Anomalous increase in thermal conductivity of Mg solid solutions by co-doping with two solute elements
Source abstract
Abstract
Solute atoms inevitably induce lattice distortion in solid solution (matrix), thereby prompting the widespread belief that minimizing solute atom concentration within the Mg matrix (solid solution) is imperative in designing high thermal conductivity Mg alloys. Nevertheless, the aforementioned design approach is summarized from experimental perspectives. This study reveals, for the first time, the direct factors influencing thermal conductivity—specifically, electron motion ability (free electron density ( n ) and mean free path of free electrons near the Fermi surface ( l F ))—from a physical essence standpoint. A quantitative physical model of thermal conductivity incorporating these two factors has been established. It is demonstrated that by doping two specific solute elements (one with a larger atomic volume than that of Mg, and the other with a smaller atomic volume than that of Mg; The binding force between the two types of solute atoms must exceed their individual binding forces with Mg) into the Mg matrix could possibly enhance l F , thereby improving the thermal conductivity of Mg solid solutions. The method was successfully applied to MgSm(Al) solid solutions and achieved an anomalous increase in thermal conductivity. This suggests that designing high thermal conductivity Mg alloys may not require reducing the concentration of solute atoms in the Mg matrix. This discovery challenges the traditional approach to designing high thermal conductivity Mg alloys and presents broader opportunities for enhancing the diversity and performance of high thermal conductivity Mg alloy systems.
Source: expertdata · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Thermodynamic and Structural Properties of Metals and Alloys · Thermal Expansion and Ionic Conductivity · Magnesium Oxide Properties and Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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