Dynamic recrystallization of Silver nanocubes during high-velocity impacts
Bibliographic record
Abstract
We study microstructural evolution of Silver (Ag) single-crystal nanocubes during high-velocity impacts, their dynamic recrystallization , and post-impact lattice structure using a combination of molecular dynamics and ab-initio simulations. Our study shows that, upon the impact, some preferential orientations can develop intricate, architected microstructures with grains of different sizes. These selected orientations correspond to the cases where at least eight or more slip systems are simultaneously activated, leading to an avalanche of dislocations. These dislocations interact and have the ability to produce severe plastic work, stimulating recrystallization in the nanocubes. On the other hand, dynamic recrystallization is not observed for the orientations with asynchronously activated slip systems besides large shock-wave pressures, plastic deformation , and large dislocation densities . Using thermalized ab-initio simulations, we find that the severe plastic deformation can trigger phase transformation of the initial face-centered cubic lattice structure to the 4H hexagonal closed-packed phase, which is thermodynamically more stable than the 2H hexagonal closed-packed phase. These results are in good agreement with experimental works. Our systematic numerical experiments shed light on the factors that promote dynamic recrystallization and provide a pathway to control the microstructure and atomic structure simultaneously by orienting nanocubes during the impact.
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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".