Synthesis of Monodisperse Biotinylated p(NIPAAm)-Coated Iron Oxide Magnetic Nanoparticles and their Bioconjugation to Streptavidin
Bibliographic record
Abstract
We describe here the synthesis of 10 nm, monodisperse, iron oxide nanoparticles that we have coated with temperature-sensitive, biotinylated p(NIPAAm) (b-PNIPAAm). The PNIPAAm was prepared by the reversible addition fragmentation chain transfer polymerization (RAFT), and one end was biotinylated with a PEO maleimide-activated biotin to form a stable thioether linkage. The original synthesized iron oxide particles were stabilized with oleic acid. They were dispersed in dioxane, and the oleic acid molecules were then reversibly exchanged with a mixture of PNIPAAm and b-PNIPAAm at 60 degrees C. The b-PNIPAAm-coated magnetic nanoparticles were found to have an average diameter of approximately 15 nm by dynamic light scattering and transmission electron microscopy. The ability of the biotin terminal groups on the b-PNIPAAm-coated nanoparticles to interact with streptavidin was confirmed by fluorescence and surface plasmon resonance. It was found that the b-PNIPAAm-coated iron oxide nanoparticles can still bind with high affinity to streptavidin in solution or when the streptavidin is immobilized on a surface. We have also demonstrated that the binding of the biotin ligands on the surface of the temperature-responsive magnetic nanoparticles to streptavidin can be turned on and off as a function of temperature.
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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) | 0.000 | 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 | 0.001 |
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".