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Record W2159861516 · doi:10.1002/anie.201200997

Significantly Improved Analytical Sensitivity of Lateral Flow Immunoassays by Using Thermal Contrast

2012· article· en· W2159861516 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.

Bibliographic record

VenueAngewandte Chemie International Edition · 2012
Typearticle
Languageen
FieldEngineering
TopicBiosensors and Analytical Detection
Canadian institutionsUniversity of Toronto
FundersDivision of Chemical, Bioengineering, Environmental, and Transport SystemsNational Institute of Allergy and Infectious DiseasesUniversity of MinnesotaMcKnight FoundationNational Institutes of HealthNational Science Foundation
KeywordsSensitivity (control systems)Materials scienceContrast (vision)ThermalNanoparticleColloidal goldFlow (mathematics)NanotechnologyBiomedical engineeringOpticsMedicineMathematicsElectronic engineering

Abstract

fetched live from OpenAlex

Heat beyond visual: The thermal contrast from the heating of gold nanoparticles upon laser stimulation can improve the analytical sensitivity of lateral flow assays (LFAs; see picture). A 32-fold improvement in sensitivity of an approved LFA for cryptococcal antigen (purple diamond) was shown, with the potential for further improvement by optimizing the backing material and the properties of the antibody-coated nanoparticles (red circle with blue Y).

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 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 score0.228
Threshold uncertainty score0.497

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 head0.227
Teacher spread0.216 · 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