Cation Exchange: A Facile Method To Make NaYF<sub>4</sub>:Yb,Tm-NaGdF<sub>4</sub> Core–Shell Nanoparticles with a Thin, Tunable, and Uniform Shell
Bibliographic record
Abstract
Cation exchange was performed on up-conversion NaYF 4:Yb,Tm nanoparticles, resulting in NaYF 4:Yb,Tm-NaGdF 4 core–shell nanoparticles as indicated by electron energy-loss spectroscopy 2D mapping. Results show that core–shell nanoparticles with a thin, tunable, and uniform shell of subnanometer thickness can be made via this cation exchange process. The resulting NaYF 4:Yb,Tm-NaGdF 4 core–shell nanoparticles have an enhanced up-conversion intensity relative to the initial core nanoparticles. As potential magnetic resonance imaging (MRI) contrast agents, they were tested for their proton relaxivities. The r 1 relaxivity per Gd 3+ ion of the nanoparticles with a thin NaGdF 4 shell (ca. 0.6 nm thick) measured at 9.4 T was found to be 2.33 mM –1 ·s –1 . This r 1 relaxivity is among the highest in all the reported NaYF 4 –NaGdF 4 core–shell nanoparticles. The r 1 relaxivity per nanoparticle is 1.56 × 10 4 mM –1 ·s –1, which is over 4000 times higher than commercial Gd 3+ -complexes. The very high proton relaxivity per nanoparticle is critical for targeted MRI as such nanoparticles provide strong contrast even in low concentrations. The presented cation exchange method is a promising way to manufacture core–shell nanoparticles with up-conversion NaYF 4:Yb,Tm core and paramagnetic NaGdF 4 shell for bimodal imaging, i.e. MR and optical imaging.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".