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Record W2625446159 · doi:10.1002/pssa.201770135

Optical modulation using strain tunable metallo‐dielectric films (Phys. Status Solidi A 6∕2017)

2017· article· en· W2625446159 on OpenAlexaff
J. Kenji Clark, Nazir P. Kherani

Bibliographic record

Venuephysica status solidi (a) · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced Sensor and Energy Harvesting Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceMolar absorptivityDielectricOptoelectronicsPlasmonThin filmInfraredWavelengthModulation (music)MetamaterialScatteringOpticsNanotechnology

Abstract

fetched live from OpenAlex

Metal thin-films are used in a diversity of applications, which include single metal thin-film layers in plasmonic SPP sensors,multi-layer metallo-dielectrics in solar control coatings and the development of a wide variety of metamaterial devices. Such devices are nevertheless limited in their applicability due to the lack of active tunability in the properties of metallic layers. However, recent work on stretchable electronics shows the presence of active changes in the structure of metallic layers. The article by Clark and Kherani (article no.201600756) hypothesizes the viability of tuning the optical properties of metallo-dielectric films using a simple mechanica stimulus. Through the application of small strains the authors demonstrate the tunability of the visible light scattering; near-infrared absorptivity and transmissivity at specific wavelengths; mid-infrared absorptivity and transmissivity; and electrical conductivity. This first experimental and theoretical study establishes the basis for a potentially new field of strain tunable optical modulation in metal thin films.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.477
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.036
GPT teacher head0.282
Teacher spread0.246 · 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 designSimulation or modeling
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
Published2017
Admission routes1
Has abstractyes

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