Abstract
Nanotechnology has emerged as a transformative field in material science, offering unprecedented opportunities to enhance the electrical properties of conventional materials through the incorporation of nano-sized particles. In this extensive study, we explore the effects of varying weight percentages (1%, 2%, and 3%) and lengths (30 nm, 60 nm, 150 nm, and 250 nm) of nano copper on the electrical conductivity of aluminum (Al) across different temperatures (20°C, 50°C, and 100°C). Additionally, we investigate the predictive capabilities of Artificial Neural Networks (ANN) in forecasting the electrical conductivity variations of Al based on these parameters. Through a detailed analysis of experimental results and ANN modeling,
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