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Title: Transformation of shock-compressed graphite to hexagonal diamond in nanoseconds

Journal Article · · Science Advances

The graphite-to-diamond transformation under shock compression has been of broad scientific interest since 1961. The formation of hexagonal diamond (HD) is of particular interest because it is expected to be harder than cubic diamond and due to its use in terrestrial sciences as a marker at meteorite impact sites. However, the formation of diamond having a fully hexagonal structure continues to be questioned and remains unresolved. Using real-time (nanosecond), in situ x-ray diffraction measurements, we show unequivocally that highly oriented pyrolytic graphite, shock-compressed along the c axis to 50 GPa, transforms to highly oriented elastically strained HD with the (100)HD plane parallel to the graphite basal plane.

Research Organization:
Argonne National Laboratory (ANL), Argonne, IL (United States). Advanced Photon Source (APS)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
OSTI ID:
1408127
Journal Information:
Science Advances, Vol. 3, Issue 10; ISSN 2375-2548
Publisher:
AAAS
Country of Publication:
United States
Language:
ENGLISH

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Cited By (10)

Predicting superhard materials via a machine learning informed evolutionary structure search journal September 2019
Quantifying hexagonal stacking in diamond journal July 2019
Laboratory setup for rapid in situ powder X-ray diffraction elucidating Ni particle formation in supercritical methanol journal July 2019
Graphite to diamond transformation under shock compression: Role of orientational order journal June 2019
Nano-to-micro diamond formation by nanosecond pulsed laser annealing journal September 2019
Ab initio dielectric response function of diamond and other relevant high pressure phases of carbon journal November 2019
Structural studies of carbons by neutron and x-ray scattering journal November 2018
Nanosecond Melting and Recrystallization in Shock-Compressed Silicon journal September 2018
Analysis of stacking disorder in ice I using pair distribution functions journal July 2018
Predicting Superhard Materials via a Machine Learning Informed Evolutionary Structure Search preprint January 2019

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