Flat ion milling: a powerful tool for preparation of cross-sections of lead-silver alloys
Bibliographic record
Abstract
While conventional mechanical and chemical polishing results in stress, deformation and polishing particles embedded on the surface, flat milling with Ar+ ions erodes the material with no mechanical artefacts. This flat milling process is presented as an alternative method to prepare a Pb-Ag alloy cross-section for scanning electron microscopy. The resulting surface is free of scratches with very little to no stress induced, so that electron diffraction and channelling contrast are possible. The results have shown that energy dispersive spectrometer (EDS) mapping, electron channelling contrast imaging and electron backscatter diffraction can be conducted with only one sample preparation step. Electron diffraction patterns acquired at 5 keV possessed very good pattern quality, highlighting an excellent surface condition. An orientation map was acquired at 20 keV with an indexing rate of 90.1%. An EDS map was performed at 5 keV, and Pb-Ag precipitates of sizes lower than 100 nm were observed. However, the drawback of the method is the generation of a noticeable surface topography resulting from the interaction of the ion beam with a polycrystalline and biphasic sample.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".