Size-Tunable, Ultrasmall NaGdF<sub>4</sub> Nanoparticles: Insights into Their T<sub>1</sub> MRI Contrast Enhancement
Bibliographic record
Abstract
We report on the size-tunable synthesis of thermodynamically stable (β) NaGdF 4 nanoparticles (NPs) below 10 nm. Paramagnetic β-NaGdF 4 NPs of four different sizes (2.5–8.0 nm with a narrow size distribution) were synthesized by simple modifications of the reaction conditions affecting nanoparticle growth dynamics. The synthesized NPs were transferred to water by exchanging the oleate ligands with biocompatible polyvinylpyrrolidone, and analyzed for their ability to affect magnetic resonance (MR) T 1 longitudinal relaxivity at 1.5 T. The ionic relaxivity (unit Gd 3+ concentration) values increased from 3.0 mM –1 s –1 to 7.2 mM –1 s –1 with decreasing particle size, and the relaxivity of the 2.5-nm particle is almost twice that of clinically used Gd-DTPA (Magnevist) relaxivity. The relaxivity per contrast agent (i.e., per nanoparticle) for these NPs is 200–3000 times larger than the clinical agent, showing great potential as local contrast enhancement probes. The rate of increase in ionic relaxivity with decreasing NP size was similar to the rate of increase in the NP surface-to-volume (S/V) ratio, giving direct indication that the surface Gd ions are the major contributors to the relaxivity enhancement. Further analysis based on concentration of NPs, mass concentration of NP, and unit surface area, has revealed that the surface Gd ions on a larger NP affect the relaxivity more strongly than those on a smaller NP. This is discussed based on the increase in NP rotational correlation time (τ R ) with increasing size. A particular advantage of β-NaGdF 4 NPs over other Gd 3+ -based inorganic NPs is that they are good hosts for upconverting emission. We demonstrate this by extending the synthesis protocol outlined here to prepare luminescent ultrasmall β- NaGdF 4:Yb 3+ /Tm 3+ NPs as potential bimodal probes.
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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".