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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 OpenAlexafffund
Lidija Malic, Teodor Veres, Maryam Tabrizian

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations53
Published2008
Admission routes2
Has abstractyes

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