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Record W2769754743 · doi:10.1364/aop.9.000891

Sensing with periodic nanohole arrays

2017· article· en· W2769754743 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvances in Optics and Photonics · 2017
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceOpticsFocus (optics)Near and far fieldSensitivity (control systems)Refractive indexRigorous coupled-wave analysisOptoelectronicsPhysicsElectronic engineering

Abstract

fetched live from OpenAlex

In this paper we review the resonance conditions of periodic indentations in metallic layers and evaluate their potential for surface sensing of analytes. A review of significant contributions of nanohole arrays for sensing is presented in a first section. It is then followed by a theoretical analysis of their optical properties using coupled mode theory and an evaluation of their potential for sensing. The sensitivity, resolution, and field distribution are presented as a function of the different parameters of the metal film (periodicity, hole size, and thickness) to determine the optimal design for sensing. The focus of this paper is made on 1-D nanoslit arrays and 2-D square nanohole arrays to identify general considerations regarding sensing experiments using these types of structure. We include a MATLAB user interface, also available as a standalone application, that plots the transmission and reflection spectrum as well as the field distribution of nanohole arrays.

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.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.384

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 head0.010
GPT teacher head0.252
Teacher spread0.242 · 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