Effect of Annealing on Microstructure, Grain Growth and Hardness of Nanocrystalline Cu-Zr Alloy Prepared by Cryogenic Ball Milling
Bibliographic record
Abstract
Nanocrystalline Cu-0.75 at.%Zr alloy was synthesized by high energy ball milling under cryogenic temperature. To investigate the influence of 0.75 at.%Zr addition on thermal stabilization of nanocrystalline state of Copper, milled powder was annealed up to T/Tm = 0.79 for 1h in an inert atmosphere. The microstructural changes of both milled and annealed powders were characterized by X-ray diffraction (XRD) and transmission electron microscopy (TEM). Mechanical properties were determined in terms of hardness. It was found that addition of 0.75 at.%Zr can stabilize grain size at higher temperature, i.e., ~ 32 nm at 800oC (T/Tm = 0.79). The hardness of Cu-0.75 at.%Zr at 800oC was found to decrease by only ~ 13% as opposed to a 65% decrease in pure copper from cryomilled condition. The thermal stability of Cu-0.75 at.%Zr system at high temperatures was attributed to the kinetic stabilization, i.e., grain boundary pinning by intermetallic phases. Thermal stability contributions were assessed by thermodynamic models elicits added Zr is not sufficient for stabilization, rather kinetic stabilization (by intermetallic pinning of grain boundary) became active at higher annealing temperature.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".