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Record W2044563447 · doi:10.1063/1.4907712

Sub-nanosecond time-resolved near-field scanning magneto-optical microscope

2015· article· en· W2044563447 on OpenAlexafffund
J. Rudge, Haitian Xu, J. Kolthammer, Yang‐Ki Hong, B. C. Choi

Bibliographic record

VenueReview of Scientific Instruments · 2015
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsUniversity of Victoria
FundersBritish Columbia Knowledge Development Fund
KeywordsMicroscopeOpticsOptical microscopeScanning Hall probe microscopeNear-field scanning optical microscopeMaterials scienceResolution (logic)MicroscopyImage resolutionTemporal resolutionWavelengthNanosecondNear-field optics4Pi microscopeScanning electron microscopePhysicsConventional transmission electron microscopeLaserScanning transmission electron microscopy

Abstract

fetched live from OpenAlex

We report on the development of a new magnetic microscope, time-resolved near-field scanning magneto-optical microscope, which combines a near-field scanning optical microscope and magneto-optical contrast. By taking advantage of the high temporal resolution of time-resolved Kerr microscope and the sub-wavelength spatial resolution of a near-field microscope, we achieved a temporal resolution of ∼50 ps and a spatial resolution of <100 nm. In order to demonstrate the spatiotemporal magnetic imaging capability of this microscope, the magnetic field pulse induced gyrotropic vortex dynamics occurring in 1 μm diameter, 20 nm thick CoFeB circular disks has been investigated. The microscope provides sub-wavelength resolution magnetic images of the gyrotropic motion of the vortex core at a resonance frequency of ∼240 MHz.

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 score0.002

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 head0.253
Teacher spread0.239 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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