Computational characterization of plasma effects in ultrafast laser irradiation of spherical gold nanostructures for photothermal therapy
Bibliographic record
Abstract
Abstract Ultrashort pulsed lasers can provide high peak intensity with low pulse fluence. This makes them an ideal choice in photothermal therapy and applications where damage to the surrounding material needs to be minimized. Depending on the peak intensity, the ultrashort pulsed laser’s interaction with matter can lead to plasma formation through nonlinear effects such as multiphoton and impact electron excitation. The capability of the spherical gold nanoparticles, as the most employed nanoparticle so far for photothermal therapy, to enhance and strongly localize the incident laser field leads to plasma formation around the particles at even lower pulse fluences. Under certain circumstances, during the pulse duration, this plasma can absorb more energy than the nanoparticle itself. Consequently, the absorbed energy by the generated plasma can act as an energy source for different phenomena such as the evolution of the temperature distribution, thermoelastic stress generation, and stress-induced bubble formation. In this paper, we study the plasma-mediated interaction of a 45 fs pulsed laser with two types of spherical gold nanoparticles in water: solid nanoparticle and core–shell (silica–gold) nanoparticle. We use a numerical framework based on the finite element method (FEM) to compare energy deposition profiles in these nanoparticles and in their surrounding plasma, by focusing on the impact of the nanoparticle size and the laser fluence. Our calculations show that the maximum energy deposition in plasma occurs in core–shell nanoparticles with a diameter of 130 nm and the ratio of core to shell radius of 0.8 and in solid nanoparticles with a diameter of 170 nm.
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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 | 0.000 | 0.000 |
| 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".