Coating and Structural Locking of Dipolar Chains of Cobalt Nanoparticles
Bibliographic record
Abstract
Above a critical size, Co nanoparticles aggregate because of magnetic dipole-dipole interaction into chains. Reported in this paper is the coating of such chains by an AB diblock copolymer in a block-selective solvent for the A block. Also reported is the cross-linking of the deposited or anchored B block of the diblock copolymer to lock in the coating and thus the dipolar chain structure. The Co nanoparticles used were prepared from the high-temperature decomposition of Co(2)(CO)(8) using poly(2-cinnamoyloxyethyl methacrylate)-block-poly(acrylic acid) or PCEMA-b-PAA as surfactant. To coat the dipolar chains, the particles and diblock copolymer poly(tert-butyl acrylate)-block-poly(2-cinnamoyloxyethyl methacrylate), PtBA-b-PCEMA, were dispersed in a good solvent for PCEMA and PtBA. Methanol, a precipitant for PCEMA and a good solvent for PtBA, was then added. This induced the collapsing of the PCEMA blocks and the deposition of the PCEMA block of PtBA-b-PCEMA onto the surface of PCEMA-b-PAA-coated Co nanoparticle chains. The dipolar chains remained colloidally stable in solution for steric stabilization provided by PtBA. The coating was cured by photocrosslinking the PCEMA layer. Such "permanent" and solvent-dispersible Co dipolar chains are novel and may have interesting applications.
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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".