Analysis of Photoacoustic Response from Gold–Silver Alloy Nanoparticles Irradiated by Short Pulsed Laser in Water
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".