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Record W4390697397 · doi:10.1039/d3nh00412k

Magnetic field-responsive graphene oxide photonic liquids

2024· article· en· W4390697397 on OpenAlexafffund
Yi-Tao Xu, Amanda J. Ackroyd, Arash Momeni, Mohamed Oudah, Mark J. MacLachlan

Bibliographic record

VenueNanoscale Horizons · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsCanada Foundation for Innovation
KeywordsGraphenePhotonicsOxideMagnetic fieldMaterials scienceFerrofluidNanotechnologyField (mathematics)OptoelectronicsPhysics

Abstract

fetched live from OpenAlex

, introducing a secondary phase or external field) can affect the way they interact and assemble, thereby giving control over the physical properties of a dynamic system. Here, graphene oxide (GO) photonic liquids that respond to a magnetic field are demonstrated for the first time. Magnetic nanoparticles are used to provide a continuous magnetizable liquid environment around the GO liquid crystalline domains. In response to a magnetic field, the alignment of magnetic nanoparticles, coupled with the diamagnetic property of GO nanosheets, drives the reorientation and alignment of the nanosheets, enabling switchable photonic properties using a permanent magnet. This phenomenon is anticipated to be extendable to other relevant photonic systems of shape-anisotropic nanoparticles and may open up opportunities for developing GO-based optical materials and devices.

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 score0.002
Threshold uncertainty score0.006

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)0.0020.000

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 head0.249
Teacher spread0.243 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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