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Record W2015149259 · doi:10.1364/oe.16.004376

Photothermal detection of gold nanoparticles using phase-sensitive optical coherence tomography

2008· article· en· W2015149259 on OpenAlexfundno aff
Desmond C. Adler, Shu‐Wei Huang, Robert Huber, James G. Fujimoto

Bibliographic record

VenueOptics Express · 2008
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsnot available
FundersAir Force Office of Scientific ResearchNational Eye InstituteNatural Sciences and Engineering Research Council of CanadaCarl Zeiss Meditec AGNational Cancer InstituteNational Institutes of HealthNational Science Foundation
KeywordsMaterials scienceNanoshellOpticsOptical coherence tomographyPhotothermal therapyLaserColloidal goldNanoparticleOptoelectronicsPlasmonNanotechnology

Abstract

fetched live from OpenAlex

The detection of a gold nanoparticle contrast agent is demonstrated using a photothermal modulation technique and phase sensitive optical coherence tomography (OCT). A focused beam from a laser diode at 808 nm is modulated at frequencies of 500 Hz-60 kHz while irradiating a solution containing nanoshells. Because the nanoshells are designed to have a high absorption coefficient at 808 nm, the laser beam induces small-scale localized temperature oscillations at the modulation frequency. These temperature oscillations result in optical path length changes that are detected by a phase-sensitive, swept source OCT system. The OCT system uses a double-buffered Fourier domain mode locked (FDML) laser operating at a center wavelength of 1315 nm and a sweep rate of 240 kHz. High contrast is observed between phantoms containing nanoshells and phantoms without nanoshells. This technique represents a new method for detecting gold nanoparticle contrast agents with excellent signal-to-noise performance at high speeds using OCT.

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 communication0.0000.000
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 head0.024
GPT teacher head0.249
Teacher spread0.225 · 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

Citations230
Published2008
Admission routes1
Has abstractyes

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