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Record W2044797432 · doi:10.1364/boe.5.002481

Self-referenced spectroscopy using plasmon waveguide resonance biosensor

2014· article· en· W2044797432 on OpenAlexaff
Farshid Bahrami, Mathieu Maisonneuve, Michel Meunier, J. Stewart Aitchison, Mo Mojahedi

Bibliographic record

VenueBiomedical Optics Express · 2014
Typearticle
Languageen
FieldEngineering
TopicPlasmonic and Surface Plasmon Research
Canadian institutionsPolytechnique MontréalUniversity of Toronto
Fundersnot available
KeywordsSurface plasmon resonanceMaterials scienceRefractive indexBiosensorSpectroscopyOpticsSurface plasmonPolarization (electrochemistry)OptoelectronicsMulti-mode optical fiberResonance (particle physics)WaveguidePlasmonOptical fiberNanotechnologyChemistryPhysicsNanoparticle

Abstract

fetched live from OpenAlex

A plasmon waveguide resonance (PWR) sensor is designed, fabricated, and tested for self-referenced biosensing. The PWR sensor is able to support two different polarizations, TM and TE. The TM polarization has a large sensitivity to variations in the background refractive index while the TE polarization is more sensitive to the surface properties. The ability of the PWR sensor to simultaneously operate in both TM and TE modes is used to decouple the background index variations (bulk effects) from the changes in adlayer thickness (surface effects) via multimode spectroscopy. To benchmark the performance of the PWR, a conventional surface plasmon resonance (SPR) sensor is fabricated and tested under the same conditions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.259
Teacher spread0.240 · 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 source (direct Gemma or distilled Codex), 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

Citations28
Published2014
Admission routes1
Has abstractyes

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