Improving Lanthanide Nanocrystal Colloidal Stability in Competitive Aqueous Buffer Solutions using Multivalent PEG-Phosphonate Ligands
Bibliographic record
Abstract
The range of properties available in the lanthanide series has inspired research into the use of lanthanide nanoparticles for numerous applications. We aim to use NaLnF(4) nanoparticles for isotopic tags in mass cytometry. This application requires nanoparticles of narrow size distribution, diameters preferably less than 15 nm, and robust surface chemistry to avoid nonspecific interactions and to facilitate bioconjugation. Nanoparticles (NaHoF(4), NaEuF(4), NaGdF(4), and NaTbF(4)) were synthesized with diameters from 9 to 11 nm with oleic acid surface stabilization. The surface ligands were replaced by a series of mono-, di-, and tetraphosphonate PEG ligands, whose synthesis is reported here. The colloidal stability of the resulting particles was monitored over a range of pH values and in phosphate containing solutions. All of the PEG-phosphonate ligands were found to produce non-aggregated colloidally stable suspensions of the nanoparticles in water as judged by DLS and TEM measurements. However, in more aggressive solutions, at high pH and in phosphate buffers, the mono- and diphosphonate PEG ligands did not stabilize the particles and aggregation as well as flocculation was observed. However, the tetraphosphonate ligand was able to stabilize the particles at high pH and in phosphate buffers for extended periods of time.
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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) | 0.000 | 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".