Preparation of Ag/reduced graphene oxide reinforced copper matrix composites through spark plasma sintering: An investigation of microstructure and mechanical properties
Source abstract
Abstract
Abstract The reduced graphene oxide (rGO) decorated with Ag nanoparticles was synthesized by the chemical reduction of graphene oxide in an aqueous solution containing AgNO3, in the presence of hydrazine hydrate as a reducing agent. The reduction of graphene oxide was confirmed by FT-IR and raman spectroscopy analyses. The x-ray diffraction pattern and UV–visible investigations demonstrated the formation of Ag particles on the surface of rGO sheets. After successful decoration, the Ag/rGO nano-composite was used as the reinforcement in the copper matrix composite. Cu–Ag/rGO composites with different percentages of Ag/rGO (0.4, 0.8, 1.6 and 3.2 vol%.) were prepared by mechanical milling and spark plasma sintering (SPS). The effects of the Ag/rGO content on the consolidation process, micro-hardness, bending strength and also, fracture surface of the prepared samples were then investigated. The three-point bending strength of the sintered samples was increased from 285 to 472 MPa by the addition 0.8 vol%. of Ag/rGO, as compared to the pure Cu. Moreover, increasing the reinforcement content to the 3.2 vol%. Ag/rGO led to decreasing the bending strength to 433 MPa. The highest micro-hardness (81 Hv) was obtained for the composite sample containing the 1.6 vol%. Ag/rGO. By increasing Ag/r-GO as the reinforcement (3.2 vol%.), the Vickers hardness was decreased to 69 Hv. Also, investigation of the fracture surface morphology showed transformation of fracture mechanism from plastic changes to brittle ones by raising the Ag/rGO content volume from 0.8 to 1.6 vol %.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Aluminum Alloys Composites Properties · Advanced materials and composites · Electromagnetic wave absorption materials
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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