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Record W2584158256 · doi:10.1364/josab.34.000483

Laser cooling of solids under the influence of surface phonon polaritons

2017· article· en· W2584158256 on OpenAlexafffund
Galina Nemova, Raman Kashyap

Bibliographic record

VenueJournal of the Optical Society of America B · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOptical properties and cooling technologies in crystalline materials
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceYttriumLaserLaser coolingPolaritonSilicon carbideSurface phononWavelengthOpticsAluminiumPhononOptoelectronicsComposite materialMetallurgyCondensed matter physics

Abstract

fetched live from OpenAlex

Laser cooling of solids with anti-Stokes fluorescence in rare earth-doped low phonon samples, which support surface phonon polaritons (SPhP), placed in the vicinity of the sample supporting SPhP in the same or different wavelength region is considered in this paper. As an example, the laser cooling process in an ytterbium-doped yttrium aluminum garnet (Yb3+:YAG) sample placed near yttrium aluminum garnet (YAG) as well as silicon carbide (SiC) samples has been investigated. All Yb3+:YAG, YAG, and SiC samples can support SPhPs in different frequency ranges. It was shown that for short distances between samples, when SPhPs propagating in the sample undergoing laser cooling and SPhPs propagating in the next sample supported at room temperature are coupled, the laser cooling process can deteriorate substantially. In the opposite case, the SPhPs do not influence the cooling process significantly, even if the distance between the samples is less than the dominant wavelength of thermal radiation. The influence of coupled SPhPs on the laser cooling process in the case of samples with different sizes and for different distances between samples was investigated. These results are important for the development of optical solid state cryocoolers.

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 score0.154
Threshold uncertainty score0.529

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.015
GPT teacher head0.265
Teacher spread0.250 · 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
Published2017
Admission routes2
Has abstractyes

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Same venueJournal of the Optical Society of America BSame topicOptical properties and cooling technologies in crystalline materialsFrench-language works237,207