Water-Soluble GdF<sub>3</sub> and GdF<sub>3</sub>/LaF<sub>3</sub> NanoparticlesPhysical Characterization and NMR Relaxation Properties
Bibliographic record
Abstract
Nanoparticles consisting of either a solid core of GdF 3 or an 80/20 mixture of GdF 3 and LaF 3 have been prepared for use as NMR and MRI relaxation agents. To obtain high aqueous solubilities, the particles were coated with either citrate ( cit ) groups (in the case of GdF 3 nanoparticles), giving the nanoparticle a negatively charged surface, or 2-aminoethyl phosphate ( AEP ) groups (in the case of GdF 3 /LaF 3 = 80/20), giving the nanoparticle a positively charged surface at physiological pH. In the presence of the 80/20 GdF 3 /LaF 3: AEP, the paramagnetic contribution to the water spin−lattice relaxation rate was observed to be 7.5 s - 1 at a nanoparticle concentration of 9.0 nM (0.78 mg/mL, 25 °C, 600 MHz 1 H Larmor frequency). Similarly, paramagnetic rates of 10.5 s - 1 were observed for water using the GdF 3: cit nanoparticles at a nanoparticle concentration of 0.55 nM (0.77 mg/mL, 25 °C, 600 MHz 1 H Larmor frequency). Relaxivity measurements confirmed the potential of the particles for applications as contrast agents at MRI imaging field strengths. T 1 and T 2 experiments of the GdF 3: cit revealed mass relaxivities of 7.4 ± 0.2 and 8.4 ± 0.2 s - 1 (mg/mL) - 1, respectively, at 1.5 T, whereas identical measurements at 3.0 T revealed respective relaxivities of 8.8 ± 0.2 and 9.4 ± 0.2 s - 1 (mg/mL) - 1 . The relatively high mass relaxivities exhibited by the nanoparticles may also find uses in NMR studies in which spin−lattice relaxation times are prohibitively long, as in the case of highly deuterated proteins. Direct interaction with the protein can be minimized by the use of surface charges opposite to the net charge of the molecule, whereas the nanoparticles are easily removed by ultracentrifugation.
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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".