Size effect on atomic structure in low-dimensional Cu-Zr amorphous systems
Bibliographic record
Abstract
Abstract The size effect on atomic structure of a Cu64Zr36 amorphous system, including zero-dimensional small-size amorphous particles (SSAPs) and two-dimensional small-size amorphous films (SSAFs) together with bulk sample was investigated by molecular dynamics simulations. We revealed that sample size strongly affects local atomic structure in both Cu64Zr36 SSAPs and SSAFs, which are composed of core and shell (surface) components. Compared with core component, the shell component of SSAPs has lower average coordination number and average bond length, higher degree of ordering, and lower packing density due to the segregation of Cu atoms on the shell of Cu64Zr36 SSAPs. These atomic structure differences in SSAPs with various sizes result in different glass transition temperatures, in which the glass transition temperature for the shell component is found to be 577 K, which is much lower than 910 K for the core component. We further extended the size effect on the structure and glasses transition temperature to Cu64Zr36 SSAFs, and revealed that the Tg decreases when SSAFs becomes thinner due to the following factors: different dynamic motion (mean square displacement), different density of core and surface and Cu segregation on the surface of SSAFs. The obtained results here are different from the results for the size effect on atomic structure of nanometer-sized crystalline metallic alloys.
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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".