journal article·2025-04Open access

Elucidating grain boundary energy minimization mechanisms in textured Ca-doped alumina with inclination-dependent Monte Carlo Potts simulations

Lin Yang, Bryan Conry, Hailey E. Hall, Joel B. Harley, Michael S. Kesler, Michael R. Tonks, Amanda R. Krause

Acta Materialia· Volume 288

Abstract

The grain growth behavior of textured Ca-doped alumina is compared to Monte Carlo Potts (MCP) simulations to investigate the effect of anisotropic grain boundary (GB) properties on local boundary migration. Experimentally, the growth of textured Ca-doped alumina results in highly elongated grains. The relative GB energy distribution is measured using the thermal groove method before and after heat treating at 1600°C, finding that high energy GBs are eliminated during grain growth. No significant difference in the GB energy distributions is found between the long and short axes of the elongated grains, suggesting that anisotropic mobility may be responsible for the grain shape. However, MCP simulations with anisotropic mobility as a function of plane inclination do not result in grains with distinct morphologies, regardless of the degree of anisotropy introduced. The final grain shape after grain growth of textured Ca-doped alumina resembles that of the MCP simulations using an anisotropic GB energy as a cosine function of plane inclination. Several energy functions are tested and only those that mathematically impose a torque (second derivative of energy with respect to the plane inclination angle) result in elongated grains. Although area reduction is the dominant energy minimization mechanism, these results suggest that local GB migration is affected by anisotropic GB energy and torque and alternative mechanisms like GB replacement and reorientation.
Source: openalex_inverted_index · Quality: baseline

Research context

OpenAlex topics: Solidification and crystal growth phenomena · Aluminum Alloy Microstructure Properties · Advanced ceramic materials synthesis

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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