Investigating the Competitive Adsorption of Polymer-Bisphosphonate Ligands on Lanthanide Nanoparticles
Bibliographic record
Abstract
We describe the binding of five “stealth” polymers to the surface of NaYF 4:Yb,Er NPs with diameters of 20 and 24 nm. All polymers had a neridronate (Ner) end group that provided a bisphosphonate to promote strong binding to the lanthanide NP (LnNP) surface. The polymers included four poly(ethylene glycol) (PEG) samples with M n = 2000 and 5000 and a zwitterionic poly(sulfobetaine methacrylate) (PSBMA, DP = 35). At surface saturation, the polymers had densities ranging from 2.3 to 3.8 nm 2 /polymer, and all provided excellent colloidal stability in PBS buffer. Ligand exchange experiments for samples aged 24 h after treatment with the first polymer ligand showed that PEG-Ner was more effective at displacing PSBMA-Ner than the reverse reaction. One surprising result is that PEG-Ner polymers with a distal azide group bound to the nanoparticles (NPs) at higher surface density than the corresponding methoxy-PEG polymers. Another set of surprising results is that when methoxy-PEG-Ner samples competed with PSBMA-Ner, more PSBMA than PEG became bound to the surface. However, when the azide-PEG-Ner samples competed with PSBMA-Ner, more PEG than PSBMA became bound to the surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".