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Record W3006394313 · doi:10.1364/oe.385747

Multimodal plasmonic optical fiber grating aptasensor

2020· article· en· W3006394313 on OpenAlexaff
Maxime Lobry, Médéric Loyez, Eman M. Hassan, Karima Chah, Maria C. DeRosa, Erik Goormaghtigh, Ruddy Wattiez, Christophe Caucheteur

Bibliographic record

VenueOptics Express · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced Fiber Optic Sensors
Canadian institutionsCarleton University
FundersFonds De La Recherche Scientifique - FNRS
KeywordsRefractometryMulti-mode optical fiberFiber Bragg gratingBiosensorOptical fiberOpticsMaterials scienceMultiplexingFiber optic sensorSingle-mode optical fiberPlasmonOptoelectronicsNanotechnologyRefractive indexComputer scienceTelecommunicationsPhysics

Abstract

fetched live from OpenAlex

Tilted fiber Bragg gratings (TFBGs) are now a well-established technology in the scientific literature, bringing numerous advantages, especially for biodetection. Significant sensitivity improvements are achieved by exciting plasmon waves on their metal-coated surface. Nowadays, a large part of advances in this topic relies on new strategies aimed at providing sensitivity enhancements. In this work, TFBGs are produced in both single-mode and multimode telecommunication-grade optical fibers, and their relative performances are evaluated for refractometry and biosensing purposes. TFBGs are biofunctionalized with aptamers oriented against HER2 (Human Epidermal Growth Factor Receptor-2), a relevant protein biomarker for breast cancer diagnosis. In vitro assays confirm that the sensing performances of TFBGs in multimode fiber are higher or identical to those of their counterparts in single-mode fiber, respectively, when bulk refractometry or surface biosensing is considered. These observations are confirmed by numerical simulations. TFBGs in multimode fiber bring valuable practical assets, featuring a reduced spectral bandwidth for improved multiplexing possibilities enabling the detection of several biomarkers.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.194
Threshold uncertainty score1.000

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 head0.017
GPT teacher head0.230
Teacher spread0.213 · 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.

Study designSimulation or modeling
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

Citations39
Published2020
Admission routes1
Has abstractyes

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