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Record W3024380334 · doi:10.1364/ao.394624

Degree of polarization of luminescence from GaAs and InP as a function of strain: a theoretical investigation

2020· article· lv· W3024380334 on OpenAlexaff
Daniel T. Cassidy, Jean-Pierre Landesman

Bibliographic record

VenueApplied Optics · 2020
Typearticle
Languagelv
FieldPhysics and Astronomy
TopicSemiconductor Quantum Structures and Devices
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOpticsLuminescencePolarization (electrochemistry)Materials scienceDegree of polarizationStrain (injury)Refractive indexOptoelectronicsPhysicsChemistryScatteringMedicine

Abstract

fetched live from OpenAlex

Experimentally, it is known that the degree of polarization (DOP) of luminescence is a sensitive function of strain in III–V materials. It has been assumed that DOP = − K e ( e 1 − e 2 ) and that the rotated degree of polarization = 2 K e e 6 , where K e is a positive calibration constant, e 1 and e 2 are the normal components of strain along perpendicular “1” and “2” directions, and e 6 = e 12 is the tensor shear strain. K e has been measured experimentally for GaAs and InP. In this paper, the results of a simple analytic determination of expressions for DOP as a function of strain are presented. Given the wide ranges reported for the strain deformation potentials b and d , it is not possible to give definitive and meaningful numerical values for expressions for DOP and K e . However, the sensitivity of DOP to strain suggests that it might be possible to design simple experiments to provide accurate values for the deformation potentials. The b and d deformation potentials might not be independent. For the results presented here and in the limit of isotropic material, an isotropic result for the DOP is found if d = 3 b .

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.022
GPT teacher head0.223
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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
Published2020
Admission routes1
Has abstractyes

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