Absolute upconversion quantum yields of blue-emitting LiYF<sub>4</sub>:Yb<sup>3+</sup>,Tm<sup>3+</sup> upconverting nanoparticles
Bibliographic record
Abstract
The upconversion quantum yield (ΦUC) is an essential parameter for the characterization of the optical performance of lanthanoid-doped upconverting nanoparticles (UCNPs). Despite its nonlinear dependence on excitation power density (Pexc), it is typically reported only as a single number. Here, we present the first measurement of absolute upconversion quantum yields of the individual emission bands of blue light-emitting LiYF4:Yb3+,Tm3+ UCNPs in toluene. Reporting the quantum yields for the individual emission bands is required for assessing the usability of UCNPs in various applications that require upconverted light of different wavelengths, such as bioimaging, photocatalysis and phototherapy. Here, the reliability of the ΦUC measurements is demonstrated by studying the same batch of UCNPs in three different research groups. The results show that whereas the total upconversion quantum yield of these UCNPs is quite high-typically 0.02 at a power density of 5 W cm-2-most of the upconverted photon flux is emitted in the 794 nm upconversion band, while the blue emission band at 480 nm is very weak, with a much lower quantum yield of ∼6 × 10-5 at 5 W cm-2. Overall, although the total upconversion quantum yield of LiYF4:Yb3+,Tm3+ UCNPs seems satisfying, notably for NIR bioimaging, blue-light demanding phototherapy applications will require better-performing UCNPs with higher blue light upconversion quantum yields.
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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 | 0.001 |
| 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 | 0.001 |
| 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".