Unlocking Long-Term Stability of Upconversion Nanoparticles with Biocompatible Phosphonate-Based Polymer Coatings
Bibliographic record
Abstract
Achieving long-term (>3 months) colloidal stability of upconversion nanoparticles (UCNPs) in biologically relevant buffers has been a major challenge, which has severely limited practical implementation of UCNPs in bioimaging and nanomedicine applications. To address this challenge, nine unique copolymers formulations were prepared and evaluated as UCNP overcoatings. These polymers consisted of a poly(isobutylene-alt-maleic anhydride) (PIMA) backbone functionalized with different ratios and types of phosphonate anchoring groups and poly(ethylene glycol) (PEG) moieties. The syntheses were done as simple, one-pot nucleophilic addition reactions. These copolymers were subsequently coated onto NaYF4:Yb3+,Er3+ UCNPs, and colloidal stability was evaluated in 1 × PBS, 10 × PBS, and other buffers. UCNP colloidal stability improved (up to 4 months) when coated with copolymers containing greater proportions of anchoring groups and higher phosphonate valences. Furthermore, small molecules could be conjugated to these overcoated UCNPs by use of copper-free click chemistry, as was done to demonstrate suitability for sensor and bioprobe development.
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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 | 0.000 | 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) | 0.002 | 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".