Solid solution softening in single crystalline metal nanowires studied by atomistic simulations
Bibliographic record
Abstract
Solid solution strengthening is a common method used in physical metallurgy to increase the strength of metals. However, it is also possible for solute atoms to reduce the strength of metals, known as the solid solution softening effect. In this paper, atomistic simulations were carried out using molecular dynamics and Monte Carlo simulations to explore the softening phenomenon in single crystalline metal nanowires (MNWs) of different alloy systems. It was found that, for single crystalline MNWs, softening is more prominent than strengthening when solute atoms are introduced, which contrasts with the solid solution strengthening that is usually observed in bulk metals. The reduction of unstable stacking fault energy, increase in atomic size misfit, and solute clustering are responsible for this phenomenon, as they facilitate the surface dislocation nucleation in the alloyed nanowires. Additionally, while the nanowire diameter, orientation, surface segregation, and chemical short-range ordering all influence the yield strength, they do not alter the overall softening trend. It is assumed that the softening mechanisms uncovered in this paper are applicable to metallic structures whose yielding is determined by dislocation nucleation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".