PEG-capped, lanthanide doped GdF3 nanoparticles: luminescent and T2 contrast agents for optical and MRI multimodal imaging
Bibliographic record
Abstract
A facile method for the synthesis of water dispersible Er(3+)/Yb(3+) and Tm(3+)/Yb(3+) doped upconverting GdF(3) nanoparticles is reported. Strong upconversion emissions are observed in the red (for Er/Yb doped) and near-infrared (for Tm/Yb doped) regions upon laser excitation at 980 nm. The PEG coating ensures a good dispersion of the system in water and reduces the radiationless de-excitation of the excited states of the Er(3+) and Tm(3+) ions by water molecules. The r(2) relaxivity values are quite high with respect to the common T(2)-relaxing agents (22.6 ± 3.4 mM(-1) s(-1) and 15.8 ± 3.4 mM(-1) s(-1) for the Tm/Yb and Er/Yb doped samples, respectively), suggesting that the present NPs can be interesting as T(2) weighted contrast agents for proton MRI purpose. Preliminary experiments conducted in vitro, in stem cell cultures, and in vivo, after subcutaneous injection of the lanthanide-doped GdF(3) NPs, indicate scarce toxic effects. After an intravenous injection in mice, the GdF(3) NPs localize mainly in the liver. The present results indicate that the present Er(3+)/Yb(3+) and Tm(3+)/Yb(3+) doped GdF(3) NPs are suitable candidates to be efficiently used as bimodal probes for both in vitro and in vivo optical and magnetic resonance 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) | 0.000 | 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 | 0.001 | 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".