Surface science insight note: Imaging X‐ray photoelectron spectroscopy
Bibliographic record
Abstract
Quantification of X‐ray photoelectron spectroscopy (XPS) data is often limited by the heterogeneous nature of the material surface. However, it is often the case that heterogeneous material contains areas within the analyzed area that are effectively homogeneous. In this Insight note, concepts, and methods used to analyze both XPS data are presented to extract both spatial and spectral information from heterogeneous surfaces. These concepts and methods are applied to a specific material surface that contains three chemical compounds separated spatially. The analysis entails converting XPS image data to spectral data and is designed to highlight the potential of XPS imaging in revealing compositional information correlation with spatial information. Properties of algorithms used to evaluate XPS images and spectra are described to outline their application to image data. A case study of an imaging XPS data set is presented that demonstrates how poor signal‐to‐noise images, where the signal is recorded for 4 s per image, are still open to analysis yielding useful information. Ultimately, the methods presented here will aid in interpreting complex XPS data obtained from spatially complex materials often obtained during extensive cycling, such as conventional or electrocatalysts.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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 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".