Effect of Excess Ligand on the Reverse Microemulsion Silica Coating of NaLnF<sub>4</sub> Nanoparticles
Bibliographic record
Abstract
Silica coating of inorganic nanoparticles (NPs) is widely employed as a means of providing colloidal stability in aqueous media and surface functionality for a variety of applications, particularly in biology. When the NPs are synthesized with a surface coating of an organic surfactant like oleic acid, silica coating is performed by using the reverse microemulsion method. There are many reports in the literature of the successful application of this method to NaYF 4 upconversion NPs (doped with Yb and Er), and we have used this method to coat NaHoF 4 NPs designed as a mass cytometry reagent. This method failed when we attempted to apply it to other NaLnF 4 NPs (Ln = Sm, Eu, Tb). In this report we describe an investigation of the problem and show how it can be overcome. To control size in the synthesis of NaLnF 4 NPs and at the same time maintain size uniformity, it is necessary to adjust the Na/F and F/Ln ratios. Problems with silica coating are associated with substoichiometric F/Ln ratios (F/Ln < 4) that leave Ln oleate salts as a byproduct, often as a phase-separated oily layer that could not be purified from the NPs by precipitation with ethanol and redispersion in hexanes. The nature of the oily byproduct was inferred from a combination of TGA, NMR, and FTIR measurements. We explored five different additional purification procedures, and by adopting the appropriate purification method, NaLnF 4 NPs with a variety of compositions and synthesized using different reaction conditions could be coated with a thin shell of silica.
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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 | 0.001 |
| 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".