Interpretation of complex x-ray photoelectron peak shapes. II. Case study of Fe 2p3/2 fitting applied to austenitic stainless steels 316 and 304
Bibliographic record
Abstract
In this paper, a review of the analysis of Fe 2p3/2 peak and other transition metals in the austenitic stainless steel literature is presented. It reveals the significant shortcomings of the most widely used approaches, based on the principle of “chemistry fitting,” where single symmetric peaks are used to represent either individual oxidation states or specific compounds. No meaningful conclusions can be drawn from these commonly employed two- or three-component peak fitting (2C and 3C) approaches; the implication being that a large portion of the literature that relies on this approach is flawed. As a significantly more accurate and reliable alternative to “chemistry fitting,” we also assess “envelope fitting” (using empirical multiplet structures) and examine its limitations when applying the approach to austenitic stainless steel data. A detailed comparison of these two fitting approaches is described in Part I. For other elements such as Cr 2p, the problems associated with using single components to represent oxidation states or compounds are not as severe. It was found that it does not impact binding energy measurements, but does influence relative intensities, which will have a flow-on effect for oxide thickness calculations and obtaining a correct understanding of the surface more broadly.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 teacher head, 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".