Water dispersible ligand-free rare earth fluoride nanoparticles: water transfer <i>versus</i> NaREF<sub>4</sub>-to-REF<sub>3</sub> phase transformation
Bibliographic record
Abstract
The chemical stability of oleate-capped sub-10 nm α- and β-NaREF4 NPs (RE = Y, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Yb, Lu for α- and RE = Pr, Nd, Sm, Eu, Gd, Tb, Dy for β-phase NPs) was evaluated under the acidic conditions used for ligand removal towards water dispersibility. It was found that for such small NPs, a pH lower than 3 was necessary for the water transfer to be efficient and to yield well-dispersed ligand-free NPs. In stark contrast to the generally considered good chemical stability of NaREF4, these conditions were observed to pose a risk to phase transformation of the NaREF4 NPs into much larger, hexagonal- or orthorhombic-phase REF3, depending on the NP composition. A correlation between the thermodynamic stability of the α/β-NaREF4 and the hexagonal/orthorhombic REF3 phases - dictated by the RE ion choice - and the chemical stability of the NPs was found. For instance, β-NaGdF4 NPs remained stable, while α-NaGdF4 NPs underwent phase transformation into hexagonal GdF3. More general, NaREF4 NPs based on lighter RE ions were more prone towards phase transformation, while those based on heavier RE ions exhibited stability. Herein, within the RE series, the borderline for phase transformation was identified as Tb/Dy for α-NaREF4 NPs and Sm/Eu for β-NaREF4 NPs, respectively. Also, given the large interest in luminescent NPs for, e.g. biomedical applications, optically active Ln3+ ions (Ln = Nd, Eu, Tb, Er/Yb) were doped into α/β-NaGdF4 host NPs, and the dopant influence on the chemical stability was evaluated. Steady state and time-resolved spectroscopy unveiled spectral features characteristic for Ln3+ f-f transitions, i.e. downshifting and upconversion, before and after ligand removal. Overall, the results herein described emphasise the importance of minding the chemical procedure used for ligand removal of NaREF4 NPs of different crystalline phases and RE compositions.
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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".