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Femtosecond Laser Assisted Fabrication of Visible Wavelength All-Dielectric Nano-Membrane Metasurfaces

2019· article· en· W2980427828 on OpenAlexaff
Martynas Beresna, Behrad Gholipour, Timothy Lee, Kevin F. MacDonald, Nikolay I. Zheludev, Gilberto Brambilla

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMetamaterialPlasmonMaterials scienceDielectricOptoelectronicsFemtosecondLaserNanoscopic scaleElectromagnetic radiationPhotonic metamaterialNanotechnologyRefractive indexUltravioletNano-OpticsPhysics

Abstract

fetched live from OpenAlex

Metamaterials allow control and tailoring the optical response of natural materials to achieve unprecedented functionalities. These artificial electromagnetic media are engineered by structuring materials on a subwavelength scale. Metamaterials have conventionally been made out of noble plasmonic metals. Intrinsically, plasmonic metamaterials suffer from high energy dissipation due to ohmic losses at ultraviolet to visible spectral frequencies. Therefore, in recent years, all-dielectric resonant metamaterials typically made from high-index dielectrics have been explored widely as they can potentially alleviate such losses, while allowing similar functionalities. One of the factors constraining their widespread use is slow and high-cost production techniques required to achieve nanoscale structures across large areas in reasonable timescales.

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 score0.004

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.017
GPT teacher head0.241
Teacher spread0.223 · 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

Citations1
Published2019
Admission routes1
Has abstractyes

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