Anti-Stokes fluorescence cooling in Yb-doped ZBLAN fibers at atmospheric pressure: experiments and near-future prospects
Bibliographic record
Abstract
We report for the first time cooling by anti-Stokes fluorescence (ASF) of a single-mode fiber, and cooling of a fiber at atmospheric pressure. This demonstration and the ability of our model to accurately predict cooling are crucial steps towards the development of radiation-balanced fiber lasers and were the primary focus of this work. We also experimentally investigated the effects of pump power and wavelength, the core size, and the dopant concentration on ASF cooling, in order to maximize this process. Experiments were performed on two Yb-doped ZBLAN fiber from Le Verre Fluoré: a single-mode fiber doped with 1 mol% Yb and a multimode fiber doped with 3 mol% Yb. The maximum temperature change achieved in the two fibers was -5.2 mK and -0.64 K, respectively, confirming that cooling scales with doped area. However, we also discuss limitations to this scaling, namely the absorptive loss, concentration quenching, and the mode profile of the pump. We use our previously reported model to quantify these scarcely reported parameters. For the multimode fiber, comparison between the experimental data and the model gave an inferred absorptive loss of 45 dB/km and a critical quenching concentration of 3.57x1027 m-3. In addition to these parameters, accurate modeling also requires precise knowledge of the absorption and emission cross-sections. To this end, we propose a method to obtain spectra that obey the McCumber relation and accurately represent the material under investigation. Finally, we report on the cooling efficiencies achieved in the single-mode (2.0%) and multimode (0.85%) fibers and show that the efficiency decreases with increasing pump power due to absorptive loss.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 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".