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Record W2318318700 · doi:10.1021/la304287v

Adapting Fluorescence Resonance Energy Transfer with Quantum Dot Donors for Solid-Phase Hybridization Assays in Microtiter Plate Format

2013· article· en· W2318318700 on OpenAlexafffund
Eleonora Petryayeva, W. Russ Algar, Ulrich J. Krull

Bibliographic record

VenueLangmuir · 2013
Typearticle
Languageen
FieldMaterials Science
TopicQuantum Dots Synthesis And Properties
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Toronto MississaugaNew Mexico Department of Transportation
KeywordsFörster resonance energy transferMicrotiter plateAlexa FluorDetection limitQuantum dotFluorescenceChemistryOligonucleotideAnalytical Chemistry (journal)Peptide nucleic acidChromatographyNucleic acidMaterials scienceDNANanotechnologyOptics

Abstract

fetched live from OpenAlex

Methods have been developed for the solid-phase detection of nucleic acids using mixed films of quantum dots (QDs) and oligonucleotide probes in microtiter plates. Polystyrene microwells were functionalized with multidentate imidazole ligands to immobilize QDs. Oligonucleotide hybridization was transduced using QDs as donors in fluorescence resonance energy transfer (FRET). One detection channel paired green-emitting QD donors with Cy3 acceptors and served as an internal standard. A second detection channel paired red-emitting QDs with Alexa Fluor 647 acceptors and served as the primary detection channel. A selective assay for multiple targets was demonstrated using a 96-well plate format, which combined the advantages of two-plex QD-FRET with the high-throughput capability and convenience of microtiter plates. The assay had excellent resistance to the nonspecific adsorption of DNA and discriminated between fully complementary and single base-pair mismatched sequences with a contrast ratio >2. Under optimal conditions for a single color (green QD) assay format, the limit of detection (LOD) was 4 nM, and the dynamic range was from 20 to 300 nM. In a two-color assay, the detection channel (red QD) exhibited linear response between 4 and 100 nM and a LOD of 4 nM.

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 score0.035
Threshold uncertainty score0.512

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 communication0.0000.001
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 head0.020
GPT teacher head0.250
Teacher spread0.231 · 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

Citations32
Published2013
Admission routes2
Has abstractyes

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