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Record W1564365265 · doi:10.1109/jlt.2015.2431912

High Resolution NIR TFBG-Assisted Biochemical Sensors

2015· article· en· W1564365265 on OpenAlexafffund
Violeta A. Márquez-Cruz, Jacques Albert

Bibliographic record

VenueJournal of Lightwave Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPrismSurface plasmon resonanceMaterials scienceOpticsFiber Bragg gratingGratingWavelengthSurface plasmonRangingOptoelectronicsResolution (logic)PlasmonReflection (computer programming)NanotechnologyNanoparticlePhysics

Abstract

fetched live from OpenAlex

Tilted fiber Bragg gratings (TFBGs) have shown to be a suitable tool for exciting surface plasmon waves in a comparable manner as in typical attenuated total reflection Kretschmann configurations. In this paper, we present a comparative analysis of the characteristics of prism and grating configurations for generating surface plasmon resonance (SPR) for 800 and 1550 nm wavelengths. Results indicate that longer wavelength SPR may present advantages in sensing. Recent label-free biochemical sensing results using NIR TFBGs with and without SPR coatings demonstrate limits of detection ranging from the nM to the pM range without the need for thermal stabilization.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.056
Threshold uncertainty score0.655

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.233
Teacher spread0.217 · 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

Citations61
Published2015
Admission routes2
Has abstractyes

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