Dynamic imaging of transient bubbles generated by femtosecond irradiation of plasmonic nanoparticles in suspensions and cell environment
Bibliographic record
Abstract
Femtosecond (fs) laser generation of submicron bubbles around plasmonic nanoparticles (NPs) plays a key role in advanced laser nanosurgery applications such as cell membrane perforation and cell transfection. In this context, we have developed a pump-probe shadowgraphic ultrafast imaging technique capable of tracking transient bubbles generated by fs irradiation (λ =800 nm, τ = 45 fs) of: (a) plasmonic NPs in suspensions and (b) plasmonic NPs attached to cells. The laser fluence was systematically varied from 100 mJ/cm<sup>2</sup> to 500 mJ/cm2 to study the effect on the bubble dynamics generated around 100 nm gold NPs (Au NPs). The plasmonic bubble (PB) generation threshold as well as the NPs deformation threshold were defined. Dark field (DF) imaging and scanning electron microscopy (SEM) revealed NP clustering when 100 nm bare Au NPs were incubated with a cancer cell culture. NP clustering was correlated with PB generation using a combined pump-probe and DF imaging approach. The clustering effect resulted in a significant 4- times decrease to the PB generation threshold compared to single NPs. The clustering effect was further investigated by evaluating bare and polyethylene glycol (PEG) functionalized NPs in terms of PB generation efficiency.
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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 | 0.001 | 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".