Influence of environmental conditions in bovine bone ablation by ultrafast laser
Bibliographic record
Abstract
Ultrafast lasers are promising tools for surgical applications requiring precise tissue cutting. Shallow ablation depth and slow rate as well as collateral damage are common barriers limiting the use of laser in clinical applications. Localized cooling with water and/or air jet is known to reduce collateral thermal damage. We studied the influence of environmental conditions including air, compressed air flow, still water and water jet on ablation depth, ablation rate and surface morphology on bovine bone samples with an 800 nm femtosecond laser. At 15 J/cm2, no thermal effect was observed by electron microscopy and Raman spectroscopy. The experimental results indicate that environmental conditions play a significant role in laser ablation. The deepest cavity and highest ablation rate were achieved under the compressed air flow condition, which is attributed to debris removal during the ablation process. The shallowest ablation depth and lowest ablation rates were associated with water flushing. For surface morphology, smooth surface and the absence of microcracks were observed under air flow conditions, while rougher surfaces and minor microcracks were observed under other conditions. These results suggest that ultrafast ablation of bone can be more efficient and with better surface qualities if assisted with blowing air jet.
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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) | 0.000 | 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 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".