Grain growth and stabilisation of nanostructured aluminium at high temperatures: Review
Bibliographic record
Abstract
Nanostructured (NS) materials have a large stored energy due to their large grain boundary area and thus tend to be unstable with respect to grain growth during high temperature annealing or deformation. This problem can limit the application of NS materials at high temperatures (>0·5 Tm, absolute melting temperature), especially Al alloys owing to their low melting points. Restoration processes and grain growth in NS Al based materials are critically reviewed, with emphasis on nanostructure grain stabilisation at high temperatures. The mechanisms of normal and abnormal grain growth during isothermal annealing are presented, followed by consideration of thermal stabilisation by the addition of solute atoms/impurities and/or dispersion of second phase particles. Grain growth is significantly facilitated by applying deformation at elevated temperatures during preparation or further processing of semifinished NS materials. The dynamic restoration processes, dynamic grain growth and dynamic particle coarsening are addressed in NS Al. Finally, grain growth during consolidation of nanocrystalline powders (one of the principal methods to fabricate bulk NS Al) is presented, and the effects of processing parameters on grain size stabilisation are discussed.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".