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Record W2092392610 · doi:10.1063/1.1835566

Room temperature oxidation kinetics of Si nanoparticles in air, determined by x-ray photoelectron spectroscopy

2004· article· en· W2092392610 on OpenAlexafffund
De‐Quan Yang, Jean-Numa Gillet, Michel Meunier, E. Sacher

Bibliographic record

VenueJournal of Applied Physics · 2004
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsX-ray photoelectron spectroscopyNanoparticleMaterials scienceTransmission electron microscopyKineticsAnalytical Chemistry (journal)OxideExcimer laserSpectroscopySiliconScanning electron microscopeDiffusionOxygenChemical engineeringNanotechnologyChemistryLaserOpticsOptoelectronicsMetallurgy

Abstract

fetched live from OpenAlex

The air oxidation kinetics of low coverages of ∼5nm Si nanoparticles, deposited by pulsed excimer laser ablation (KrF, 248 nm) in He, have been characterized by x-ray photoelectron spectroscopy. A simple model, based on the evolution of the Si 2p spectral components during oxidation, has been developed to determine the nanoparticle oxide thickness. It is found that the short-term oxide thickness is greater, and the long-term room-temperature air oxidization rate of these nanoparticles is less, than those reported for bulk a-Si and c-Si. The results are also consistent with an earlier transmission electron microscope observation of the oxidation of larger Si particles at higher temperatures. The greater short-term oxide thickness may be attributed to surface defects on the prepared Si nanoparticles, and lower long-term oxidation rate is due to the nonlinear decrease of oxygen diffusion in spherical systems.

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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.005
GPT teacher head0.219
Teacher spread0.214 · 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

Citations102
Published2004
Admission routes2
Has abstractyes

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