A high repetition rate TEA CO 2 laser operating at λ=9.3-μm for the rapid and conservative ablation and modification of dental hard tissues
Bibliographic record
Abstract
TEA CO2 lasers tuned to the strong mineral absorption of hydroxyapatite near λ=9-μm are ideally suited for the efficient ablation of dental hard tissues if the laser-pulse is stretched to greater than 5-10-μs to avoid plasma shielding phenomena. Such CO2 lasers are capable of operating at high repetition rates for the rapid removal of dental hard tissues. An Impact 2500 TEA CO2 laser system from GSI Lumonics (Rugby, UK) custom modified by LightMachinery (Ottawa, Canada) with a repetition rate of 0-500 Hz was used for rapid tissue removal. The single pulse ablation rates through enamel were determined for incident fluence ranging from (1-160 J/cm2). Lateral incisions using a computer controlled scanning stage and water spray were produced and the crater morphology and chemical composition were measured using optical microscopy and high-resolution synchrotron radiation infrared spectromicroscopy. The transmission through 2-meter length 300, 500, 750 and 1000-μm silica hollow waveguides was measured and 80% transmission was achieved with 40-mJ per pulse. The λ=9.3-μm laser pulses efficiently removed dental enamel at rates exceeding 15-μm per pulses with minimal heat accumulation. The residual energy remaining in tooth samples was measured to be 30-40% without water cooling, significantly lower than for longer CO2 laser pulses. These results suggest that high repetition rate TEA CO2 laser systems operating at λ=9.3-μm with pulse durations of 10-20-μs are ideally suited for dental application.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".