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Record W1994253222 · doi:10.1039/c3lc50107h

On-chip nanohole array based sensing: a review

2013· review· en· W1994253222 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

VenueLab on a Chip · 2013
Typereview
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaEidgenössische Technische Hochschule Zürich
KeywordsFabricationMicrofluidicsNanotechnologyPalette (painting)Context (archaeology)PlasmonMaterials scienceComputer scienceOptoelectronics

Abstract

fetched live from OpenAlex

The integration of nanohole array based plasmonic sensors into microfluidic systems has enabled the emergence of platforms with unique capabilities and a diversified palette of applications. Recent advances in fabrication techniques together with novel implementation schemes have influenced the progress of these optofluidic platforms. Here, we review the advances that nanohole array based sensors have experienced since they were first merged with microfluidics. We examine established and new fabrication methodologies that have enabled both the fabrication of nanohole arrays with improved optical attributes and a reduction in manufacturing costs. The achievements of several platforms developed to date and the significant benefits obtained from operating the nanoholes as nanochannels are also reviewed herein. Finally, we discuss future opportunities for on-chip nanohole array sensors by outlining potential applications and the use of the abilities of the nanostructures beyond the optical context.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.797
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0010.010

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 head0.053
GPT teacher head0.304
Teacher spread0.251 · 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