Enhanced Electrical Conductivity and Tensile Strength of Cu/Single-Layer Graphene/Cu Nanomaterials
Source abstract
Abstract
Copper (Cu) is widely used for electrical conduction but the inherent resistive heating not only wastes significant amounts of energy but also creates severe reliability issues; therefore, how to increase copper’s electrical conductivity has been a goal with a long history. Although nanometer-thin graphene has been regarded with great potential to increase copper’s conductive performance, published results are far from expectations and the quest remains about the actual enhancement efficiency of graphene. Here, we demonstrated that single-layer graphene in a Cu-sandwiched Cu/graphene/Cu composite structure fabricated by combining chemical vapor deposition (CVD) of graphene with magnetron sputtering deposition of copper can significantly increase annealed Cu’s electrical conductivity and strength at the same time. With a graphene content of less than 0.0008 vol % (a single-layer graphene sandwiched by 260 nm sputtering copper and 45 μm copper foil), the resultant electrical conductivity is about 110% of the annealed copper foil. In addition, the tensile strength is about 187% of the annealed copper. The converted enhancement efficiency per unit volume fraction of graphene is about a factor of 125 for the electrical conductivity and about a factor of 1096 for the tensile strength, respectively. The increased electrical conductivity and strength of Cu/graphene/Cu-sandwiched composite is attributed to the soundness of single-layer graphene obtained from the CVD growth, the graphene quality retention through the sputtering deposition process, and the good interfacial bonding formed between graphene and copper nanofilms that materializes band gap tuning by doping.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Graphene research and applications · Nanomaterials and Printing Technologies · Advanced Memory and Neural Computing
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
School of Electrical Engineering, Southwest Jiaotong University, Chengdu 610031, PR China
Funding
Cao Jianyou Foundation
References (52)
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