Red, Green, and Blue Light Through Cooperative Up-Conversion in Sol-Gel Thin Films Made With ${\hbox{Yb}}_{0.80}{\hbox{La}}_{0.15}{\hbox{Tb}}_{0.05}{\hbox{F}}_{3}$ and ${\hbox{Yb}}_{0.80}{\hbox{La}}_{0.15}{\hbox{Eu}}_{0.05}{\hbox{F}}_{3}$ Nanoparticles
Bibliographic record
Abstract
Silica and zirconium dioxide sol-gel thin films made with Yb0.80La0.15Tb0.05F3or Yb0.80La0.15Eu0.05F3nanoparticles are reported. Bright blue (413 and 435 nm), green (545 nm), and red (585 and 625 nm) emissions are produced from Tb3+ions through cooperative up-conversion of 980 nm light. Similarly, red (591 and 612 nm) emission is generated from Eu3+ions. These up-convertors may find use in white light sources. The cooperative up-conversion of Yb3+-Tb3+ions is more efficient than of Yb3+-Eu3+ions because the efficiency of energy transfer from excited Yb3+ions to a Tb3+ion (0.37) is more than two-times higher than of excited Yb3+ions to a Eu3+ion (0.15), as estimated from the lifetime of excited Yb3+ion. The estimated quantum yields of both Tb3+ion and Eu3+ion emissions are on the order of 40%, and hence are not the cause of the difference in efficiency. This approach does not work for Sm3+, Pr3+, and Dy3+. Incorporation of the respective Ln3+ions in nanoparticles is crucial, as controls, in which the various Ln3+ions are incorporated directly into the sol-gel, that do not show cooperative up-conversion
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".