Quantification of Surface Ligands on NaYF<sub>4</sub> Nanoparticles by Three Independent Analytical Techniques
Bibliographic record
Abstract
There have been important advances in characterizing the surface coverage of ligands on colloidal inorganic nanoparticles (NPs), but our knowledge of ligand coverage on lanthanide NPs is much more limited. The as-synthesized NPs are often coated with hydrophobic ligands that need to be replaced with hydrophilic ligands such as poly(ethylene glycol) (PEG) for biomedical applications. The two challenges in terms of characterizing ligand coverage on NPs are first to show that different analytical methods give consistent results and second to show how the sample preparation protocol affects ligand density. Here, we report a quantitative study of the native oleate content of as-synthesized NaYF 4 and NaTbF 4 NPs, as well as the surface coverage after ligand exchange with three methoxyPEG-monophosphates with M n = 750, 2000, and 5000 Da. For NaYF 4, we obtained consistent results for both oleates and PEGs by three independent methods (TGA, 1 H NMR, and ICP-AES). The oleate coverage was very sensitive to the sample isolation/purification protocol, with a high surface coverage (5.5 to 8 nm –2 ) for ethanol/hexane sedimentation/redispersion but only 2 nm –2 if THF was used in place of hexanes. The surface coverages PEG750 (∼1.1 nm –2 ), PEG2000 (∼1.7 nm –2 ), and PEG5000 (∼0.2 nm –2 ) suggest that corona repulsion limits the number of PEG5000 molecules that can graft to the surface. For NaTbF 4 NPs, we compared the surface coverage of PEG2000-monophosphate with a PEG2000-tetraphosphonate ligand shown to provide enhanced colloidal stability in PBS buffer. We found the surprising result that the footprints of these ligands were comparable, suggesting that there was insufficient room for all four phosphonate groups of the tetradentate ligand to bind simultaneously to the NP surface.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".