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Impact of GNPs Reinforcement on Specific Properties of Cu-GNPs Metal Matrix Nanocomposites Under Rapid Induction Sintering Conditions
No full textGraphene is an effective reinforcement material for enhancing the structural properties of copper (Cu) matrix nanocomposites due to its superior mechanical and electrical characteristics. This study investigates the effect of different graphene nanoparticle (GNP) reinforcement rates and rapid induction sintering on the mechanical, electrical, and microstructural characteristics of copper–graphene metal matrix nanocomposites. A powder metallurgy manufacturing strategy involving 3D ball milling and induction sintering was applied to strengthen Cu-GNP metal matrix nanocomposites mechanically. The manufactured nanocomposites demonstrated excellent properties, such as a maximum density of 99% and improved microhardness (27% higher than pure copper). The nanocomposite with the best yield strength (127 MPa) showed a 70% increase, and its ultimate tensile strength (278 MPa) was 43% higher than that of pure Cu, respectively. The nanocomposites were efficiently produced by the induction sintering method. As a result of microstructure examinations, the graphene nanoplates were uniformly dispersed in the copper matrix nanocomposite. Graphene's electrical conductivity was negatively affected by factors such as interfacial resistance and porosity, with a decrease of 3–5% IACS observed depending on the increasing amount of GNPs. This study also presents a practical and efficient approach for producing copper matrix nanocomposites reinforced with GNPs
LONG-TERM OUTCOME OF AN OBSERVATIONAL APPROACH IN PATIENTS WITH<i> NRAS</i>-MUTATED JUVENILE MYELOMONOCYTIC LEUKEMIA AND ABSENCE OF HIGH RISK FEATURES
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Neoadjuvan tedavi öncesi ve sonrası F-18 FDG PET/BT görüntülerinden bireyselleştirilmiş threshold ile elde edilen radyomik parametreleri kullanarak mide ve gastroözofageal bileşke yerleşimli lokal ileri evre mide adenokarsinomlarında patoloji sonuçlarının ve genel sağkalımın makine öğrenimi ile öngörülmesi: Retrospektif çalişma
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