Speciation of Magnesium Surfaces by X‐Ray Photoelectron Spectroscopy (XPS)
Bibliographic record
Abstract
ABSTRACT Magnesium metal, alloys, and salts have ubiquitous uses as structural materials, components in energy‐conversion devices, and catalysts. The surface states and reactions that underpin these applications are commonly studied by X‐ray photoelectron spectroscopy (XPS). Yet, reported binding energies for the Mg 2p transition vary widely, making accurate speciation of magnesium surfaces by XPS notoriously challenging. We found that these literature discrepancies result from differences between charge referencing procedures, particularly due to the unusually high binding energy of adventitious carbon on MgO and Mg(OH) 2 . Relevant pure and mixed samples were analyzed to assess the chemical speciation for magnesium. The range of Mg 2p binding energies was only 1.2 eV for pure samples, insufficient for speciation in many cases. The Mg KLL spectral lines were studied and found to provide additional chemical information due to final‐state effects. The range of the modified Auger parameter ( α ’), which is also independent of charging, was found to be 2.9 eV. The position and shape of the Mg KLL spectral lines are reported, and their use for the speciation of mixed systems is demonstrated. Moreover, a curve‐fitting procedure for O 1s signals was developed, which can separate MgO and Mg(OH) 2 despite their overlapping Mg 2p signals.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".