnessing the power of machine-l
earning algorithms to create interatomic potentials. Our best a-Si network is obtained by cooling from the melt in molecular-dynamics simulations, at a rate of 10$^{11}$ K/s (that is, on the 10 ns timescale). This structure shows a defect concentration of below 2% and agrees with experiments regarding excess energies, diffraction data, as well as $^{29}$Si solid-state NMR chemical shifts. We show that this level of quality is impossible to achieve with faster quench simulations. We then generate a 4,096-atom system which correctly reproduces the magnitude of the first sharp diffraction peak (FSDP) in the structure factor, achieving the closest agreement with experiments to date. Our study demonstrates the broader impact of machine-learning interatomic potentials for elucidating accurate structures and properties of amorphous functional materials.
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