Manipulation of graphene-based systems is a formidable challenge, since it requires the control of atomic interactions over long timescales. Although the effectiveness of a certain number of processes has been experimentally demonstrated, the underlying atomic mechanisms are often not understood. An import class of techniques relies on the interaction between hydrogen and graphene, which is the focus of this research. In particular, the growth of epitaxial graphene on SiC(0001) is subject to a single-atom-thick interface carbon layer strongly bound to the substrate, which can be detached through hydrogen intercalation. Here we report that a nucleation phenomenon induces the transformation of this buffer layer into graphene. We study the graphenization dynamics by an ab initio based method that permits the simulation of large systems with an atomic resolution, spanning the time scales from nanoseconds to hours. The early evolution stage (∼ms time scale) is characterised by the formation of a metastable H layer deposited on the C surface. H penetration in the interface between the C monolayer and the SiC(0001) surface is a rare event due to the large penetration barrier, which is ∼2 eV. However, at high H densities, energetically favoured Si–H bonding appears on the substrate's surface. The local increase of the H density at the interface due to statistical transitions leads to the graphenization of the overlying C atoms. Thermally activated density fluctuations promote the formation of these graphene-like islands on the buffer layer: this nucleation phenomenon is evidenced by our simulations at a later evolution stage (>102 s at 700 °C for ∼3.6 × 1015 at. cm−2 s−1 H flux). Such nuclei grow and quasi-freestanding graphene forms if the exposition to the H flux continues for a sufficiently long time (∼30 min for the same conditions). We have systematically explored this phenomenon by varying the substrate temperature and the H flux, demonstrating that the surface morphology during graphenization and post-graphenization anneals significantly depends on these variables. The computational findings are consistent with the experimental analyses reported so far and could serve as guidelines for future experimental works on graphene manipulation.
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