Mussel-Inspired Multidentate Block Copolymer to Stabilize Ultrasmall Superparamagnetic Fe<sub>3</sub>O<sub>4</sub> for Magnetic Resonance Imaging Contrast Enhancement and Excellent Colloidal Stability
Bibliographic record
Abstract
Colloidal ultrasmall superparamagnetic iron oxide nanoparticles (USNPs) with better control of their surface chemistry have been considered as a biocompatible alternative to clinically used gadolinium-based contrast agents for in vivo bright magnetic resonance imaging (MRI). Herein, we report a versatile mussel-inspired multidentate block copolymer strategy that allows for the stabilization of USNPs as promising MRI contrast agents with excellent colloidal stability. A well-controlled multidentate block copolymer with pendant multiple catechol groups (Cat-MDBC) is synthesized by a combination of controlled radical polymerization and postmodification methods. The Cat-MDBC proves to be effective to strongly anchor to USNP surfaces as well as provide optimal hydrophilic surfaces, thus enabling the fabrication of aqueous Cat-MDBC/USNP colloids at single layers with a diameter of ≈20 nm through a biphasic ligand exchange process. They exhibit excellent colloidal stability in broad pH range and physiological conditions; no significant protein adsorption; and great magnetic properties including relaxivity and in vitro MRI. Further comparison of Cat-MDBC with its corresponding catechol-based multidentate random copolymer suggests the importance of the architecture of multidentate polymeric ligands for USNP-based MRI diagnosis.
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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".