Multiscale modeling of plasmonic enhanced energy transfer and cavitation around laser-excited nanoparticles
Bibliographic record
Abstract
Nanoscale bubbles generated around laser-excited metallic nanoparticles are promising candidates for targeted drug and gene delivery in living cells. The development of new nanomaterials for efficient nanobubble-based therapy is however limited by the lack of reliable computational approaches for the prediction of their size and dynamics, due to the wide range of time and space scales involved. In this work, we present a multiscale modeling framework that segregates the various channels of plasmon de-excitation and energy transfer to describe the generation and dynamics of plasmonic nanobubbles. Detailed comparison with time-resolved shadowgraph imaging and spectroscopy data demonstrates that the bubble size, dynamics, and formation threshold can be quantitatively predicted for various types of nanostructures and irradiation parameters, with an error smaller than the experimental uncertainty. Our model in addition provides crucial physical insights into non-linear interactions in the near-field that should guide the experimental design of nanoplasmonic materials for nanobubble-based applications in nanomedicine.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".