The effect of grain boundary on the local incipient plastic deformation of fcc metals during nanoindentation
Bibliographic record
Abstract
The effect of grain boundaries (GBs) on deformation mechanisms becomes increasingly important as the volume of deformation reaches the submicrometer and nanoscale. The current work investigates the impact of grain boundaries on the incipient plasticity of small-scale deformations of fcc metals. For this purpose, the behavior of single and bi-crystal Au thin films during nanoindentation are studied, using large-scale atomistic simulations. Various symmetric ⟨110⟩ tilt GBs with a wide range of misorientation angles are included to analyze the effect of GB geometry on the nanoscale plasticity mechanisms. Potentially, GBs can act as a source, sink, or obstacle for lattice dislocation, depending on their geometry, energy level, and distance from the deformation zone. The role of the heterogeneous nucleation and emission of dislocations from GBs on the plasticity and hardness of bicrystals is analyzed. According to our results, the intrinsic free volume involved in the GB region is associated with dislocation nucleation at the GB. The volume of the plastic zone generated beneath the tip and the way it grows is strongly dependent on the GB structure. Dislocation nucleation occurs predominantly in the early stages of indentation at GBs with a dissociated interface structural unit, before the interaction of lattice dislocation and GB. Coherent twin boundaries display the lowest effect on the hardness. Based on our results, there is a strong correlation between the interfacial boundary energy and its effect on the bicrystal hardness. GBs with lower interfacial energy offer a higher barrier against slip transmission and nucleation at the 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 | 0.000 | 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".