journal article·2021-09-08Open access

Quantifying Mn Diffusion through Transferred versus Directly Grown Graphene Barriers

Patrick J. Strohbeen, Sebastian Manzo, Vivek Saraswat, Katherine Su, Michael S. Arnold, Jason K. Kawasaki

ACS Applied Materials & Interfaces· Volume 13· Issue 35

Abstract

We quantify the mechanisms for manganese (Mn) diffusion through graphene in Mn/graphene/Ge (001) and Mn/graphene/GaAs (001) heterostructures for samples prepared by graphene layer transfer versus graphene growth directly on the semiconductor substrate. These heterostructures are important for applications in spintronics; however, challenges in synthesizing graphene directly on technologically important substrates such as GaAs necessitate layer transfer and annealing steps, which introduce defects into the graphene. In situ photoemission spectroscopy measurements reveal that Mn diffusion through graphene grown directly on a Ge (001) substrate is 1000 times lower than Mn diffusion into samples without graphene (Dgr,direct ∼ 4 × 10–18 cm2/s, Dno-gr ∼ 5 × 10–15 cm2/s at 500 °C). Transferred graphene on Ge suppresses the Mn in Ge diffusion by a factor of 10 compared to no graphene (Dgr,transfer ∼ 4 × 10–16 cm2/s). For both transferred and directly grown graphene, the low activation energy (Ea ∼ 0.1–0.5 eV) suggests that Mn diffusion through graphene occurs primarily at graphene defects. This is further confirmed as the diffusivity prefactor, D0, scales with the defect density of the graphene sheet. Similar diffusion barrier performance is found on GaAs substrates; however, it is not currently possible to grow graphene directly on GaAs. Our results highlight the importance of developing graphene growth directly on functional substrates to avoid the damage induced by layer transfer and annealing.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Graphene research and applications · Advancements in Battery Materials · Advanced Memory and Neural Computing

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Materials Science and Engineering, University of Wisconsin Madison, Madison, Wisconsin 53706, United States

Funding

National Science Foundation · U.S. Department of Energy, Office of Science, Basic Energy Sciences

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