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Record W2336977391 · doi:10.1038/srep15933

Nanorod Surface Plasmon Enhancement of Laser-Induced Ultrafast Demagnetization

2015· article· en· W2336977391 on OpenAlexafffund
Haitian Xu, Ghazal Hajisalem, G. M. Steeves, Reuven Gordon, B. C. Choi

Bibliographic record

VenueScientific Reports · 2015
Typearticle
Languageen
FieldPhysics and Astronomy
TopicMagnetic properties of thin films
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsNanorodMaterials scienceSurface plasmon resonanceLaserMagnetizationDemagnetizing fieldSurface plasmonFemtosecondUltrashort pulseKerr effectOptoelectronicsMagnetization dynamicsPlasmonOpticsThin filmLocalized surface plasmonMagnetic fieldNanotechnologyNanoparticlePhysics

Abstract

fetched live from OpenAlex

Ultrafast laser-induced magnetization dynamics in ferromagnetic thin films were measured using a femtosecond Ti:sapphire laser in a pump-probe magneto-optic Kerr effect setup. The effect of plasmon resonance on the transient magnetization was investigated by drop-coating the ferromagnetic films with dimensionally-tuned gold nanorods supporting longitudinal surface plasmon resonance near the central wavelength of the pump laser. With ~4% nanorod areal coverage, we observe a >50% increase in demagnetization signal in nanorod-coated samples at pump fluences on the order of 0.1 mJ/cm(2) due to surface plasmon-mediated localized electric-field enhancement, an effect which becomes more significant at higher laser fluences. We were able to qualitatively reproduce the experimental observations using finite-difference time-domain simulations and mean-field theory. This dramatic enhancement of ultrafast laser-induced demagnetization points to possible applications of nanorod-coated thin films in heat-assisted magnetic recording.

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 categoriesInsufficient payload (model declined to judge)
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 score0.044
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.233
Teacher spread0.212 · 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.

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

Citations22
Published2015
Admission routes2
Has abstractyes

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