All-fiber passively Q-switched Tm-doped laser with a mode field area mismatched Tm-doped saturable absorber
Bibliographic record
Abstract
We demonstrate a high peak power, all-fiber passively Q-switched Tm-doped laser operating at 1940 nm for applications in soft tissue ablation. High peak power and passive Q-switching were achieved via a clad pumped gain fiber and a smaller core Tm-doped fiber saturable absorber respectively. Clad pumping was achieved via two 30 W diodes operating at 793 nm. At 50.7 W of pump power, laser pulses with 140 ns FWHM duration and average power of 14.5 W were obtained at a repetition rate of 328 kHz which corresponded to a pulse energy of ~44 µJ and a peak power of ~316 W. The laser had a narrow 0.14 nm linewidth at the maximum output power. The laser was used to cut chicken breast as well as ovine cortical and subcortical brain tissues and average ablation efficiencies of 29, 40 and 42% were obtained, respectively. Cutting speeds of 10 mm/s for ovine brain tissue and 5 mm/s for chicken breast were achieved. Further resection experiments were performed with the target tissue placed under a thin layer of water. A resected volume with length, width and depth of 5 mm, 2.5 mm, and 2.8 mm were obtained, corresponding to a resection rate of ~0.58 mm 3 /s. To our best knowledge, this is the first report of an all-fiber clad-pumped passively Q-switched Tm-doped fiber laser with a core mismatched saturable absorber being used for tissue ablation experiments.
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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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".