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Record W2146855606 · doi:10.1002/jbio.201200094

Detection of recombinant growth hormone by evanescent cascaded waveguide coupler on silica‐on‐silicon

2012· article· en· W2146855606 on OpenAlexaff
Jayan Ozhikandathil, Muthukumaran Packirisamy

Bibliographic record

VenueJournal of Biophotonics · 2012
Typearticle
Languageen
FieldEngineering
TopicPhotonic and Optical Devices
Canadian institutionsConcordia University
Fundersnot available
KeywordsWaveguideFinite-difference time-domain methodOpticsMaterials sciencePenetration depthBiosensorEvanescent waveOptoelectronicsPhysicsNanotechnology

Abstract

fetched live from OpenAlex

An evanescent wave based biosensor is developed on the silica-on-silicon (SOS) with a cascaded waveguide coupler for the detection of recombinant growth hormone. So far, U -bends and tapered waveguides are demonstrated for increasing the penetration depth and enhancing sensitivity of the evanescent wave sensor. In this work, a monolithically integrated sensor platform containing a cascaded waveguide coupler with optical power splitters and combiners designed with S -bends and tapper waveguides is demonstrated for an enhanced detection of recombinant growth hormone. In the cascaded waveguide coupler, a large surface area to bind the antibody with increased penetration depth of evanescent wave to excite the tagged-rbST is obtained by splitting the waveguide into multiple paths using Y splitters designed with s -bends and subsequently combining them back to a single waveguide through tapered waveguide and combiners. Hence a highly sensitive fluoroimmunoassay sensor is realized. Using the 2D FDTD (Finite-difference time-domain method) simulation of waveguide with a point source in Rsoft FullWAVE, the fluorescence coupling efficiency of straight and bend section of waveguide is analyzed. The sensor is demonstrated for the detection of fluorescently-tagged recombinant growth hormone with the detection limit as low as 25 ng/ml.

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 score0.033
Threshold uncertainty score0.631

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 head0.009
GPT teacher head0.216
Teacher spread0.207 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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