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Record W1999805691 · doi:10.1039/b814697g

Two-dimensional droplet-based surface plasmon resonance imaging using electrowetting-on-dielectric microfluidics

2008· article· en· W1999805691 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 · 2008
Typearticle
Languageen
FieldEngineering
TopicElectrowetting and Microfluidic Technologies
Canadian institutionsMcGill UniversityNational Research Council Canada
FundersNational Research Council CanadaFonds Québécois de la Recherche sur la Nature et les TechnologiesSteadman Philippon Research Institute
KeywordsElectrowettingMicrofluidicsDigital microfluidicsSurface plasmon resonanceNanotechnologyMaterials scienceDielectricOptoelectronicsNanoparticle

Abstract

fetched live from OpenAlex

This article presents a multichannel droplet-based surface plasmon resonance platform. The platform comprises a digital electrowetting-on-dielectric (EWOD) microfluidic device coupled to surface plasmon resonance imaging (SPRi). SPRi is now a well-established detection technique that enables in-situ monitoring of multiple reactions occurring at the surface of the chip without the use of labels. Currently, the limiting factor in the application of SPRi for high-throughput applications is the flow-cell technology which relies on sequential sample processing within the continuous fluid flow. An original solution compared to the continuous flow-cell technology is proposed to increase the capability of existing SPRi technology. A parallel SPRi detection of different samples on the surface is achieved using the array-based digital microfluidic device.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.018
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
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.215
Teacher spread0.205 · 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