Anti-Stokes fluorescence cooling of nanoparticle-doped silica fibers
Bibliographic record
Abstract
The first observation of cooling by anti-Stokes pumping in nanoparticle-doped silica fibers is reported. Four Yb-doped fibers fabricated using conventional modified chemical vapor deposition (MCVD) techniques were evaluated, namely, an aluminosilicate fiber and three fibers in which the Yb ions were encapsulated in CaF2, SrF2, or BaF2 nanoparticles. The nanoparticles, which oxidize during preform processing, provide a modified chemical environment for the Yb3+ ions that is beneficial to cooling. When pumped at the near-optimum cooling wavelength of 1040 nm at atmospheric pressure, the fibers experienced a maximum measured temperature drop of 20.5 mK (aluminosilicate fiber), 26.2 mK (CaF2 fiber), and 16.7 mK (SrF2 fiber). The BaF2 fiber did not cool but warmed slightly. The three fibers that cooled had a cooling efficiency comparable to that of the best previously reported Yb-doped silica fiber that cooled. Data analysis shows that this efficiency is explained by the fibers’ high critical quenching concentration and low residual absorptive loss (linked to sub-ppm OH contamination). This study demonstrates the large untapped potential of nanoparticle doping in the current search for silicate compositions that produce optimum anti-Stokes cooling.
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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 | 0.001 |
| 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 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".