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Record W4312154086 · doi:10.1364/oe.477731

Emission and sensing of high-frequency terahertz electric fields using a GaSe crystal

2022· article· en· W4312154086 on OpenAlexafffund
B. N. Carnio, Meng Zhang, Peter G. Schunemann, Oussama Moutanabbir, A. Y. Elezzabi

Bibliographic record

VenueOptics Express · 2022
Typearticle
Languageen
FieldEngineering
TopicTerahertz technology and applications
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaDefence Research and Development Canada
KeywordsTerahertz radiationElectric fieldOpticsCrystal (programming language)Polarization (electrochemistry)Materials scienceTerahertz spectroscopy and technologyOptoelectronicsPhysicsChemistryComputer science

Abstract

fetched live from OpenAlex

A GaSe crystal cut along the (001) crystallographic plane is investigated for the emission and detection of high-frequency (i.e. up to ∼20 THz) electric fields. To date, a comprehensive analysis on high-frequency difference frequency generation and electro-optic sensing in GaSe has not been performed and should consider aspects such as electric field polarization orientation, symmetries inherent to the crystal structure, and the various possible generation and detection phase-matching arrangements. Herein, terahertz radiation generation is investigated for various excitation electric field polarizations as the GaSe crystal is rotated in the (001) plane. Subsequently, the crystal is rotated out-of-plane to investigate the difference frequency generation and electro-optic sampling phase-matching conditions for various arrangements. The measured terahertz radiation spectra show peak generation at the frequencies of 10, 16, and 18 THz (dependent on the GaSe crystal orientation), in agreement with the frequencies exhibiting perfect phase-matching. GaSe has the potential to emerge as the primary crystal for the emission and detection of high-frequency electric fields, such that this comprehensive analysis is necessary for the widespread adoption and practical implementation of GaSe as a high-frequency source crystal.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.574
Threshold uncertainty score0.333

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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

Citations14
Published2022
Admission routes2
Has abstractyes

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