MétaCan
Menu
Back to cohort
Record W2013572940 · doi:10.1002/sia.2036

Fick's law of diffusion depth profiles applied to the degradation of oxidized polystyrene during ARXPS analysis

2005· article· en· W2013572940 on OpenAlexafffund
M. Ménard, R.W. Paynter

Bibliographic record

VenueSurface and Interface Analysis · 2005
Typearticle
Languageen
FieldMaterials Science
TopicElectron and X-Ray Spectroscopy Techniques
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsPolystyreneDiffusionX-ray photoelectron spectroscopyOxygenAnalytical Chemistry (journal)IrradiationChemistryRadicalPolymerHeliumMaterials scienceNuclear magnetic resonanceOrganic chemistryThermodynamics

Abstract

fetched live from OpenAlex

Abstract Angle‐resolved x‐ray photoelectron spectroscopy (ARXPS) measurements were made, using Al Kα and Mg Kα radiation alternately, on a polystyrene sample that had been exposed to a helium plasma. It was observed that oxygen was introduced into the sample surface by the plasma treatment, and that some of it was lost over a period of 5 h under x‐ray irradiation in the vacuum of the spectrometer. Laplace transforms of Fick's law of diffusion profiles were derived and applied to the data. The ARXPS results obtained in this study are consistent with a sample history in which the oxidation of the polymer surface resulting from exposure to plasma is controlled by a diffusion process, whereas the loss of oxygen during exposure to x‐rays is principally controlled by a first‐order reaction such as the liberation of oxygen (presumably as CO 2 ) from carbon–oxygen groups by the action of radicals created by the ionizing radiation. Copyright © 2005 John Wiley & Sons, Ltd.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.008
GPT teacher head0.266
Teacher spread0.258 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations13
Published2005
Admission routes2
Has abstractyes

Explore more

Same venueSurface and Interface AnalysisSame topicElectron and X-Ray Spectroscopy TechniquesFrench-language works237,207