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Record W2332452545 · doi:10.1103/physrevb.86.245312

Plasmonic enhancement of the magneto-optical response of MnP nanoclusters embedded in GaP epilayers

2012· article· en· W2332452545 on OpenAlexaff
Gabriel Monette, Nima Nateghi, R. A. Masut, S. Francoeur, David Ménard

Bibliographic record

VenuePhysical Review B · 2012
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsPolytechnique MontréalRegroupement Québécois sur les Matériaux de Pointe
Fundersnot available
KeywordsMaterials scienceFaraday effectNanoclustersCondensed matter physicsMagnetizationSpectral line shapeMagnetic fieldMagnetic circular dichroismOpticsNuclear magnetic resonanceSpectral linePhysicsNanotechnology

Abstract

fetched live from OpenAlex

We report on the magneto-optical activity of MnP nanoclusters embedded in GaP epilayers and MnP thin film as a function of temperature, magnetic field, and wavelength in the near infrared and visible. The measured Faraday rotation originates from the ferromagnetic magnetization of the metallic MnP phase and exhibits a hysteretic behavior as a function of an externally applied magnetic field closely matching that of the magnetization. The Faraday rotation spectrum of MnP shows a magnetoplasmonic resonance whose energy depends on the MnP filling factor and surrounding matrix permittivity. At resonance, the measured rotary power for the epilayer systems increases by a factor of 2 compared to that of the MnP film in terms of degrees of rotation per MnP thickness for an applied magnetic field of 410 mT. We propose an effective medium model, which qualitatively reproduces the Faraday rotation and the magnetocircular dichroism spectra, quantitatively determines the spectral shift induced by variations in the MnP volume fraction, and demonstrates the influence of the shape and orientation distributions of ellipsoidal MnP nanoclusters on the magneto-optical activity and absorption spectra.

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)0.001
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 score0.012
Threshold uncertainty score0.351

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.035
GPT teacher head0.302
Teacher spread0.266 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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