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Record W2094062313 · doi:10.1063/1.2772175

Integrating optics and microfluidics for time-correlated single-photon counting in lab-on-a-chip devices

2007· article· en· W2094062313 on OpenAlexaff
Alison Cleary, Andrew Glidle, P.J.R. Laybourn, Sonia M. García‐Blanco, S. Pellegrini, Carole Helfter, Gerald S. Buller, J. Stewart Aitchison, Jonathan M. Cooper

Bibliographic record

VenueApplied Physics Letters · 2007
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of TorontoInstitut National d'Optique
Fundersnot available
KeywordsMicrofluidicsFluorophoreMaterials scienceCladding (metalworking)OptoelectronicsFluorescencePhoton countingOpticsSiliconChipPlanarNanotechnologyPhoton

Abstract

fetched live from OpenAlex

The authors describe the integration of low-loss optical waveguides with lab-on-a-chip structures to produce an integrated optical-microfluidic platform for time-correlated single-photon counting of fluorescent molecules. Waveguides were fabricated using electron beam densification of planar silica on silicon, eliminating any requirement for depositing upper cladding silica layers. Microfluidic channels were dry etched directly through the waveguides and the device was sealed using a poly(dimethylsiloxane) gasket. Time-resolved fluorescence lifetime measurements of the fluorophore nile blue were used as a model system to demonstrate the operation of the microfluidic device, with dye concentrations as low as 1.5nM (equivalent to <6000molecules) being measured.

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)0.001
metaresearch head score (Gemma)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.010
GPT teacher head0.213
Teacher spread0.203 · 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

Citations19
Published2007
Admission routes1
Has abstractyes

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