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Record W2795591524 · doi:10.1063/1.5020320

Cadmium zinc telluride as a mid-infrared variable retarder

2018· article· en· W2795591524 on OpenAlexafffund
William Fitzgerald, Saeid Taherion, F. Joseph Kumar, David Giles, Dennis K. Hore

Bibliographic record

VenueJournal of Applied Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicChalcogenide Semiconductor Thin Films
Canadian institutionsRedlen Technologies (Canada)University of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCadmium zinc telluridePolarization (electrochemistry)CadmiumCadmium telluride photovoltaicsMaterials scienceInfraredZincIndiumMercury cadmium tellurideOpticsOptoelectronicsChemistryPhysicsMetallurgy

Abstract

fetched live from OpenAlex

The electro-optic behavior of cadmium zinc telluride is examined in the mid-infrared region between 3 and 11 μm, for applied DC field strengths of up to 106 V/m. The measurements performed here include full characterization of the polarization state of the transmitted light by means of the Stokes vector. We demonstrate the suitability of this material for DC variable retarder applications such as those achieved by quarter- or half-wave retardation. A comparison of two different metallic coatings for electrodes, gold and indium, reveals important differences in performance that are attributed to the homogeneity of the field through the bulk of the crystal. We illustrate that, in the case of both metals, the same electro-optic coefficients are measured, but regions of higher and lower retardation result in significant depolarization in the case of gold. Such depolarization may adversely affect the contrast ratio in a light valve, or increase the voltage necessary for the operation of an arbitrary polarization state generator.

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 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 score0.194
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

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 head0.012
GPT teacher head0.217
Teacher spread0.206 · 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.

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

Citations1
Published2018
Admission routes2
Has abstractyes

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