Effective Control of the Ratio of Red to Green Emission in Upconverting LaF<sub>3</sub> Nanoparticles Codoped with Yb<sup>3+</sup> and Ho<sup>3+</sup> Ions Embedded in a Silica Matrix
Bibliographic record
Abstract
The red to green ratio from upconversion in Yb 3+ and Ho 3+ codoped LaF 3 nanoparticles embedded in a silica matrix can be controlled by careful tuning of the sol−gel process. Red to green ratios of 1:2.3 to 23:1 were observed from the samples that had the same composition of Yb 3+ and Ho 3+ ions codoped LaF 3 nanoparticles. The varied ratios were achieved by changing the aging and drying time of the sol−gel and the subsequent annealing process at elevated temperatures. XRD measurements showed that the sample that gave a red to green ratio of 23:1 had large amounts of amorphous silica, whereas the sample that gave a ratio of 1:2.3 had cristobalite, i.e., crystalline silica, in excess. The phonon energy of amorphous silica is higher than that of cristobalite, so quenching of the green emission effectively resulted in enhanced red emission. To prove that amorphous silica has a higher phonon energy, we completed upconversion luminescence studies at 77 K, which resulted in a decrease in the red to green ratio by a factor of 3. This indeed proves that the phonon energy of amorphous silica is the factor for observing enhanced red emission and a good control over the ratio of red to green. Infrared spectra show Si−O stretching vibrations over a broader energy range for amorphous silica than cristobalite which thus more easily matches with the difference in the energy levels of Ho 3+ ions, making the quenching process more efficient. To substantiate the above evidence, we performed partial etching of samples where enhanced red to green ratio was observed, and XRD results show the presence of amorphous silica and LaF 3 nanoparticles. After nearly complete etching of the silica, XRD results show the presence of LaF 3 and very little silica.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".