Effects of heat transfer and energy absorption in the ablation of biological tissues by pulsetrain-burst (>100 MHz) ultrafast laser processing
Bibliographic record
Abstract
Energy absorption and heat transfer are important factors for regulating the effects of ablation of biological tissues. Heat transfer to surrounding material may be desirable when ablating hard tissue, such as teeth or bone, since melting can produce helpful material modifications. However, when ablating soft tissue it is important to minimize heat transfer to avoid damage to healthy tissue - for example, in eye refractive surgery (e.g., Lasik), nanosecond pulses produce gross absorption and heating in tissue, leading to shockwaves, which kill and thin the non-replicating epithelial cells on the inside of the cornea; ultrafast pulses are recognized to reduce this effect. Using a laser system that delivers 1ps pulses in 10μs pulsetrains at 133MHz we have studied a range of heat- and energy-transfer effects on hard and soft tissue. We describe the ablation of tooth dentin and enamel under various conditions to determine the ablation rate and chemical changes that occur. Furthermore, we characterize the impact of pulsetrain-burst treatment of collagen-based tissue to determine more efficient methods of energy transfer to soft tissues. By studying the optical science of laser tissue interaction we hope to be able to make qualitative improvements to medical treatments using lasers.
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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) | 0.002 | 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".