The Internal Structure of Lanthanide-Doped Nanoparticles and the Effect of High-Temperature Annealing on Their Luminescent Properties
Bibliographic record
Abstract
Up-converting nanoparticles are widely studied for a wide range of applications based on their unique optical properties, with NaYF 4 nanoparticles doped with Yb 3+ and Er 3+ receiving particular attention. While developing this material in nanoparticle form extends their potential applications, the resulting nanoparticles have proven less efficient up-converters than their bulk counterpart. Reported up-conversion quantum yields are significantly lower, even when very thick shells were grown to eliminate quenching by surface defects and surface-bound molecules. This raises the question whether the internal structure of these particles contributes to the lower quantum yield. In our work, we investigated the internal structure of NaYF 4:Yb 3+ and Er 3+ NPs using high-resolution scanning energy dispersive X-ray spectroscopy, generating two-dimensional elemental maps. We deduced that the ions are not distributed homogeneously in the nanoparticles as made via a colloidal synthesis route. Heating the nanoparticles to temperatures used to anneal bulk crystals resulted in a homogeneous distribution, but an increase in emission intensity under similar measurement conditions was not observed. Vibrational spectroscopy showed the presence of OH – in dried nanoparticles, which might act as an internal quencher.
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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 | 0.000 |
| 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".