MétaCan
Menu
Back to cohort
Record W2980584211 · doi:10.1088/1361-6463/ab4ec0

Comparison of the effects of surface plasmon resonance and the transverse magneto-optic Kerr effect in magneto-optic plasmonic nanostructures

2019· article· en· W2980584211 on OpenAlexafffund
Conrad Rizal, P. O. Kapralov, Daria O. Ignatyeva, V. I. Belotelov, Simone Pisana

Bibliographic record

VenueJournal of Physics D Applied Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsYork University
FundersRussian Foundation for Basic ResearchCMC Microsystems
KeywordsMagnetoMagneto-optic Kerr effectPlasmonKerr effectSurface plasmon resonanceTransverse planeSurface plasmonNanostructureSurface plasmon polaritonResonance (particle physics)Materials scienceCondensed matter physicsOpticsPhysicsOptoelectronicsNanotechnologyNanoparticleMedicineMagnetAtomic physics

Abstract

fetched live from OpenAlex

Abstract In this letter, we report on the response of surface plasmon resonance (SPR, reflectance versus incident angle) and transverse magneto-optic Kerr effect (T-MOKE) versus incident angle) in two different magneto-optic-plasmonic (MOP) configurations: Ti/Au/Co/Au (Configuration A) and Ti/Ag/Co/Au (Configuration B) at the excitation wavelength of 780 nm in air. Configuration A includes a 35 nm Au layer and Configuration B includes a 35 nm Ag layer. Both configurations consist of a thin (4 or 8 nm) Co magneto-optic layer and showed an enhancement of the T-MOKE signal over the SPR signal. Configuration B showed higher SPR and T-MOKE responses over Configuration A, possibly due to the low loss and higher plasmonic properties of Ag over Au. The T-MOKE based sensor shows improvements in quality factor by over two times compared to that of SPR. The magneto-optic SPR sensitivity of the sensor obtained shows an improvement by three times over the SPR sensitivity, and this can be further improved by suitably modifying the configuration. These results are of importance to the development of enhanced MOP/plasmonic sensors.

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)0.001
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 score0.447
Threshold uncertainty score0.922

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.224
Teacher spread0.219 · 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

Citations18
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Physics D Applied PhysicsSame topicPlasmonic and Surface Plasmon ResearchFrench-language works237,207