Influences of triple junctions on stress-assisted grain boundary motion in nanocrystalline materials
Bibliographic record
Abstract
Stress-assisted grain boundary motion is among the most studied modes of microstructural evolution in crystalline materials. In this study, molecular dynamics simulations were used to systematically investigate the influences of triple junctions on the stress-assisted motion of symmetric tilt grain boundaries in Cu by considering a honeycomb nanocrystalline model. It was found that the grain boundary motion in nanocrystalline models was highly sensitive to the loading mode, and a strong coupling effect which was prevalent in bicrystal models was only observed when simple shear was applied. In addition, the coupling factor extracted from the honeycomb model was found to be larger and more sensitive to temperature change than that from bicrystal models for the same type of grain boundary under the same loading conditions. Furthermore, the triple junctions seemed to exhibit unusual asymmetric pinning effects to the migrating grain boundary and the constraints by the triple junctions and neighboring grains led to remarkable non-linear grain boundary motion in directions both parallel and normal to the applied shear, which was in stark contrast to that observed in bicrystal models. In addition, dislocation nucleation and propagation, which were absent in the bicrystal model, were found to play an important role on shear-induced grain boundary motion when triple junctions were present. In the end, a generalized model for shear-assisted grain boundary motion was proposed based on the findings from this research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".