Chain Length Effect of the Multidentate Block Copolymer Strategy to Stabilize Ultrasmall Fe<sub>3</sub>O<sub>4</sub> Nanoparticles
Bibliographic record
Abstract
Abstract A multidentate block copolymer (MDBC) strategy to stabilize ultrasmall superparamagnetic iron oxide nanoparticles (USNPs) as biocompatible T1‐positive contrast agents for magnetic resonance imaging (MRI) is explored. The MDBC is designed with a poly(methacrylic acid) (PMAA) block having pendant carboxylates as multidentate anchoring groups and a hydrophilic polymethacrylate block having pendant oligo(ethylene oxide) chains (POEOMA). A series of multifunctional MDBCs with different chain lengths of POEOMA and PMAA blocks were synthesized by atom transfer radical polymerization and subsequent hydrolytic cleavage. The resultant aqueous MDBC/USNP colloids fabricated through a ligand‐exchange process were characterized to investigate the effect of chain lengths of POEOMA‐b‐PMAA copolymers on size, morphology, and colloidal stability as well as relaxometric properties and in vitro MRI performance. The results suggest that the optimal design of chain lengths of both anchoring and hydrophilic blocks is required for enhanced colloidal stability under biologically relevant conditions as well as effective T1‐weighted contrast enhancement.
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 | 0.000 | 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 | 0.000 |
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".