About the contrast of δ’ precipitates in bulk Al–Cu–Li alloys in reflection mode with a field‐emission scanning electron microscope at low accelerating voltage
Bibliographic record
Abstract
Summary Characterising the impact of lithium additions in the precipitation sequence in Al–Li–Cu alloys is important to control the strengthening of the final material. Since now, transmission electron microscopy (TEM) at high beam voltage has been the technique of choice to monitor the size and spatial distribution of δ’ precipitates (Al3Li). Here we report on the imaging of the δ’ phase in such alloys using backscattered electrons (BSE) and low accelerating voltage in a high‐resolution field‐emission scanning electron microscope. By applying low‐energy Ar+ ion milling to the surface after mechanical polishing (MP), the MP‐induced corroded layers were efficiently removed and permitted the δ’s to be visible with a limited impact on the observed microstructure. The resulting BSE contrast between the δ’s and the Al matrix was compared with that obtained using Monte Carlo modelling. The artefacts possibly resulting from the sample preparation procedure were reviewed and discussed and permitted to confirm that these precipitates were effectively the metastable δ’s. The method described in this report necessitates less intensive sample preparation than that required for TEM and provides a much larger field of view and an easily interpretable contrast compared to the transmission techniques.
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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) | 0.002 | 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".