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Record W1968148558 · doi:10.1364/josab.30.000051

Silicon quantum dot coated microspheres for microfluidic refractive index sensing

2012· article· en· W1968148558 on OpenAlexafffund
Yanyan Zhi, Torrey Thiessen, A. Meldrum

Bibliographic record

VenueJournal of the Optical Society of America B · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsWhispering-gallery waveMicrofluidicsMaterials scienceQuantum dotMicrosphereCapillary actionRefractive indexFluorescenceOptoelectronicsOptical fiberSiliconNanotechnologyOpticsDetection limitOptofluidicsChemistryResonator

Abstract

fetched live from OpenAlex

The electromagnetic resonances of optical microspheres—the so-called whispering gallery modes (WGMs)—can be used for refractometric sensing of surrounding fluids. Microspheres are attractive because they offer high sensitivity and can be utilized with fluorescent dyes or quantum dots. One issue with microspheres, however, is that they are difficult to integrate into microfluidic systems. Here, we develop a microfluidic structure that permits sensing applications using a single microsphere in a capillary. To achieve this, a microsphere formed on the end of a tapered fiber was first coated with fluorescent silicon quantum dots (QDs). The sphere was then inserted into a microcapillary and the fluorescence WGMs were monitored as different fluids were pumped through the channel. The sensitivity and detection limits for this sphere-in-a-capillary device were measured for several different QD film thicknesses and for two different microsphere sizes. Because of the relatively high-visibility mode structure, the sensitivity and detection limit can be defined by Fourier analysis of the free spectral range and WGM spectral shifts.

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 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 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: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.378

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 head0.011
GPT teacher head0.245
Teacher spread0.233 · 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 teacher head, 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

Citations17
Published2012
Admission routes2
Has abstractyes

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