Supraparticles from Cubic Iron Oxide Nanoparticles: Synthesis, Polymer Encapsulation, Functionalization, and Magnetic Properties
Bibliographic record
Abstract
Supraparticles (SPs) consisting of superparamagnetic iron oxide nanoparticles (SPIONs) are of great interest for biomedical applications and magnetic separation. To enable their functionalization with biomolecules and to improve their stability in aqueous dispersion, polymer shells are grown on the SPs' surface. Robust polymer encapsulation and functionalization is achieved via atom transfer radical polymerization (ATRP), improving the reaction control compared to free radical polymerizations. This study presents the emulsion-based assembly of differently sized cubic SPIONs (12-30 nm) into SPs with diameters ranging from ∼200 to ∼400 nm using dodecyltrimethylammonium bromide (DTAB) as the surfactant. The successful formation of well-defined spherical SPs depends upon the method used for mixing the SPION dispersion with the surfactant solution and requires the precise adjustment of the surfactant concentration. After purification, the SPs are encapsulated by growing surface-grafted polystyrene shells via activators generated by electron transfer (AGET) ATRP. The polymer shell can be decorated with functional groups (azide and carboxylate) using monomer blends for the polymerization reaction. When the amount of the monomer is varied, the shell thickness as well as the interparticle distances between the encapsulated SPIONs can be tuned with nanometer-scale precision. Small-angle X-ray scattering (SAXS) reveals that cubic SPIONs form less ordered assemblies within the SPs than spherical SPIONs. As shown by vibrating sample magnetometer measurements, the encapsulated SPs feature the same superparamagnetic behavior as their SPION building blocks. The saturation magnetization ranges between 10 and 30 emu/g and depends upon the nanocubes' size and phase composition.
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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) | 0.000 | 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".