Strong Hall–Petch Type Behavior in the Elastic Strain Limit of Nanotwinned Gold Nanowires
Bibliographic record
Abstract
Pushing the limits of elastic deformation in nanowires subjected to stress is important for the design and performance of nanoscale devices from elastic strain engineering. Particularly, introducing nanoscale twins has proved effective in rising the tensile strength of metals. However, attaining ideal elastic strains in nanotwinned materials remains challenging, because nonuniform twin sizes locally affect the yielding behavior. Here, using in situ high-resolution transmission electron microscopy tensile testing of nanotwinned [111]-oriented gold nanowires, we report direct lattice-strain measurements that demonstrate a strong Hall-Petch type relationship in the elastic strain limit up to 5.3%, or near the ideal theoretical limit, as the twin size is decreased below 3 nm. It is found that the largest twin in nanowires with irregular twin sizes controls the slip nucleation and yielding processes in pure tension, which is in agreement with earlier atomistic simulations. Continuous hardening behavior without loss of strength or softening is observed in nanotwinned single-crystalline gold nanowires, which differs from the behaviors of bulk nanocrystalline and nanotwinned-nanocrystalline metals. These findings are of practical value for the use of nanotwinned metallic and semiconductor nanowires in strain-engineered functional microdevices.
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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 | 0.001 |
| 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".