Forecast of electric transport property via two models

Abstract

Temperature and synthesis condition-dependent resistivity are critical parameters for determining the electrical properties of materials. Modeling serves as a powerful tool for predicting the electrical transport characteristics of materials. The first objective of this work was to conduct a quantitative analysis of the reported metallic and insulating resistivity behaviors in double-phase materials. Using non-linear regression, both a phenomenological model and an Asym2sig asymmetric peak model were employed to quantitatively analyze the temperature-dependent resistivity. The fitting results align closely with the observed resistivity-temperature curves. The second objective of this work was to establish a method linking chemical composition (synthesis conditions) to the parameters derived from the two models, calculation on the principle of non-linear curve fitting, two numerical equations are highlighted for quantitatively analyzing chemical composition-dependent parameters. The observed effects of chemical composition on the shift in the temperature-resistive curve, which are fundamental to understanding resistivity behavior, are discussed quantitatively for the first time to our knowledge for resistive-temperature behavior. Hence, the resistivity of both crystalline and non-crystalline materials can be forecasted across various compositions and temperatures using the methods presented, even without experimental data.