Influences of solute segregation on grain boundary motion
Bibliographic record
Abstract
Nanocrystalline materials are polycrystalline solids with grain size in the nanometer range (< 100nm), which have been found to exhibit superior properties such as high magnetic permeability and corrosion resistance, as well as a considerably increase of strength when compared with their coarse grain counterparts. All those improved properties are attributed to the high volume fraction of grain boundaries (GBs). However, the high density of GBs brings a large amount of excess enthalpy to the whole system, making the nanostructures unstable and suffer from severe thermal or mechanical grain growth. In order to maintain the advantageous properties of nanocrystalline materials, it is necessary to stabilize GB and inhibit grain growth. While alloying has been found to be an effective way of achieving stabilized nanocrystalline metal alloys experimentally, the direct quantification of solute effects on GB motion still poses great challenge for investigating thermal stability of general nanocrystalline materials. In this research, impurity segregation and solute drag effects on GB motion were investigated by extending the interface random-walk method in direct molecular dynamics simulations. It was found that the GB motion was controlled by the solute diffusion perpendicular to the boundary plane. Based on the simulation results at different temperatures and impurity concentrations, the solute drag effects can be well modeled by the theory proposed by Cahn, Lücke and Stüwe (CLS model) more than fifty years ago. However, a correction to the original CLS model needs to be made in order to quantitatively predict the solute drag effects on a moving GB.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".