Permanent encoding of nano‐ to macro‐scale hierarchies of order from evaporative magnetic fluids
Bibliographic record
Abstract
Abstract Magnetic field‐directed assemblies of magnetic nanoparticles (MNPs) in ferrofluids exhibit complex interconvertible metastable patterns and structures. Formally, ferrofluid patterns are unstable – they disappear when the magnetic field is removed. The present study shows that ferrofluid patterns can be “trapped” as kinetically stable structures that encode a surprising degree of morphological detail over nanometer to millimeter length scales. An external magnetic field is used to direct assembly of oleic acid‐decorated magnetite (Fe 3 O 4 ) nanoparticles to make spike and labyrinthine patterns in volatile host solvents of heptane, octane and nonane. Solvent evaporation coupled with increases in sample magnetization drive pattern formation and its permanent recording. Use of a crosslinking siloxane polymer host yields remarkably different material responses. From the trapped states in both fluid systems, previously unreported hierarchies of order emerge in nanocomposite spike structures that also exhibit orientational and magnetic anisotropy. The possibility of designing hierarchical matter from initially uncorrelated MNPs is demonstrated by a directed solid‐state transformation of the magnetic nanocomposite; the spikes template memory of their origin onto the transformation products.
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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 | 0.000 |
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".