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      Nonreciprocal thermal photonics

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          Boron nitride substrates for high-quality graphene electronics

          Graphene devices on standard SiO(2) substrates are highly disordered, exhibiting characteristics that are far inferior to the expected intrinsic properties of graphene. Although suspending the graphene above the substrate leads to a substantial improvement in device quality, this geometry imposes severe limitations on device architecture and functionality. There is a growing need, therefore, to identify dielectrics that allow a substrate-supported geometry while retaining the quality achieved with a suspended sample. Hexagonal boron nitride (h-BN) is an appealing substrate, because it has an atomically smooth surface that is relatively free of dangling bonds and charge traps. It also has a lattice constant similar to that of graphite, and has large optical phonon modes and a large electrical bandgap. Here we report the fabrication and characterization of high-quality exfoliated mono- and bilayer graphene devices on single-crystal h-BN substrates, by using a mechanical transfer process. Graphene devices on h-BN substrates have mobilities and carrier inhomogeneities that are almost an order of magnitude better than devices on SiO(2). These devices also show reduced roughness, intrinsic doping and chemical reactivity. The ability to assemble crystalline layered materials in a controlled way permits the fabrication of graphene devices on other promising dielectrics and allows for the realization of more complex graphene heterostructures.
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            Weyl and Dirac semimetals in three-dimensional solids

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              Discovery of a Weyl fermion semimetal and topological Fermi arcs

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                Author and article information

                Contributors
                Journal
                Nature Photonics
                Nat. Photon.
                Springer Science and Business Media LLC
                1749-4885
                1749-4893
                May 2024
                April 10 2024
                May 2024
                : 18
                : 5
                : 412-424
                Article
                10.1038/s41566-024-01409-y
                d7146c67-d0a1-4658-b4fb-f8dee992e90f
                © 2024

                https://www.springernature.com/gp/researchers/text-and-data-mining

                https://www.springernature.com/gp/researchers/text-and-data-mining

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