Nonstatistical Dopant Distribution of Ln<sup>3+</sup>-Doped NaGdF<sub>4</sub> Nanoparticles
Bibliographic record
Abstract
Oleate-stabilized NaGdF 4 nanoparticles codoped with 20% Y 3+ and 5% Tb 3+ (NaGdF 4:Y,Tb), with 20% Nd 3+ (NaGdF 4:Nd), and with 20% Tb 3+ (NaGdF 4:Tb) were prepared in organic medium. The distribution of dopant ions was studied using synchrotron radiation X-ray photoelectron spectroscopy along with X-ray powder diffractometry, transmission electron microscopy, energy dispersive X-ray spectroscopy, and luminescence spectroscopy. Results show that these nanoparticles do not have the intended statistical dopant distribution despite the fact that different synthesis procedures and dopant ions with different ionic radii were used. NaGdF 4:Y,Tb nanoparticles have a subtle gradient structure with Gd 3+ more concentrated toward the center and Y 3+ more concentrated toward the surface of the nanoparticles. NaGdF 4:Nd nanoparticles have a steep gradient structure with Gd 3+ more concentrated toward the center and Nd 3+ more concentrated toward the surface of the nanoparticles. Even NaGdF 4:Tb nanoparticles have a steep gradient structure with Tb 3+ more concentrated toward the center and Gd 3+ more concentrated toward the surface of the nanoparticles in spite of the very similar ionic radius of Gd 3+ and Tb 3+ . This work shows that the general assumption that Ln 3+ dopant ions in lanthanide-based nanoparticles are statistically distributed in the whole nanoparticle may not be true.
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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) | 0.000 | 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 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".