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Record W4316034760 · doi:10.1515/nanoph-2022-0502

Spin–orbit coupling induced by ascorbic acid crystals

2023· article· en· W4316034760 on OpenAlexafffund
Florence Grenapin, Alessio D’Errico, Ebrahim Karimi

Bibliographic record

VenueNanophotonics · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOrbital Angular Momentum in Optics
Canadian institutionsNational Research Council CanadaUniversity of Ottawa
FundersCanada First Research Excellence FundCanada Research Chairs
KeywordsAscorbic acidSpin–orbit interactionNanomaterialsCoupling (piping)Spin (aerodynamics)Materials scienceCondensed matter physicsChemical physicsOrbit (dynamics)NanotechnologyChemistryPhysicsComposite materialAerospace engineering

Abstract

fetched live from OpenAlex

Some anisotropic materials form semicrystalline structures, called spherulites, when observed in a polarisation microscope, exhibit a characteristic "maltese-cross"-like pattern. While this observation has been hitherto considered as a tool to characterize these materials, we show that these patterns are associated with a strong light's spin-orbit coupling induced by the spherulite structures. We experimentally demonstrate these effects using samples of crystallized ascorbic acid and observing the creation of optical vortices in transmitted laser beams, as well as the formation of inhomogeneous polarisation patterns. Our findings suggest the use of some spherulites based on other materials in frequency ranges, e.g. in the THz domain, where polarisation and spatial shaping of electromagnetic radiation is still a challenging task.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.014
GPT teacher head0.260
Teacher spread0.247 · 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 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

Citations2
Published2023
Admission routes2
Has abstractyes

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