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Record W2084280585 · doi:10.1149/1.3100625

The Effects of Deposition and Processing Parameters on the Electronic Structure and Photoluminescence from Nitride-Passivated Silicon Nanoclusters

2009· article· en· W2084280585 on OpenAlexafffund
Patrick R. Wilson, Tyler Roschuk, O. H. Y. Zalloum, 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 CanadaCanadian Institutes of Health Research
KeywordsNanoclustersPlasma-enhanced chemical vapor depositionPhotoluminescenceMaterials scienceSiliconSilicon nitrideChemical vapor depositionAnnealing (glass)NitrideLuminescenceAnalytical Chemistry (journal)OptoelectronicsNanotechnologyChemistryMetallurgy

Abstract

fetched live from OpenAlex

Silicon nanoclusters (Si-ncs) embedded in silicon nitride films have been studied to determine the effect that deposition and processing parameters have on their growth, luminescent properties, and electronic structure. Luminescence was observed from Si-ncs formed in silicon-rich silicon nitride films with a broad range of compositions and grown using three different chemical vapour deposition (CVD) based systems: plasma-enhanced CVD (PECVD), inductively coupled plasma CVD (ICP CVD), and electron cyclotron resonance PECVD (ECR PECVD). Photoluminescence experiments revealed broad, tunable emissions with peaks ranging from the near-infrared across the full visible spectrum. The emission energy was highly dependent on the film composition and changed only slightly with annealing, which primarily affected the emission intensity. X-ray absorption spectra at the Si K- and L3,2-edges exhibited composition dependent phase separation and structural re-ordering of the Si-ncs and silicon nitride host matrix under different post-deposition annealing conditions.

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 score0.009
Threshold uncertainty score0.435

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 studies0.0010.000
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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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

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