Interpretation of complex x-ray photoelectron peak shapes. I. Case study of Fe 2p3/2 spectra
Bibliographic record
Abstract
Analyzing transition metal XPS peaks is widely used to determine surface composition and chemistry. However, these peaks have a complex structure, which is still the subject of investigation. Fe 2p analysis is a case in point where the multiplet structure and many-electron-effects lead to peak shapes that cannot be analyzed using standard approaches. Examination of the literature reveals that one of the most widely used approaches to data reduction when processing Fe 2p3/2 spectra involves using symmetric two- or three-component peak fitting with each peak effectively acting to capture a single chemical species (chemistry fit) in the complex spectra. Herein, this approach is compared to an envelope fit approach using Biesinger multiplet components of known iron oxides to determine how effective these methods are in reproducing iron oxide composition. Mixed oxide and metal XPS Fe 2p spectra were synthesized using reference spectra collected experimentally. For the first time, the accuracy and differences between the two approaches are reported. It is demonstrated that no meaningful conclusions can be drawn using single symmetric peaks to analyze complex Fe 2p3/2 spectra, implying that a large portion of the literature is flawed. The envelope fit approach, however, is shown to provide useful information regarding oxide ratios in mixed iron oxide materials, though limitations do exist. A methodology for evaluating the quality of XPS analysis of Fe 2p3/2 spectra is proposed for benchmarking new submissions so that reviewers, authors, and editors can assess these submissions.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".