SiCNFe Ceramics as Soft Magnetic Material for MEMS Magnetic Devices: A Mössbauer Study
Bibliographic record
Abstract
Polymer-derived SiCNFe ceramics is a prospective material that can be used as soft magnets in MEMS magnetic applications. The optimal synthesis process and low-cost appropriate microfabrication should be developed for best result. Homogeneous and uniform magnetic material is required for developing such MEMS devices. Therefore, the knowledge of exact composition of SiCNFe ceramics is very important for the microfabrication of magnetic MEMS devices. The Mössbauer spectrum of SiCN ceramics, doped with Fe (III) ions, and annealed at 1100 °C, was investigated at room temperature to accurately establish the phase composition of Fe-containing magnetic nanoparticles, which were formed in this material at pyrolysis and which determine their magnetic properties. The analysis of Mössbauer data shows the formation of several Fe-containing magnetic nanoparticles in SiCN/Fe ceramics, such as α-Fe, FexSiyCz, traces of Fe-N and paramagnetic Fe3+ with octahedral oxygen environment. The presence of iron nitride and paramagnetic Fe3+ ions shows that the pyrolysis process was not completed in SiCNFe ceramics annealed at 1100 °C. These new observations confirm the formation of different Fe-containing nanoparticles with complex composition in SiCNFe ceramic composite.
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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".