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Record W2079579001 · doi:10.1088/2050-6120/2/1/015002

Energy transfer between a biological labelling dye and gold nanorods

2013· article· en· W2079579001 on OpenAlexaff
Chris Racknor, Mahi R. Singh, Yinan Zhang, David J. S. Birch, Yu Chen

Bibliographic record

VenueMethods and Applications in Fluorescence · 2013
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsWestern University
FundersBiotechnology and Biological Sciences Research CouncilEngineering and Physical Sciences Research Council
KeywordsNanorodAlexa FluorFluorescenceEnergy transferMaterials scienceNanotechnologyDipoleFörster resonance energy transferMicroscopyChemistryOpticsChemical physicsPhysics

Abstract

fetched live from OpenAlex

We have demonstrated energy transfer between a biological labelling dye (Alexa Fluor 405) and gold nanorods experimentally and theoretically. The fluorescence lifetime imaging microscopy and density matrix method are used to study a hybrid system of dye and nanorods under one- and two-photon excitations. Energy transfer between dye and nanorods via the dipole-dipole interaction is found to cause a decrease in the fluorescence lifetime change. Enhanced energy transfer from dye to nanorods is measured in the presence of an increased density of nanorods. This study has potential applications in fluorescence lifetime-based intra-cellular sensing of bio-analytes as well as nuclear targeting cancer therapy.

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: none
Teacher disagreement score0.301
Threshold uncertainty score0.363

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 head0.022
GPT teacher head0.335
Teacher spread0.313 · 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

Citations27
Published2013
Admission routes1
Has abstractyes

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