Design and Regulation of NaHoF<sub>4</sub> and NaDyF<sub>4</sub> Nanoparticles for High-Field Magnetic Resonance Imaging
Bibliographic record
Abstract
Lanthanide-based (Ln 3+ -based) nanoparticles have great potential for high-field magnetic resonance imaging (MRI) applications. Here, we report NaHoF 4 and NaDyF 4 nanoparticles with modulated sizes (9–40 nm) and shapes (spherical-like, hexagonal prism, rod-like), suitable for high-field MRI as shown through in vivo studies. We used X-ray diffraction, transmission electron microscope, dynamic light scattering, and MRI techniques to investigate the structure, morphology, hydrodynamic size, and relaxivity of the prepared NaHoF 4 and NaDyF 4 nanoparticles, showing monodisperse nanoparticles of high crystallinity. In particular, we studied effects of the particle size, shape, surface coating and ζ-potential on transverse (spin–spin relaxivity, r 2 ) and longitudinal (spin–lattice relaxivity, r 1 ) relaxivity at 9.4 T. We found that the NaHoF 4 and NaDyF 4 nanoparticles have r 2 relaxivities of (274.0 ± 6.9) × 10 4 and (4767.3 ± 160.9) × 10 4 mM NP –1 s –1 per nanoparticle and high r 2 / r 1 ratio of 781 and 410 at a high magnetic field of 9.4 T, respectively, making them attractive as MRI T 2 contrast agents. The growth of the hexagonal structure of the NaHoF 4 and NaDyF 4 nanoparticles was mainly dominated by the growth competition along the [001] and [100] directions that could be modulated by the amount of oleic acid (OA), 1-octadecene (ODE), NaOH, and NH 4 F. Moreover, both the larger particle size and thin coating polymer layer of nanoparticles increased the transverse relaxivity. The effects of the parameters, such as rotation correlation time, diffusion, and electronic relation times on the dipolar and Curie components of the inner- and outer-sphere contribution, and thus directly on the relaxivity, are discussed. We found that these parameters can be modulated by the particle size and surface coating. Following the outer-sphere relaxation theory, we used computer simulations of r 1 and r 2 relaxivity as a function of the particle core size, hydrodynamic size, diffusion time, and electronic relation time, which all show an impact on r 1 and r 2, albeit to very different extends, which has enhanced our understanding of the relaxivity mechanisms. According to the computer simulations, r 1 can be controlled by the core size, hydrodynamic size at low magnetic fields of ∼0.02 T corresponding to proton Larmor frequency of about 1 MHz. The r 1 does not change significantly at the higher frequency and actually drops precipitously. However, on the basis of theoretical simulations, we expect r 2 can be increased in a wide range of Larmor frequencies (0.1–1000 MHz) by increasing the core size, reducing thickness of the coating layer, or increasing the magnetic field. The particle sizes, hydrodynamic sizes, and diffusion times and magnetic fields have larger contribution to the relaxivity than that of other parameters. Five animals were imaged and in vivo MRI showed that the rod-like NaDyF 4 (25 nm × 35 nm) nanoparticles, without any targeting ligands, provided visible contrast between brain and breast tumors and normal tissues up to 24 h after injection. Contrast was observed in all animals after injection of nanoparticle solution, which may be attributed to enhanced permeation and retention effects of the rod-shape nanoparticles in tumor. The experimental and simulation results suggest that the NaHoF 4 and NaDyF 4 nanoparticles are indeed good candidates for high-field (>3 T) T 2 imaging contrast agents.
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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".