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Record W4289711336 · doi:10.1002/sia.7143

Applying monochromated silver X‐rays to the surface characterization of silicon‐containing materials

2022· article· en· W4289711336 on OpenAlexaff
Stuart R. Leadley, Michael B. Clark, Mayank Jhalaria

Bibliographic record

VenueSurface and Interface Analysis · 2022
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsDow Chemical (Canada)
Fundersnot available
KeywordsSiliconX-ray photoelectron spectroscopyAugerAuger electron spectroscopyBinding energyCharacterization (materials science)Materials scienceAnalytical Chemistry (journal)NanotechnologyChemistryChemical engineeringAtomic physicsPhysicsOptoelectronicsOrganic chemistry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.012
GPT teacher head0.273
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2022
Admission routes1
Has abstractyes

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