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Record W1970157228 · doi:10.1063/1.4757968

Nano-islands integrated evanescence-based lab-on-a-chip on silica-on-silicon and polydimethylsiloxane hybrid platform for detection of recombinant growth hormone

2012· article· en· W1970157228 on OpenAlex

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

VenueBiomicrofluidics · 2012
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsConcordia University
FundersMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsPolydimethylsiloxaneSiliconMicrofluidicsBiosensorMaterials scienceNanotechnologyNano-Realization (probability)Lab-on-a-chipChipFinite-difference time-domain methodOptoelectronicsComputer scienceOpticsPhysics

Abstract

fetched live from OpenAlex

Integration of nano-materials in optical microfluidic devices facilitates the realization of miniaturized analytical systems with enhanced sensing abilities for biological and chemical substances. In this work, a novel method of integration of gold nano-islands in a silica-on-silicon-polydimethylsiloxane microfluidic device is reported. The device works based on the nano-enhanced evanescence technique achieved by interacting the evanescent tail of propagating wave with the gold nano-islands integrated on the core of the waveguide resulting in the modification of the propagating UV-visible spectrum. The biosensing ability of the device is investigated by finite-difference time-domain simulation with a simplified model of the device. The performance of the proposed device is demonstrated for the detection of recombinant growth hormone based on antibody-antigen interaction.

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 categoriesMeta-epidemiology (narrow)
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 score0.006
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 head0.012
GPT teacher head0.210
Teacher spread0.199 · 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