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Record W2105054574 · doi:10.1351/pac-con-08-10-10

Near-field optical probes provide subdiffraction-limited excitation areas for fluorescence correlation spectroscopy on membranes

2009· article· en· W2105054574 on OpenAlexaff
Dušan Vobornik, Daniel S. Banks, Zhengfang Lu, Cécile Fradin, Rod S. Taylor, Linda J. Johnston

Bibliographic record

VenuePure and Applied Chemistry · 2009
Typearticle
Languageen
FieldEngineering
TopicNear-Field Optical Microscopy
Canadian institutionsMcMaster UniversitySteacie Institute for Molecular Sciences
Fundersnot available
KeywordsFluorescence correlation spectroscopyChemistrySpectroscopyExcitationNear-field scanning optical microscopeMicroscopyMembraneOpticsFluorescence spectroscopyConfocalAperture (computer memory)FluorescenceAnalytical Chemistry (journal)Optical microscope

Abstract

fetched live from OpenAlex

Abstract Near-field optical probes have been used to produce a subdiffraction-limited observation area for fluorescence correlation spectroscopy (FCS) experiments on supported membranes. The design of a bent, etched fiber probe that is compatible with biological imaging in an aqueous environment is described. This probe design is used for proof of principle experiments to measure lipid diffusion in a fluid-supported bilayer. A reduction in excitation area of approximately one order of magnitude (relative to a confocal FCS experiment) is obtained with a probe aperture diameter of 140 nm. We also demonstrate a simple approach for modeling the autocorrelation decay due to diffusion within the excitation profile at the near-field scanning optical microscopy (NSOM) probe aperture. The use of probes with smaller apertures is expected to provide an additional order of magnitude reduction in the observation area, thus enabling the study of cellular membranes with higher concentrations of fluorophores than is currently possible with diffraction-limited techniques.

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)0.001
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0010.000

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 head0.005
GPT teacher head0.217
Teacher spread0.211 · 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
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

Citations5
Published2009
Admission routes1
Has abstractyes

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