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Record W2920936908 · doi:10.1088/1361-6528/ab0435

Nanostructured metals for light-based technologies

2019· article· en· W2920936908 on OpenAlexafffund
Reuven Gordon

Bibliographic record

VenueNanotechnology · 2019
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePlasmonOptoelectronicsNanophotonicsNanolithographyLight emissionFemtosecondNonlinear opticsLaserTerahertz radiationNanotechnologyOpticsPhysicsFabrication

Abstract

fetched live from OpenAlex

The basic theoretical understanding of light interacting with nanostructured metals that has existed since the early 1900s has become more relevant in the last two decades, largely because of new approaches to structure metals down to the nanometer scale or smaller. Here, a broad overview of the concepts and applications of nanostructuring metals for light-based technologies is given. The theory of the response of metals to an applied oscillating field is given, including a discussion of nonlocal, nonlinear and quantum effects. Using this metal response, the guiding of electromagnetic (light) waves using metals is given, with a particular emphasis on the impact of nanostructured metals for tighter confinement and slower propagation. Similarly, the influence of metal nanostructures on light scattering by isolated metal structures, like nanoparticles and nanoantennas, is described, with basic results presented including plasmonic/circuit resonances, the single channel limit, directivity enhancement, the maximum power transfer theorem, limits on the magnetic response from kinetic inductance and the scaling of gap plasmons to the nanometer scale and smaller. A brief overview of nanofabrication approaches to creating metal nanostructures is given. Finally, existing and emerging light-based applications are presented, including those for sensing, spectroscopy (including local refractive index, Raman, IR absorption), detection (including Schottky detectors), switching (including terahertz photoconductive antennas), modulation, energy harvesting and photocatalysis, light emission (including lasers and tunneling based light emission), optical tweezing, nonlinear optics, subwavelength imaging and lithography and high density data storage.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.195
Threshold uncertainty score0.710

Codex and Gemma teacher scores by category

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 integrity0.0010.000
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 head0.009
GPT teacher head0.228
Teacher spread0.219 · 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

Citations25
Published2019
Admission routes2
Has abstractyes

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