Effect of recovery process on the efficiency of nano-diamond synthesis by shock compression
Source abstract
Abstract
Abstract Shock-induced phase transitions of C60 fullerene crystal structure into amorphous nano-diamond is investigated through molecular dynamics simulations. For this purpose, the Hugoniostat scheme is employed to provide the material behavior in a wide range of shock strength. The shock pressure corresponding to the Hugoniot elastic limit and phase transition (PT) are extracted from the Hugoniot curves at 27 and 63 GPa, respectively, which are in close correlations with former experimental results. Increasing the shock strength beyond the PT, an incompressibility is observed in the material which accelerates the trend of temperature rise in the shocked structure followed by material melting. After the shock compression to a pressure above the PT, a quenching process is also imposed on the post-shock material to bring it into the ambient conditions. To this end, a range of cooling rates from 1013 K/s to infinity are examined. It is shown that although the increase in the cooling rate does not have any significant effect on the percentage of carbon atoms in sp3 hybridization, the stability of final product is enhanced to some extent by utilizing lower cooling rates. However, it should be noted that the cooling rate should be high enough to prevent the material from liquefying. Moreover, the simulation results emphasize on the importance of a quenching process within the shock compression. This scheme acts as a heat sink during the shock compression that discharge the high level of kinetic energy from the material and let the atoms to form a compact configuration. The simulation results are in agreement with some evidences in former experiments.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Diamond and Carbon-based Materials Research · High-pressure geophysics and materials · Fullerene Chemistry and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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