Structure of NaYF<sub>4</sub> Upconverting Nanoparticles: A Multinuclear Solid-State NMR and DFT Computational Study
Bibliographic record
Abstract
The exceptional upconverting properties of lanthanide-doped nanoparticles make them attractive systems with applications ranging from photovoltaics to biological labeling, imaging, and therapeutics. While they draw considerable interest, structural data, which are necessary to understand the upconversion process, remain scarce. In this work, we demonstrate the use of 23 Na, 19 F, and 89 Y solid-state NMR together with DFT calculations to characterize the structure of cubic NaYF 4 nanoparticles with and without Er 3+ doping. By measuring 23 Na MAS NMR spectra at various magnetic fields and 3QMAS spectra at ultrahigh field, we show that the spectra are characteristic of a solid solution in which cation sites are statistically occupied by Na + or Y 3+ ions. The 23 Na NMR spectra are broadened as a result of isotropic chemical shift distribution, whereas the extracted quadrupolar products appear to be small (≤1.8 MHz), which is in good agreement with DFT calculations using CASTEP. The chemical shift distribution in 19 F NMR spectra is well-predicted by CASTEP calculations and shown to strongly depend on coordination by Y 3+ . Finally, the 89 Y NMR spectra consist of a single broad pattern, which also results from a chemical shift distribution that can be correlated to the coordination environment of the Y 3+ cations. Our results show that the structure is a slightly distorted cubic phase and lanthanide doping has only a minor effect on the lattice parameters. The approach appears to be promising for gaining additional insight into the atomic level structure details to better understand properties that govern the upconversion process and its efficiency.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".