Modeling ultrafast laser-induced nanocavitation around plasmonic nanoparticles (Conference Presentation)
Bibliographic record
Abstract
Vapor nanobubbles generated around plasmonic nanoparticles (NPs) by ultrafast laser irradiation are efficient for inducing localized damage to living cells. Killing targeted cancer cells or gene delivery can therefore be envisioned using this new technology [1,2]. The extent of the damage and its non-lethal character are linked to the size of the nanobubble. Precise understanding of the mechanisms leading to bubble formation around plasmonic nanostructures is necessary to optimize the technique. In this presentation, we present a complete model that successfully describes all interactions occurring during the irradiation of plasmonics nanostructures by an ultrafast laser of various pulse widths and fluences. Nanoavitation is caused by the interplay between heat conduction at the NP-medium interface and non-linear plasmon-enhanced photoionization of a nanoplasma in the near-field [3-5], the former being dominant for in-resonance and the latter for off-resonance irradiation. Modeling of the whole laser-nanoparticle interaction, together with the help of the shadowgraphic imaging and scattering techniques [3-5], give valuable insight on the mechanisms of cavitation at the nanoscale, leading to possible optimization of the nanostructure for bubble-based nanomedicine applications. 1- E. Boulais, R. Lachaine, A. Hatef, and M. Meunier, Journal of Photochemistry and Photobiology C: Photochemistry Reviews 17, 26-49 (2013). 2- E. Bergeron, S. Patskovsky, D. Rioux, and M. Meunier, Nanoscale 7,17836-17847 (2015). 3- E. Boulais, R. Lachaine, and M. Meunier, Nano Letters 12, 4763-4769 (2012). 4- R. Lachaine, E. Boulais, and M. Meunier, ACS Photonics 1, 331-336 (2014). 5- C. Boutopoulos, A. Hatef, M. Fortin-Deschênes, and M. Meunier Nanoscale 7,11758-11765 (2015).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".