Pyrolysis of Highly Metallized Polymers: Ceramic Thin Films Containing Magnetic CoFe Alloy Nanoparticles from a Polyferrocenylsilane with Pendant Cobalt Clusters
Bibliographic record
Abstract
We describe the pyrolysis of a highly metallized polymer precursor comprised of a polyferrocenylsilane with pendant cobalt clusters under a reductive atmosphere (N 2 /H 2 = 92%/8%) leading to CoFe magnetic alloy nanoparticle-containing ceramic thin films. Variation of the pyrolysis conditions leads to changes in the nanoparticle size, size distribution, and composition, as well as the ceramic film structure, all of which influence the magnetic properties of the material. When pyrolyzed at 500 °C, the nucleation and growth afford uniform size, larger CoFe nanoparticles on the film surface, and smaller nanoparticles in the underlying layer of the ceramic films. We found that the SiC/C ceramic matrix prevents oxidation of fully embedded nanoparticles, whereas the surface nanoparticles are oxidized on exposure to air. The nanoparticle-containing films are superparamagnetic when pyrolyzed at 600 °C and are ferromagnetic at higher pyrolysis temperatures. The CoFe nanoparticle-containing ceramic thin films have been characterized by a variety of techniques, which include scanning electron microscopy, transmission electron microscopy, atomic force microscopy, energy-dispersive X-ray analysis, selected-area electron diffraction, X-ray photoelectron spectroscopy, and the magneto-optical Kerr effect.
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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".