Sadeem Abbas Fadhil, Saif M. Jasim, Ghasaq Talal Suhail, Sara M Ibraheim

Theoretical Analysis of Grain Size Influence on Electrical Conductivity in Nickel and Copper

Introduction

Theoretical analysis of grain size influence on electrical conductivity in nickel and copper. New model analyzes grain size influence on electrical conductivity in Nickel & Copper, showing best agreement with experimental data. Introduces time of events theory.

Abstract

This work presents a new model for electrical conductivity versus grain size derived from a new theory called the time of events theory. The time of events (TE) model was applied to experimental data of electrical conductivity versus grain sizes of two metals, copper Cu and Nickel Ni due to their industrial importance in transferring electricity and electrical circuits. In addition, a comparative analysis of two other models was applied to the experimental data. The Maydas-Shatzkes model and the Andrews model. The newly derived model showed the best agreement with experimental data of Cu and Ni among all models. It uses mainly three factors: the first one is for extremely tiny grain sizes, while the second is for small grain sizes, and the third is for relatively large grain sizes. From comparing the fitting data with the Anderws models, it was noticed that the third parameter values were close to the Anderws model parameter. Therefore, Andrews' model is well applied to large grain sizes, but it deviates at extremely tiny grain sizes. This new model also predicted that the Ni data have minima and maxima, while other models did not have such a prediction.

Review

This paper presents a compelling theoretical analysis investigating the critical influence of grain size on the electrical conductivity of industrially important metals, specifically copper (Cu) and nickel (Ni). The work introduces a novel approach, grounding its findings in a newly developed "time of events (TE) theory" from which a predictive model for electrical conductivity versus grain size is derived. This undertaking aims to provide a more accurate and comprehensive understanding of this relationship, which is vital for optimizing materials used in electrical transfer and circuit applications. The methodology involves the rigorous application of the newly formulated TE model to existing experimental data for both Cu and Ni. Crucially, the study includes a robust comparative analysis, evaluating the TE model's performance against two well-established models: the Maydas-Shatzkes model and the Andrews model. The results indicate a significant advancement, with the TE model demonstrating the superior agreement with experimental data across both metals. The model's strength lies in its ability to account for three distinct grain size regimes—extremely tiny, small, and relatively large—suggesting a more nuanced understanding of grain boundary scattering mechanisms. Furthermore, the analysis reveals that while the Andrews model performs adequately for large grain sizes, its utility diminishes significantly at extremely tiny grain sizes, a limitation successfully addressed by the TE model. A particularly notable finding is the TE model's unique prediction of minima and maxima in the electrical conductivity data for Ni, a phenomenon not captured by the other existing models. The findings presented in this work carry substantial implications for materials science and engineering. The demonstrated superior accuracy of the TE model, particularly its comprehensive handling of varying grain size scales and its novel prediction for Ni, suggests a significant step forward in the theoretical modeling of electrical conductivity. This enhanced predictive capability could prove invaluable for the design and optimization of metallic components in diverse electrical applications, where precise control over microstructure and electrical properties is paramount. Overall, this paper makes a valuable contribution by offering a more robust and insightful theoretical framework for understanding the intricate interplay between grain size and electrical conductivity in crucial engineering materials.

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