Development and Characterization of Field Structured Magnetic Composites
Bibliographic record
Abstract
Polymer composites containing ferromagnetic fillers are promising for applications relating to electrical and electronic devices. In this research, the authors modified an ultraviolet light (UV) curable prepolymer to additionally cure upon heating and validated a permanent magnet-based particle alignment system toward fabricating anisotropic magnetic composites. The developed dual-cure acrylate-based resin, reinforced with ferromagnetic fillers, was first tested for its ability to polymerize through UV and heat. Then, the magnetic alignment setup was used to orient magnetic particles in the dual-cure acrylate-based resin and a heat curable epoxy resin system in a polymer casting approach. The alignment setup was subsequently integrated with a material jetting 3D printer, and the dual-cure resin was dispensed and cured in-situ using UV, followed by thermal post-curing. The resulting magnetic composites were tested for their filler loading, microstructural morphology, alignment of the easy axis of magnetization, and degree of monomer conversion. Magnetic characterization was conducted using a vibrating sample magnetometer along the in-plane and out-of-plane directions to study anisotropic properties. This research establishes a methodology to combine magnetic field induced particle alignment along with a dual-cure resin to create anisotropic magnetic composites through polymer casting and additive manufacturing.
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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".