Computational Design of Durable Spherical Nanoparticles with Optimal Material, Shape, and Size for Ultrafast Plasmon-Enhanced Nanocavitation
Bibliographic record
Abstract
Photons interaction with metallic nanoparticles can excite a resonant plasmon that concentrates energy at the nanoscale. At high intensity, this quasi-particle decays into a photoexcited nanoplasma that triggers the generation of nanobubbles, which can be used for imaging and therapeutic purposes. This highly nonlinear wavelength-dependent process is controlled by the nanoparticle material, shape, and size in intricate ways, which justifies the need for a systematic design approach that currently lacks in the field. To palliate to this, we developed in this work a computational framework that enables the efficient in silico screening of large libraries of spherically symmetric structures and metallic materials. Using this framework, we have investigated the nanocavitation performance of spherical nanoparticles with more than 14 million combinations of materials, shapes, sizes, and irradiation conditions, from which we could distill general principles for the design of durable nanoantennas. In the near-infrared, our work suggests that Cu, TiN, Ag, and Au nanoparticles offer similar performance, with optimal diameters of ∼λ/5. In contrast, only Ag and Al are appropriate for irradiation in the UV–visible, cavitation being associated with structural damage for all other tested materials at these wavelengths. We also demonstrate that silica-metal nanoshell structures have the potential to reduce the cavitation threshold at all wavelengths compared to homogeneous nanoparticles due to their extensive spectral tunability. However, designing more complex layered systems seems to bring no benefit. Our work provides important physical insight on the influence of materials on nanocavitation and simulation-based design guidelines that should be broadly useful for the engineering of nonlinear nanoplasmonic materials for biological applications.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".