An Effective Polymer Cross-Linking Strategy To Obtain Stable Dispersions of Upconverting NaYF<sub>4</sub> Nanoparticles in Buffers and Biological Growth Media for Biolabeling Applications
Bibliographic record
Abstract
Ligands on the nanoparticle surface provide steric stabilization, resulting in good dispersion stability. However, because of their highly dynamic nature, they can be replaced irreversibly in buffers and biological medium, leading to poor colloidal stability. To overcome this, we report a simple and effective cross-linking methodology to transfer oleate-stabilized upconverting NaYF(4) core/shell nanoparticles (UCNPs) from hydrophobic to aqueous phase, with long-term dispersion stability in buffers and biological medium. Amphiphilic poly(maleic anhydride-alt-1-octadecene) (PMAO) modified with and without poly(ethylene glycol) (PEG) was used to intercalate with the surface oleates, enabling the transfer of the UCNPs to water. The PMAO units on the phase transferred UCNPs were then successfully cross-linked using bis(hexamethylene)triamine (BHMT). The primary advantage of cross-linking of PMAO by BHMT is that it improves the stability of the UCNPs in water, physiological saline buffers, and biological growth media and in a wide range of pH values when compared to un-cross-linked PMAO. The cross-linked PMAO-BHMT coated UCNPs were found to be stable in water for more than 2 months and in physiological saline buffers for weeks, substantiating the effectiveness of cross-linking in providing high dispersion stability. The PMAO-BHMT cross-linked UCNPs were extensively characterized using various techniques providing supporting evidence for the cross-linking process. These UCNPs were found to be stable in serum supplemented growth medium (37 °C) for more than 2 days. Utilizing this, we demonstrate the uptake of cross-linked UCNPs by LNCaP cells (human prostate cancer cell line), showing their utility as biolabels.
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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) | 0.001 | 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 | 0.001 |
| 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".