Dynamic imaging of a single gold nanoparticle in liquid irradiated by off-resonance femtosecond laser
Bibliographic record
Abstract
Plasmonic nanoparticles can lead to extreme confinement of the light in the near field. This unique ability of plasmonic nanoparticles can be used to generate nanobubbles in liquid. In this work, we demonstrate with single-particle monitoring that 100 nm gold nanoparticles (AuNPs) irradiated by off-resonance femtosecond (fs) laser in the tissue therapeutic optical window (λ = 800 nm), can act as a durable nanolenses in liquid and provoke nanocavitation while remaining intact. We have employed combined ultrafast shadowgraphic imaging, in situ dark field imaging and dynamic tracking of AuNP Brownian motion to ensure the study of individual AuNPs/nanolenses under multiple fs laser pulses. We demonstrate that 100 nm AuNPs can generate multiple, highly confined (radius down to 550 nm) and transient (life time < 50 ns) nanobubbles. The latter is of significant importance for future development of in vivo AuNP-assisted laser nanosurgery and theranostic applications, where AuNP fragmentation should be avoided to prevent side effects, such as cytotoxicity and immune system's response. The experimental results have been correlated with theoretical modeling to provide an insight to the AuNP-safe cavitation mechanism as well as to investigate the deformation mechanism of the AuNPs at high laser fluences.
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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".