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Record W2046977691 · doi:10.1364/oe.19.017575

Optical bio-chemical sensors on SNOW ring resonators

2011· article· en· W2046977691 on OpenAlexaff
Mohammadreza Khorasaninejad, Nigel J. Clarke, M. P. Anantram, Simarjeet S. Saini

Bibliographic record

VenueOptics Express · 2011
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceResonatorRefractive indexSilicon on insulatorOpticsSiliconOptoelectronicsWaveguideMonolayerSilicon photonicsNanowireNanotechnologyPhysics

Abstract

fetched live from OpenAlex

In this paper, we propose and analyze novel ring resonator based bio-chemical sensors on silicon nanowire optical waveguide (SNOW) and show that the sensitivity of the sensors can be increased by an order of magnitude as compared to silicon-on-insulator based ring resonators while maintaining high index contrast and compact devices. The core of the waveguide is hollow and allows for introduction of biomaterial in the center of the mode, thereby increasing the sensitivity of detection. A sensitivity of 243 nm/refractive index unit (RIU) is achieved for a change in bulk refractive index. For surface attachment, the sensor is able to detect monolayer attachments as small as 1 Å on the surface of the silicon nanowires.

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 score0.816
Threshold uncertainty score0.800

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 head0.019
GPT teacher head0.220
Teacher spread0.201 · 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

Citations24
Published2011
Admission routes1
Has abstractyes

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