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Record W2064765974 · doi:10.1021/ac900221y

Nanoholes As Nanochannels: Flow-through Plasmonic Sensing

2009· article· en· W2064765974 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

VenueAnalytical Chemistry · 2009
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsUniversity of VictoriaBC Cancer Agency
FundersBC Cancer AgencyNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsSurface plasmon resonanceNanofluidicsNanotechnologyPlasmonMonolayerChemistryBiosensorFlow (mathematics)MicrofluidicsSurface plasmonOptoelectronicsMaterials scienceNanoparticleMechanics

Abstract

fetched live from OpenAlex

We combine nanofluidics and nanoplasmonics for surface-plasmon resonance (SPR) sensing using flow-through nanohole arrays. The role of surface plasmons on resonant transmission motivates the application of nanohole arrays as surface-based biosensors. Research to date, however, has focused on dead-ended holes, and therefore failed to harness the benefits of nanoconfined transport combined with SPR sensing. The flow-through format enables rapid transport of reactants to the active surface inside the nanoholes, with potential for significantly improved time of analysis and biomarker yield through nanohole sieving. We apply the flow-through method to monitor the formation of a monolayer and the immobilization of an ovarian cancer biomarker specific antibody on the sensing surface in real-time. The flow-through method resulted in a 6-fold improvement in response time as compared to the established flow-over method.

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 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 score0.331
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.256
Teacher spread0.241 · 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