Applying monochromated silver X‐rays to the surface characterization of silicon‐containing materials
Bibliographic record
Abstract
Although the use of a monochromated silver X‐ray source for X‐ray photoelectron spectroscopy (XPS) analysis was first reported in the 1980s, it has found limited application. Two important advances have been made in recent years that now enable it to be used more routinely for surface analysis. These advancements are the development of relative sensitivity factors for quantification and the fully motorized dual silver/aluminum X‐ray source, which enables the change to monochromated X‐ray energies in a few minutes versus hours. Silicones are an important class of materials that have unique chemical properties that can vary from liquids to glass‐like materials. XPS, with monochromatic Al Kα X‐rays, is used routinely to characterize silicon‐containing materials and to identify different silicone functional groups. This was aided by the work published on the analysis of Si 2p peaks acquired from polysiloxane materials. The binding energy increases nonlinearly when methyl groups on a silicon atom are replaced by oxygen atoms. This work will now be extended to determine the binding energy shifts in Si 1s, 2s, and Auger spectra, which will allow the identification of silicon chemistry when using monochromated silver X‐rays. The Auger parameters of the different polysiloxanes will be compared to those of other silicon‐containing materials. Comparison of the depth of analysis in silicon‐containing materials when changing from aluminum to silver X‐rays will also be addressed.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".