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Record W2792720422 · doi:10.1117/12.2292514

Laser cooling of solids: latest achievements and prospects

2018· article· en· W2792720422 on OpenAlexaff
Raman Kashyap, Galina Nemova

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOptical properties and cooling technologies in crystalline materials
Canadian institutionsPolytechnique Montréal
Fundersnot available
KeywordsLaserLaser coolingEngineering physicsComputer scienceMaterials scienceOptoelectronicsEnvironmental scienceEngineeringOpticsPhysics

Abstract

fetched live from OpenAlex

Laser cooling of solids, also known as optical refrigeration, is an area of optical science investigating the interaction of light with condensed matter to remove thermal energy of a solid through the interaction of the pump photons and phonons in a solid. Apart from being of fundamental scientific interest, this topic addresses a number of important practical issues such as the development of all solid state optical cryo-coolers, and biological applications. A short history of laser cooling as well as latest achievement of optical refrigeration in rare-earth (RE) doped macro-samples are presented and discussed in the paper. The main technique of laser cooling of RE doped solids based on anti-Stokes fluorescence is presented in this paper. The new approach to optical refrigeration based on the Raman cooling is also considered. It is shown that the future prospects of the research are connected with laser cooling of μm- and nm-sized samples, are in their applications in biophysics in the fundamental studies of low-temperature physics.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.014
GPT teacher head0.248
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations4
Published2018
Admission routes1
Has abstractyes

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