MétaCan
Menu
Back to cohort
Record W2046778708 · doi:10.1149/1.3116894

The Influence of Structural Ordering on Luminescence from Nitride- and Oxynitride-Passivated Silicon Nanoclusters

2009· article· en· W2046778708 on OpenAlexafffund
Patrick R. Wilson, Tyler Roschuk, Kayne Dunn, Matthew Betti, Jacek Wójcik, Peter Mascher

Bibliographic record

VenueECS Transactions · 2009
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster UniversityOntario Centres of Excellence
KeywordsNanoclustersMaterials scienceSiliconSilicon oxynitrideLuminescenceSilicon nitrideNanocrystalline siliconPhotoluminescenceSilicon oxideNitrideSilicon monoxideCrystalline siliconOptoelectronicsNanotechnologyLayer (electronics)Amorphous silicon

Abstract

fetched live from OpenAlex

Luminescent silicon nanoclusters have been formed in silicon-rich silicon nitride and silicon-rich silicon oxynitride films grown using inductively coupled plasma chemical vapour deposition. The luminescent properties and electronic structure of the films have been investigated through ultraviolet-excited photoluminescence and synchrotron-based X-ray absorption spectroscopy experiments at the Si K- and L3,2-edges, respectively. In the silicon-rich silicon nitride films, luminescence in the visible was observed from quantum confinement effects and inter-bandgap defect levels while X-ray absorption spectroscopy indicates a structural reordering of the silicon nanoclusters and nitride host matrix with increased silicon nanocluster phase separation, occurring at lower annealing temperatures in more silicon-rich films. Luminescence from the silicon-rich silicon oxynitride films appeared to be defect-related and only the silicon oxide and nitride host matrices exhibited structural reordering when annealed, while there was no change in the structure of the silicon nanoclusters.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.112
Threshold uncertainty score0.593

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.006
GPT teacher head0.221
Teacher spread0.215 · 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.

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

Citations1
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueECS TransactionsSame topicSilicon Nanostructures and PhotoluminescenceFrench-language works237,207