Laser-induced plasma formation and cavitation in water: from nanoeffects to extreme states of matter
Bibliographic record
Abstract
Abstract We present an in-depth analysis of the energy dependence of optical breakdown in water by tightly focused laser pulses, from plasma formation to shock waves and cavitation. Laser pulses of fs to ns durations and UV to IR wavelengths are aberration-free focused through microscope objectives. Photography captures luminescent plasmas with submicrometer resolution, and bubble threshold and size are determined via probe beam scattering. The energy dependence of mechanical effects is quantified through the maximum bubble radius R max . We find three key scenarios depicting the interaction between multiphoton and avalanche ionization, recombination, and thermal ionization from nanoeffects near threshold to extreme energy densities. They include a previously unknown scenario that emerges with single-longitudinal-mode UV ns pulses from compact lasers. It enables cost-effective creation of nanoeffects, as demonstrated on corneal tissue and glass. High-resolution color photography revealed new insights in the spatiotemporal dynamics of plasma formation, with an interplay of breakdown waves, string formation by local instabilities of avalanche ionization, and radiative energy transport. Plasma volume data from photographs together with absorption measurements show that the average energy density of luminescent fs and ns plasmas is similar, ranging between 10 and 40 kJ cm −3 . However, small hot regions with up to 400 kJ cm −3 are formed in ns breakdown. From the hot regions, energy is spread out via x-ray bremsstrahlung, forming a luminescent halo. Well above threshold, R max scales with E 1/3 across all scenarios, with 15%–20% conversion of laser energy into bubble energy. With increasing plasma energy density, an ever-larger energy fraction is converted into shock wave energy (75% at 40 kJ cm −3 ). We discuss guidelines for parameter selection in laser surgery and material processing in bulk media as well as for laser ablation and breakdown spectroscopy in liquids. Finally, we suggest roadmaps for future experimental and modeling work, and for broadening 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 | 0.000 | 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) | 0.001 | 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".