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Record W2038022628 · doi:10.1364/oe.16.021305

Mechanical modulation method for ultrasensitive phase measurements in photonics biosensing

2008· article· en· W2038022628 on OpenAlexafffund
Sergiy Patskovsky, Mathieu Maisonneuve, Michel Meunier, Andrei V. Kabashin

Bibliographic record

VenueOptics Express · 2008
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOpticsPhase modulationRefractive indexModulation (music)Materials scienceSurface plasmon resonancePhase (matter)PhotonicsTotal internal reflectionSensitivity (control systems)Phase noisePhysicsNanotechnologyElectronic engineeringAcoustics

Abstract

fetched live from OpenAlex

A novel polarimetry methodology for phase-sensitive measurements in single reflection geometry is proposed for applications in optical transduction-based biological sensing. The methodology uses altering step-like chopper-based mechanical phase modulation for orthogonal s- and p- polarizations of light reflected from the sensing interface and the extraction of phase information at different harmonics of the modulation. We show that even under a relatively simple experimental arrangement, the methodology provides the resolution of phase measurements as low as 0.007 deg. We also examine the proposed approach using Total Internal Reflection (TIR) and Surface Plasmon Resonance (SPR) geometries. For TIR geometry, the response appears to be strongly dependent on the prism material with the best values for high refractive index Si. The detection limit for Si-based TIR is estimated as 10(-5) in terms Refractive Index Units (RIU) change. SPR geometry offers much stronger phase response due to a much sharper phase characteristics. With the detection limit of 3.2*10(-7) RIU, the proposed methodology provides one of best sensitivities for phase-sensitive SPR devices. Advantages of the proposed method include high sensitivity, simplicity of experimental setup and noise immunity as a result of a high stability modulation.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.653

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 head0.053
GPT teacher head0.304
Teacher spread0.251 · 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

Citations33
Published2008
Admission routes2
Has abstractyes

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