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Record W2026246847 · doi:10.1364/oe.20.009090

Simultaneous measurement of quality factor and wavelength shift by phase shift microcavity ring down spectroscopy

2012· article· en· W2026246847 on OpenAlexafffund
M. Imran Cheema, Simin Mehrabani, Ahmad Hayat, Yves-Alain Peter, Andrea M. Armani, Andrew G. Kirk

Bibliographic record

VenueOptics Express · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill UniversityNational Science Foundation
KeywordsCavity ring-down spectroscopyOpticsSpectroscopyWavelengthMaterials sciencePhase (matter)LaserOptical cavityOptoelectronicsPhysics

Abstract

fetched live from OpenAlex

Optical resonant microcavities with ultra high quality factors are widely used for biosensing. Until now, the primary method of detection has been based upon tracking the resonant wavelength shift as a function of biodetection events. One of the sources of noise in all resonant-wavelength shift measurements is the noise due to intensity fluctuations of the laser source. An alternative approach is to track the change in the quality factor of the optical cavity by using phase shift cavity ring down spectroscopy, a technique which is insensitive to the intensity fluctuations of the laser source. Here, using biotinylated microtoroid resonant cavities, we show simultaneous measurement of the quality factor and the wavelength shift by using phase shift cavity ring down spectroscopy. These measurements were performed for disassociation phase of biotin-streptavidin reaction. We found that the disassociation curves are in good agreement with the previously published results. Hence, we demonstrate not only the application of phase shift cavity ring down spectroscopy to microcavities in the liquid phase but also simultaneous measurement of the quality factor and the wavelength shift for the microcavity biosensors in the application of kinetics measurements.

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 score0.026
Threshold uncertainty score0.829

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 head0.022
GPT teacher head0.276
Teacher spread0.255 · 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

Citations49
Published2012
Admission routes2
Has abstractyes

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