Functional PEG–PAMAM-Tetraphosphonate Capped NaLnF<sub>4</sub> Nanoparticles and their Colloidal Stability in Phosphate Buffer
Bibliographic record
Abstract
Developing surface coatings for NaLnF4 nanoparticles (NPs) that provide long-term stability in solutions containing competitive ions such as phosphate remains challenging. An amine-functional polyamidoamine tetraphosphonate (NH2-PAMAM-4P) as a multidentate ligand for these NPs has been synthesized and characterized as a ligand for the surface of NaGdF4 and NaTbF4 nanoparticles. A two-step ligand exchange protocol was developed for introduction of the NH2-PAMAM-4P ligand on oleate-capped NaLnF4 NPs. The NPs were first treated with methoxy-poly(ethylene glycol)-monophosphoric acid (M(n) = 750) in tetrahydrofuran. The mPEG750-OPO3-capped NPs were stable colloidal solutions in water, where they could be ligand-exchanged with NH2-PAMAM-4P. The surface amine groups on the NPs were available for derivatization to attach methoxy-PEG (M(n) = 2000) and biotin-terminated PEG (M(n) = 2000) chains. The surface coverage of ligands on the NPs was examined by thermal gravimetric analysis, and by a HABA analysis for biotin-containing NPs. Colloidal stability of the NPs was examined by dynamic light scattering. NaGdF4 and NaTbF4 NPs capped with mPEG2000-PAMAM-4P showed colloidal stability in DI water and in phosphate buffer (10 mM, pH 7.4). A direct comparison with NaTbF4 NPs capped with a mPEG2000-lysine-based tetradentate ligand that we reported previously (Langmuir 2012, 28, 12861-12870) showed that both ligands provided long-term stability in phosphate buffer, but that the lysine-based ligand provided better stability in phosphate-buffered saline.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 teacher head, 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".