journal article·2021-06

Ultrahigh-strength multi-layer graphene-coated Ni film with interface-induced hardening

Dong Jin Kim, Quang-Trung Truong, Jae In Kim, Yeonjoon Suh, Joonhee Moon, Sang-Eui Lee, Byung Hee Hong, Yun Sung Woo

Carbon· Volume 178
Publisher page

Abstract

Abstract Graphene-reinforced metal matrix composites exhibit excellent mechanical properties owing to dislocation impedance at the metal-graphene interface. Graphene coated on metal with composites fabricated using powder sintering can be applied as high-strength thin films across various fields (e.g., microelectromechanical systems, flexible electronics). In this study, a bilayer composite of multilayer graphene (MLG)-coated Ni is synthesized using the chemical vapor deposition (CVD) and transfer methods; mechanical properties are investigated using nanoindentation methods. MLG-coated Ni synthesized by CVD exhibits 195% and 470% increases in hardness and Young’s modulus, respectively, compared with single-layer Ni. In contrast, the Young modulus and hardness of MLG-coated Ni synthesized via the transfer method can be estimated using the rule of mixture for composite materials. Transmission electron microscopy (TEM) shows that in MLG-coated Ni synthesized by CVD, dislocations are dense and evenly distributed compared with that synthesized by the transfer method, leading to its high mechanical strength. Molecular dynamics (MD) simulations demonstrate that interface-induced hardening is effective in graphene-coated Ni(111) with a strongly coupled interface. Therefore, ultrahigh-strength MLG-coated metal films can be obtained by manipulating the interface property between the MLG and metal, offering the potential for use as a thin film resistor against external force.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Graphene research and applications · Diamond and Carbon-based Materials Research · Metal and Thin Film Mechanics

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

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