Evolution of Precipitate Depleted Zones in Mg-Based Alloys Strengthened by Precipitation Hardening
Bibliographic record
Abstract
Extensive research work has been devoted to Mg-based alloys strengthened by precipitation hardening. Increasing the aging time leads to the appearance of zones depleted of precipitates near grain boundaries. The formation of precipitate depleted zones (PDZ's) is explained by near-grain boundary (NGB) coarsening. The evolution of PDZ's was considered on the basis of the model taking into account diffusional fluxes between adjacent precipitates. The set of equations was solved numerically by using a fourth-order Runge-Kutta method for different initial sizes of precipitates and densities of precipitate layers near grain boundaries. The dissolution of precipitates in the NGB-zones is initially provided by diffusion from them to large precipitates at the grain boundary, and then also by diffusion from these decreased precipitates to the larger precipitates at the outer border of the PDZ. As a result, the outer borders of the depleted zones are adjoined by bands of enlarged precipitates forming a PDZ "crust". Being a diffusion controlled process, the depleted zones are widened with temperature and aging time. Experimental investigation of PDZ evolution was conducted by SEM and TEM on Mg-Zn-Sn-alloys aged at different temperatures for different times. Comparison of the calculated results with experimental data allowed the evaluation of the model parameters and physical parameters of the system (diffusion coefficients and interface energy of the precipitated phases).
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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) | 0.000 | 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".