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Record W2508966439 · doi:10.1021/acs.jpcc.5b08359

Analysis of Photoacoustic Response from Gold–Silver Alloy Nanoparticles Irradiated by Short Pulsed Laser in Water

2015· article· en· W2508966439 on OpenAlexafffund
Ali Hatef, Behafarid Darvish, Adrien Dagallier, Yevgeniy R. Davletshin, W. B. Johnston, J. Carl Kumaradas, David Rioux, Michel Meunier

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2015
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsToronto Metropolitan UniversityPolytechnique MontréalNipissing University
FundersNipissing University
KeywordsMaterials scienceMultiphysicsAlloyIrradiationLaserPhotothermal therapyNanoparticleNanosecondWavelengthSIGNAL (programming language)Surface plasmon resonanceOptoelectronicsAnalytical Chemistry (journal)Composite materialOpticsNanotechnologyFinite element methodChemistry

Abstract

fetched live from OpenAlex

We perform a finite element-based numerical analysis to calculate the photoacoustic (PA) signal generated by spherical gold–silver (Au–Ag) alloy nanoparticles (NPs). These spherical particles are size-controlled and monodispersed, with tunable plasmonic resonance wavelength via change of the alloy composition. This enables their use in PA imaging as a contrast agent. This theoretical framework self-consistently solves the electromagnetic, thermodynamic and transient acoustic pressure physics using a multiphysics coupling approach. We model our system as an optically heterogeneous medium irradiated by a nanosecond laser pulse in the tissue therapeutic optical window (NIR irradiation, with wavelength of 800 nm). We calculate the PA signal generated by the photothermal expansion of both the particle and its surrounding medium. The results show the impact of the gold molar fraction (GMF) of Au–Ag alloy NPs on the PA signal for different NP sizes. We show that significantly stronger PA signals are achieved using Au–Ag alloy NPs (GMF = 0.55) in comparison with pure AuNPs (GMF = 1) and pure AgNPs (GMF = 0) of the same size and shape.

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)0.001
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 score0.049
Threshold uncertainty score0.368

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.009
GPT teacher head0.217
Teacher spread0.208 · 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

Citations62
Published2015
Admission routes2
Has abstractyes

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