Use of QUASES™/XPS measurements to determine the oxide composition and thickness on an iron substrate
Bibliographic record
Abstract
Abstract QUASES™ Analyze and Generate were used to model the extrinsic loss structures for XPS spectra of oxide films grown on iron in such a way that their thickness and structure could be determined. The Generate program used in conjunction with spectra of model iron oxides allowed for both magnetite (Fe 3 O 4 ) and maghaemite (γ‐Fe 2 O 3 ) structures to be identified in all films studied. These structures were identified as overlying layers in the oxide films and were usually intermixed at their interface. The absence of other iron oxide structures within the film could be tested based on their goodness of fit to the experimental spectrum. Comparison of the thickness values obtained using Generate with those found using nuclear reaction analysis suggested that the Generate results were higher by 20%. This difference likely resulted from the use of a calculated inelastic mean free path value for Fe 2p electrons in the Generate calculation rather than using the real attenuation length. For oxide films whose thickness approached 10 nm, the QUASES™ results for photoelectron spectra obtained with a Zr achromatic x‐ray source were compared with those from the standard Al monochromatic source. In this particular case, the oxide thicknesses obtained using Generate and Analyze were found to be more consistent when the Zr source was used. Copyright © 2004 John Wiley & Sons, Ltd.
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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) | 0.001 | 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".