Penetration of laser-induced jets into soft elastic substrates: simplified model and experiments
Bibliographic record
Abstract
We study the penetration dynamics of laser-induced bullet jets into soft elastic substrates, via experiments and a simplified energy-based model that predicts the time-dependent jet penetration depth based on jet kinematics and substrate elasticity. Our model provides solutions in two variants: one using a prescribed jet velocity and another predictive formulation based on stand-off ( i.e. , bubble-to-free surface distance). High-speed experiments in gelatin, as a representative soft elastic material, reveal that small stand-off distances enhance bubble collapse asymmetry and produce coherent jets, while larger stand-offs suppress penetration. Model predictions show reasonable agreement with experimental data: the velocity- and stand-off-based formulations reasonably capture penetration trends. A range of experiments with bullet and re-entrant jets confirm the model’s applicability across diverse jet–gelatin interactions. The model’s minimal form and predictive accuracy offer a simplified foundation for optimizing cavitation-based technologies in biomedical applications, including needle-free injections, soft robotic actuation, and bioprinting.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".