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Record W2623901937 · doi:10.1063/1.4984967

Absolute measurement of effective radiative-efficiency in GaAs grown with molecular-beam-epitaxy

2017· article· en· W2623901937 on OpenAlexafffund
Martin Gerber, R. N. Kleiman

Bibliographic record

VenueJournal of Applied Physics · 2017
Typearticle
Languageen
FieldEngineering
Topicsolar cell performance optimization
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of CanadaMcMaster University
KeywordsMolecular beam epitaxyRadiative transferPhotoluminescenceDeep-level transient spectroscopyDopingMaterials scienceSpectroscopyAnalytical Chemistry (journal)EpitaxyMolecular beamCharacterization (materials science)Atmospheric temperature rangeSpontaneous emissionChemistryOptoelectronicsOpticsPhysicsSiliconMoleculeNanotechnologyThermodynamics

Abstract

fetched live from OpenAlex

In this work, we have decoupled the radiative and nonradiative contributions to recombination by developing analytical models to fit observed non-monoexponential photoluminescence decay and bulk-lifetime temperature-dependence, which has enabled absolute measurement of the effective radiative efficiency and characterization of the dominant defect in lightly doped GaAs samples grown using molecular-beam-epitaxy. The measured effective radiative efficiency values under low level injection conditions range from (88.9 ± 0.1)% and (81.2 ± 0.1)% at 77 K to (0.028 ± 0.001)% and (0.034 ± 0.001)% at 700 K, with 300 K values of (17.3 ± 0.1)% and (10.5 ± 0.1)% in the p-type and n-type samples, respectively. A common defect-signature is observed in the temperature-dependence of the nonradiative lifetime in samples of both doping types, and our measured parameters are consistent with characteristics previously measured for the EL2 defect using deep-level transient spectroscopy.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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

Citations5
Published2017
Admission routes2
Has abstractyes

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