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Record W2240781196 · doi:10.1149/1.3578022

(Luminescence and Display Materials Division Centennial Outstanding Achievement Award) Band Gap Luminescence from Nanometer Thick Si/SiO<sub>2</sub> Quantum Wells

2011· article· en· W2240781196 on OpenAlexafffund
D. J. Lockwood

Bibliographic record

VenueECS Transactions · 2011
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsNational Research Council CanadaInstitute for Microstructural Sciences
FundersNational Research Council Canada
KeywordsMaterials scienceBand gapOptoelectronicsQuantum wellLuminescenceSiliconQuantum dotPhotonicsAmorphous solidAmorphous siliconPotential wellNanocrystalline siliconNanotechnologyOpticsCrystalline siliconChemistryPhysicsLaser

Abstract

fetched live from OpenAlex

In opto-electronics and photonics, the severe disadvantage of an indirect band gap has limited the application of elemental silicon. Amongst a number of diverse approaches to engineering efficient light emission in silicon nanostructures, one system that has received considerable attention has been Si/SiO2 quantum wells. Engineering such structures has not been easy, because to observe the desired quantum confinement effects, the quantum well thickness has to be less than 5 nm. Nevertheless, such ultra thin structures have now been produced by a variety of techniques. The SiO2 layers are amorphous, but the silicon layers can range from amorphous through nanocrystalline to single-crystal form. The fundamental band gap of the quantum wells has been measured primarily by optical techniques and strong confinement effects have been observed. A number of theories based primarily on ab initio approaches have been developed to explain these results with varying degrees of success. A detailed comparison is made between theoretical and experimental determinations of the band gap in Si/SiO2 quantum wells.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.043
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.224
Teacher spread0.201 · 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.

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

Citations0
Published2011
Admission routes2
Has abstractyes

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