From Thermo- to Plasma-Mediated Ultrafast Laser-Induced Plasmonic Nanobubbles
Bibliographic record
Abstract
The laser-induced generation of vapor bubbles around gold nanoparticles (AuNPs) is a promising diagnostic and therapeutic avenue for various pathologies. The physical mechanism that leads to their formation strongly depends on the time regime of the irradiation. While the plasmonic nanobubbles induced by nanosecond and picosecond laser pulses are known to be triggered by the energy that is absorbed in the nanoparticles and diffused in the medium (thermo-mediated cavitation), we show that a different plasma-mediated mechanism occurs in the case of femtosecond pulses. In this paper, we present experimental evidence that the dimensions of laser-induced bubbles depend on the polarization of the incident laser pulse when the pulse duration is reduced below ∼1 ps. This result cannot be explained by the standard thermo-mediated cavitation process and, thus, reveals the onset of a new mechanism in the ultrafast regime. We further show that the discrepancy between the dimensions of bubbles generated from linearly and circularly polarized laser pulses can be explained using a simple model, revealing the polarization cavitation dependence as a clear signature of the plasma mechanism. This paper presents in detail the transition between both cavitation regimes and provides insight into the precise control of the cavitation dynamics at the nanoscale.
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 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".