MétaCan
Menu
Back to cohort
Record W2918697859 · doi:10.1515/nanoph-2018-0202

Laser‐written colours on silver: optical effect of alumina coating

2018· article· en· W2918697859 on OpenAlexafffund
Jean‐Michel Guay, Antonio Calà Lesina, Graham Killaire, Peter G. Gordon, Choloong Hahn, Seán T. Barry, Lora Ramunno, Pierre Berini, Arnaud Weck

Bibliographic record

VenueNanophotonics · 2018
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsCarleton UniversityUniversity of Ottawa
FundersCanada Research ChairsUniversity of Ottawa
KeywordsMaterials scienceLaserHueCoatingDeposition (geology)OpticsColour differenceRadiometric datingRadiationStructural colorationLayer (electronics)OptoelectronicsNanotechnologyPhotonic crystalGeologyRemote sensingPhysics

Abstract

fetched live from OpenAlex

Abstract In this paper we discuss the optical response of laser‐written plasmonic colours on silver coated via the atomic layer deposition of alumina. These colours are due to nanoparticles distributed on a flat surface and on a surface with periodic topographical features (i.e. ripples). The colours are observed to shift with increasing alumina film thickness. The colours produced by surfaces with ripples recover their original vibrancy and hue after the deposition of film of thickness ~60 nm, while colours arising from flat surfaces gradually fade and never recover. Analysis of the surfaces identifies periodic topographical features to be responsible for this behaviour. Finite‐difference time‐domain simulations unravel the role played by the alumina thickness in colour formation and confirm the rotations and recovery of colours for increasing alumina thickness. The coloured surfaces were evaluated for applications in colourimetric and radiometric sensing showing large sensitivities of up to 3.06/nm and 3.19 nm/nm, respectively. The colourimetric and radiometric sensitivities are observed to be colour dependent.

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 score0.020
Threshold uncertainty score0.638

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 integrity0.0000.000
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 head0.005
GPT teacher head0.233
Teacher spread0.227 · 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

Citations12
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueNanophotonicsSame topicLaser Material Processing TechniquesFrench-language works237,207