Contribution of a New Generation Field-Emission Scanning Electron Microscope in the Understanding of a 2099 Al-Li Alloy
Bibliographic record
Abstract
Aluminum-lithium alloys are widespread in the aerospace industry. The new 2099 and 2199 alloys provide improved properties, but their microstructure and texture are not well known. This article describes how state-of-the-art field-emission scanning electron microscopy (FE-SEM) can contribute to the characterization of the 2099 aluminum-lithium alloy and metallic alloys in general. Investigations were carried out on bulk and thinned samples. Backscattered electron imaging at 3 kV and scanning transmission electron microscope imaging at 30 kV along with highly efficient microanalysis permitted correlation of experimental and expected structures. Although our results confirm previous studies, this work points out possible substitutions of Mg and Zn with Li, Al, and Cu in the T1 precipitates. Zinc and magnesium are also present in "rice grain"-shaped precipitates at the grain boundaries. The versatility of the FE-SEM is highlighted as it provides information in the macro- and microscales with relevant details. Its ability to probe the distribution of precipitates from nano- to microsizes throughout the matrix makes FE-SEM an essential technique for the characterization of 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".