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Record W2006390249 · doi:10.1364/oe.20.026969

Simultaneous retrieval of fluidic refractive index and surface adsorbed molecular film thickness using silicon wire waveguide biosensors

2012· article· en· W2006390249 on OpenAlexafffund
Yuki Atsumi, Dan‐Xia Xu, A. Delâge, Jens H. Schmid, Martin Vachon, Pavel Cheben, Siegfried Janz, Nobuhiko Nishiyama, Shigehisa Arai

Bibliographic record

VenueOptics Express · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council Canada
FundersJapan Society for the Promotion of ScienceNational Research Council CanadaMinistry of Education, Culture, Sports, Science and Technology
KeywordsFree spectral rangeRefractive indexMaterials scienceResonatorWaveguideOpticsSiliconBiosensorFluidicsSilicon photonicsPhotonicsOptoelectronicsNanotechnologyPhysics

Abstract

fetched live from OpenAlex

For silicon wire based ring resonator biosensors, we investigate the simultaneous retrieval of changes in the fluidic refractive index ∆n(c) and surface adsorbed molecular film thickness ∆d(F). This can be achieved by monitoring the resonance shifts of the sensors operating in the TE and TM polarizations at the same time. Although this procedure is straightforward in principle, significant retrieval errors can be introduced due to deviations in the sensor waveguide cross-sections from their nominal values in the range commonly encountered for silicon photonic wire devices. We propose a method of determining the fabricated waveguide size using the group indices derived from measured free spectral range (FSR) of the resonators. We further demonstrate that using experimentally measured group index values, the waveguide size can be determined to accuracies of ± 2 nm in width and ± 1 nm in height. By using this procedure, ∆n(c) and ∆d(F) can be obtained to a precision of within 10% of the true values using optically measurable parameters, improving the retrieval accuracy by more than 3 times.

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 score0.428
Threshold uncertainty score0.916

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 head0.241
Teacher spread0.230 · 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

Citations14
Published2012
Admission routes2
Has abstractyes

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