Rational Design of Plasmonic Nanoparticles for Enhanced Cavitation and Cell Perforation
Bibliographic record
Abstract
Metallic nanoparticles are routinely used as nanoscale antenna capable of absorbing and converting photon energy with subwavelength resolution. Many applications, notably in nanomedicine and nanobiotechnology, benefit from the enhanced optical properties of these materials, which can be exploited to image, damage, or destroy targeted cells and subcellular structures with unprecedented precision. Modern inorganic chemistry enables the synthesis of a large library of nanoparticles with an increasing variety of shapes, composition, and optical characteristic. However, identifying and tailoring nanoparticles morphology to specific applications remains challenging and limits the development of efficient nanoplasmonic technologies. In this work, we report a strategy for the rational design of gold plasmonic nanoshells (AuNS) for the efficient ultrafast laser-based nanoscale bubble generation and cell membrane perforation, which constitute one of the most crucial challenges toward the development of effective gene therapy treatments. We design an in silico rational design framework that we use to tune AuNS morphology to simultaneously optimize for the reduction of the cavitation threshold while preserving the particle structural integrity. Our optimization procedure yields optimal AuNS that are slightly detuned compared to their plasmonic resonance conditions with an optical breakdown threshold 30% lower than randomly selected AuNS and 13% lower compared to similarly optimized gold nanoparticles (AuNP). This design strategy is validated using time-resolved bubble spectroscopy, shadowgraphy imaging and electron microscopy that confirm the particle structural integrity and a reduction of 51% of the cavitation threshold relative to optimal AuNP. Rationally designed AuNS are finally used to perforate cancer cells with an efficiency of 61%, using 33% less energy compared to AuNP, which demonstrate that our rational design framework is readily transferable to a cell environment. The methodology developed here thus provides a general strategy for the systematic design of nanoparticles for nanomedical applications and should be broadly applicable to bioimaging and cell nanosurgery.
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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".