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Record W4412928806 · doi:10.1364/boe.568655

Penetration of laser-induced jets into soft elastic substrates: simplified model and experiments

2025· article· en· W4412928806 on OpenAlexafffund
Siew‐Wan Ohl, Claus‐Dieter Ohl, Seyed Mohammad Taghavi

Bibliographic record

VenueBiomedical Optics Express · 2025
Typearticle
Languageen
FieldEngineering
TopicLaser Material Processing Techniques
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsDeutsche Forschungsgemeinschaft
KeywordsPenetration (warfare)OpticsLaserMaterials sciencePhysicsEngineering

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.290
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.019
GPT teacher head0.277
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

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