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Record W1988882846 · doi:10.1002/pssc.201100606

Structural and optical optimization of ZnSe‐based laser heterostructures with graded index waveguide

2012· article· en· W1988882846 on OpenAlexfundno aff
S. V. Gronin, I. V. Sedova, S. V. Sorokin, G. V. Klimko, K. G. Belyaev, A. V. Lebedev, A. А. Ситникова, А. А. Торопов, S. V. Ivanov

Bibliographic record

VenuePhysica status solidi. C, Conferences and critical reviews/Physica status solidi. C, Current topics in solid state physics · 2012
Typearticle
Languageen
FieldEngineering
TopicSemiconductor Lasers and Optical Devices
Canadian institutionsnot available
FundersGlobal Institute for Water Security, University of Saskatchewan
KeywordsPhotoluminescenceHeterojunctionMaterials scienceLaserOptoelectronicsSuperlatticeWaveguideOpticsTransmission electron microscopyNanotechnologyPhysics

Abstract

fetched live from OpenAlex

Abstract We report on structural and photoluminescence (PL) studies of wide gap II‐VI laser heterostructures involving the graded index waveguide (GIW) based on short period Zn(Mg)SSe/ZnSe superlattices (SLs) and the active region comprising single or multiple electronically‐coupled CdSe/ZnSe QD sheets. Precise compensation of elastic stresses in the SL waveguide and optimization of the ZnSe/GaAs initial growth stage have resulted in good crystalline quality of the laser structures and reduction of the extended defect density down to 104 cm‐2. Express monitoring of the defect density by using the photoluminescence microscope was supported by transmission electron microscopy studies. PL data have demonstrated efficient transport of nonequilibrium carriers through the GIW SLs to the active region (© 2012 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 integrity0.0000.000
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 head0.038
GPT teacher head0.313
Teacher spread0.275 · 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 designSimulation or modeling
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

Citations11
Published2012
Admission routes1
Has abstractyes

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