Single-mode radiation-balanced Yb-doped silica fiber laser and amplifier
Bibliographic record
Abstract
This paper reports a second generation of radiation-balanced fiber laser and amplifier cooled internally using anti-Stokes fluorescence by pumping them at 1040 nm. In both devices the gain medium is a single-mode silica fiber with a core heavily doped with Yb3+, initially encapsulated in CaF2 nanoparticles, and co-doped with Al to reduce quenching and increase the cooling efficiency. After optimization of its length (4.1 m) and its output coupler reflectivity (3.3%), the 1065- nm continuous-wave fiber laser has a threshold of 160 mW and a radiation-balanced (no net heat generation) output power of 192 mW, or nearly 70% higher than the previous radiation-balanced fiber laser. At its radiation-balanced point, its optical efficiency is 56.8%. The single-frequency, single-mode fiber amplifier, constructed with the same fiber, was optimum with a length of 6.8 m, and it had a radiation-balanced gain of 20 dB: it amplified an 800-μW signal to 84.2 mW with 433 mW of input pump power. The significance of this result is underscored by the small diameter of the single-mode fiber core (7.8 μm), which makes cooling more challenging. This study further demonstrates the viability of achieving substantial gain and energy extraction in a small-core Yb-doped silica fiber while effectively utilizing anti-Stokes fluorescence to keep it cool.
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 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".