MétaCan
Menu
Back to cohort
Record W2078785316 · doi:10.1155/2008/725967

Sensitive Label‐Free Biomolecular Detection Using Thin Silicon Waveguides

2008· article· en· W2078785316 on OpenAlex
A. Densmore, Dan‐Xia Xu, Siegfried Janz, P. Waldron, J. Lapointe, T. Mischki, Gregory P. Lopinski, A. Delâge, Jens H. Schmid, Pavel Cheben

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueAdvances in Optical Technologies · 2008
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular SciencesInstitute for Microstructural Sciences
FundersNational Research Council Canada
KeywordsSiliconNanotechnologyMaterials scienceOptoelectronicsComputer science

Abstract

fetched live from OpenAlex

We review our work developing optical waveguide‐based evanescent field sensors for the label‐free, specific detection of biological molecules. Using high‐index‐contrast silicon photonic wire waveguides of submicrometer dimension, we demonstrate ultracompact and highly sensitive molecular sensors compatible with commercial spotting apparatus and microfluidic‐based analyte delivery systems. We show that silicon photonic wire waveguides support optical modes with strong evanescent field at the waveguide surface, leading to strong interaction with surface bound molecules for sensitive response. Furthermore, we present new sensor geometries benefiting from the very small bend radii achievable with these high‐index‐contrast waveguides to extend the sensing path length, while maintaining compact size. We experimentally demonstrate the sensor performance by monitoring the adsorption of protein molecules on the waveguide surface and by tracking small refractive index changes of bulk solutions.

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.

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 score0.234
Threshold uncertainty score0.704

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 head0.015
GPT teacher head0.253
Teacher spread0.238 · 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