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Record W2087976768 · doi:10.1155/2011/406425

A Camera Phone Localised Surface Plasmon Biosensing Platform towards Low-Cost Label-Free Diagnostic Testing

2011· article· en· W2087976768 on OpenAlexafffund
Philip J. R. Roche, Sandrine Filion-Côté, Maurice C.-K. Cheung, Vamsy P. Chodavarapu, Andrew G. Kirk

Bibliographic record

VenueJournal of Sensors · 2011
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsMcGill University
FundersFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsSurface plasmon resonanceSensitivity (control systems)SpectrometerMaterials sciencePhoneBiosensorColloidal goldPoint of careNanotechnologyComputer scienceComputer hardwareNanoparticleElectronic engineeringOpticsEngineeringPhysics

Abstract

fetched live from OpenAlex

Developmental work towards a camera phone diagnostic platform applying localized surface plasmon resonance (LSPR) label-free sensing is presented. The application of spherical gold nanoparticles and nanorods are considered and assessed against ease of application, sensitivity, and practicality for a sensor for the detection of CCL2 (chemokine ligand 2). The sensitivity of the platform is compared with that of a commercial UV/Vis spectrometer. The sensitivity of the camera phone platform is found to be 30% less than that of the commercial system for an equivalent incubation time, but approaches that of the commercial system as incubation time increases. This suggests that the application of LSPR sensing on a portable camera phone devices may be a highly effective label-free approach for point-of-care use as a low-cost diagnostic sensing tool in environments where dedicated equipment is not available.

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)0.000
metaresearch head score (Gemma)0.000
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: Methods · Consensus signal: Methods
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.037
GPT teacher head0.218
Teacher spread0.182 · 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
GenreMethods

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

Citations21
Published2011
Admission routes2
Has abstractyes

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