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Record W2021686704 · doi:10.1039/c3lc41411f

Atomically flat symmetric elliptical nanohole arrays in a gold film for ultrasensitive refractive index sensing

2013· article· en· W2021686704 on OpenAlexafffund
Gabriela Andrea Cervantes Tellez, Saad S. M. Hassan, R. Niall Tait, Pierre Berini, Reuven Gordon

Bibliographic record

VenueLab on a Chip · 2013
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsCarleton UniversityUniversity of OttawaUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsRefractive indexMaterials scienceOpticsSurface plasmon resonanceMicrofluidicsSubstrate (aquarium)PlasmonHigh-refractive-index polymerEtching (microfabrication)RefractometryOptoelectronicsNanotechnologyNanoparticleLayer (electronics)

Abstract

fetched live from OpenAlex

Past works on refractive index sensing using nanohole arrays in metal films typically achieved a resolution of around 10(-4) to 10(-5) refractive index units (RIU), up to 10(-6) with complicated detection setups. This is an order of magnitude worse than commercial Kretschmann-based surface-plasmon resonance (SPR) sensors. Here, we demonstrate intensity-based bulk refractive index sensing in an aqueous environment with a resolution of 9.38 × 10(-8) refractive index units (RIU), showing for the first time comparable performance for nanohole SPR with Kretschmann-based SPR. This is achieved by the combination of three advances in the materials properties: (a) template stripping to achieve ultra-flat Au surfaces of ~0.2 nm roughness, (b) elliptical nanoholes to enhance transmission, and (c) a Cytop substrate to symmetrize the refractive index with the aqueous environment above the metal film. The simple optical microscope geometry and microfluidic integration used in this work is promising for multiplexed lab-on-chip analysis.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.613
Threshold uncertainty score0.883

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.236
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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations45
Published2013
Admission routes2
Has abstractyes

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