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Record W2092436684 · doi:10.1063/1.4871015

Excitation mechanism and thermal emission quenching of Tb ions in silicon rich silicon oxide thin films grown by plasma-enhanced chemical vapour deposition—Do we need silicon nanoclusters?

2014· article· en· W2092436684 on OpenAlexaff
A. Podhorodecki, Lukasz W Golacki, G. Zatryb, J. Misiewicz, J. Wang, Wojciech M. Jadwisienczak, Kamil Fedus, J. Wójcik, Patrick R. Wilson, Peter Mascher

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsMcMaster University
FundersNational Science Foundation
KeywordsSiliconAnalytical Chemistry (journal)Materials scienceNanoclustersSilicon oxidePhotoluminescenceChemical vapor depositionCathodoluminescenceFourier transform infrared spectroscopyThin filmSpectroscopyQuenching (fluorescence)Electron cyclotron resonanceIonChemistryLuminescenceOptoelectronicsSilicon nitrideNanotechnologyFluorescenceChemical engineeringOptics

Abstract

fetched live from OpenAlex

In this work, we will discuss the excitation and emission properties of Tb ions in a Silicon Rich Silicon Oxide (SRSO) matrix obtained at different technological conditions. By means of electron cyclotron resonance plasma-enhanced chemical vapour deposition, undoped and doped SRSO films have been obtained with different Si content (33, 35, 39, 50 at. %) and were annealed at different temperatures (600, 900, 1100 °C). The samples were characterized optically and structurally using photoluminescence (PL), PL excitation, time resolved PL, absorption, cathodoluminescence, temperature dependent PL, Rutherford backscattering spectrometry, Fourier transform infrared spectroscopy and positron annihilation lifetime spectroscopy. Based on the obtained results, we discuss how the matrix modifications influence excitation and emission properties of Tb ions.

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 score0.015
Threshold uncertainty score0.981

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.214
Teacher spread0.208 · 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

Citations13
Published2014
Admission routes1
Has abstractyes

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