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Record W2236730953 · doi:10.1149/06902.0095ecst

(Invited) Bright Light Emitting Silicon/Germanium Nanostructures

2015· article· en· W2236730953 on OpenAlexaff
D. J. Lockwood

Bibliographic record

VenueECS Transactions · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSilicon Nanostructures and Photoluminescence
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSiliconMaterials scienceOptoelectronicsLight emissionGermaniumNanostructurePhotonicsBand gapNanotechnologyNanowireQuantum dot

Abstract

fetched live from OpenAlex

Light emission from Si nanostructures has been of great interest for some time now owing to the need for silicon-based light sources for applications in silicon opto-electronics and photonics. Both Si and Ge possess indirect band gaps, which makes them very inefficient light emitters. Band gap engineering employing quantum wells or quantum dots has been proposed as one way to overcome this limitation and Si/Ge or Si/SiGe-alloy thin-multilayer nanostructures grown on Si have been produced on this principle, and although light emission with greatly improved efficiency has been obtained at low temperatures the emission at room temperature is still very weak, because of exciton dissociation. Recent advances in band gap engineering technology have resulted in the development of new systems incorporating one-, two-, and three-dimensionally confined Si-Ge nanostructures that produce bright light in the near infrared. Here, the novel optical properties of three such nanostructures – super unit cells, nanowires, and nanocluster layers – are explored.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.035
Threshold uncertainty score0.117

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0350.020

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 head0.016
GPT teacher head0.235
Teacher spread0.220 · 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

Citations0
Published2015
Admission routes1
Has abstractyes

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