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 3.57x10<sup>27</sup> m<sup>-3</sup>. 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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| 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 teacher head, 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".