Lecture

Autodiffusion in Metals

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In course

MSE-209: Transfer phenomena in materials science

Ce cours porte sur le transfert de la chaleur par conduction, convection et rayonnement, ainsi que sur la diffusion à l'état solide. D'après les règles phénoménologiques (Equations de Fourrier et Fick), des problèmes de solution 1-D et 2-D ainsi que modèle ...

Description

This lecture covers the topic of autodiffusion in metals, focusing on the influence of temperature and pressure, empirical correlations, and autodiffusion in different metal structures such as cubic face centered, hexagonal compact, and cubic centered. The instructor discusses the movement of atoms, the formation and migration of defects, and provides examples like ceramic sintering. The lecture also delves into the quantification of diffusion coefficients, the effect of temperature on diffusion, and the correlation between diffusion and crystal structures. Various intrinsic defects in solids and deviations from Arrhenius' law are explored, along with the impact of pressure on diffusion. The lecture concludes with a discussion on the energy balance in thermal diffusion and empirical correlations for diffusion in different crystal structures.

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Related concepts (32)

Diffusion

Diffusion is the net movement of anything (for example, atoms, ions, molecules, energy) generally from a region of higher concentration to a region of lower concentration. Diffusion is driven by a gradient in Gibbs free energy or chemical potential. It is possible to diffuse "uphill" from a region of lower concentration to a region of higher concentration, like in spinodal decomposition. Diffusion is a stochastic process due to the inherent randomness of the diffusing entity and can be used to model many real-life stochastic scenarios.

Metal

A metal (from Ancient Greek μέταλλον métallon 'mine, quarry, metal') is a material that, when freshly prepared, polished, or fractured, shows a lustrous appearance, and conducts electricity and heat relatively well. Metals are typically ductile (can be drawn into wires) and malleable (they can be hammered into thin sheets). These properties are the result of the metallic bond between the atoms or molecules of the metal. A metal may be a chemical element such as iron; an alloy such as stainless steel; or a molecular compound such as polymeric sulfur nitride.

Correlation

In statistics, correlation or dependence is any statistical relationship, whether causal or not, between two random variables or bivariate data. Although in the broadest sense, "correlation" may indicate any type of association, in statistics it usually refers to the degree to which a pair of variables are linearly related. Familiar examples of dependent phenomena include the correlation between the height of parents and their offspring, and the correlation between the price of a good and the quantity the consumers are willing to purchase, as it is depicted in the so-called demand curve.

Molecular diffusion

Molecular diffusion, often simply called diffusion, is the thermal motion of all (liquid or gas) particles at temperatures above absolute zero. The rate of this movement is a function of temperature, viscosity of the fluid and the size (mass) of the particles. Diffusion explains the net flux of molecules from a region of higher concentration to one of lower concentration. Once the concentrations are equal the molecules continue to move, but since there is no concentration gradient the process of molecular diffusion has ceased and is instead governed by the process of self-diffusion, originating from the random motion of the molecules.

Pearson correlation coefficient

In statistics, the Pearson correlation coefficient (PCC) is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance of two variables and the product of their standard deviations; thus, it is essentially a normalized measurement of the covariance, such that the result always has a value between −1 and 1. As with covariance itself, the measure can only reflect a linear correlation of variables, and ignores many other types of relationships or correlations.

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