Cubic <i>versus</i> hexagonal – effect of host crystallinity on the <i>T</i><sub>1</sub> shortening behaviour of NaGdF<sub>4</sub> nanoparticles
Bibliographic record
Abstract
Sodium gadolinium fluoride (NaGdF4) nanoparticles are promising candidates as T1 shortening magnetic resonance imaging (MRI) contrast agents due to the paramagnetic properties of the Gd3+ ion. Effects of size and surface modification of these nanoparticles on proton relaxation times have been widely studied. However, to date, there has been no report on how T1 relaxivity (r1) is affected by the different polymorphs in which NaGdF4 crystallizes: cubic (α) and hexagonal (β). Here, a microwave-assisted thermal decomposition method was developed that grants selective access to NaGdF4 nanoparticles of either phase in the same size range, allowing the influence of host crystallinity on r1 to be investigated. It was found that at 3 T cubic NaGdF4 nanoparticles exhibit larger r1 values than their hexagonal analogues. This result was interpreted based on Solomon-Bloembergen-Morgan theory, suggesting that the inner sphere contribution to r1 is more pronounced for cubic NaGdF4 nanoparticles as compared to their hexagonal counterparts. This holds true irrespective of the chosen surface modification, i.e. small citrate groups or longer chain poly(acrylic acid). Key aspects were found to be a polymorph-induced larger hydrodynamic diameter and the higher magnetization possessed by cubic nanoparticles.
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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".