Modulating Magnetic Properties of Ferrite Cu1-XMgXFe2O4 via RF Plasma Exposure: Effects of Magnesium Concentration
Bibliographic record
Abstract
In this study, the magnetic compound Cu1-xMgxFe2O4 is synthesized using the sol-gel method at distinct magnesium concentrations (x = 0, 0.2, 0.4, and 0.6) to investigate the influence of RF plasma exposure on its structural and magnetic properties.X-ray diffraction analysis, applied to examine the prepared ferrite, confirms the formation of the face-centered cubic (FCC) structure in the samples.Utilizing X-ray diffraction broadening and Scherrer's equation for particle size determination, sizes are found to range from 37.73 nm to 19.870 nm prior to plasma exposure and 30.35 nm to 19.115 nm subsequent to exposure.The study of the compound's magnetic properties demonstrates that the saturation magnetization spans from 33.5 to 32.1 emu/g, with an initial coercivity decrease from 150 to 50 Oe, further diminishing as magnesium concentration increases.Notably, following plasma exposure, alterations in saturation magnetization values (35.32-27.4emu/g) and coercivity (150-25 Oe) are observed.The results underscore the impact of rising magnesium concentrations and plasma exposure on the compound's structural properties, potentially attributable to atomic rearrangement within the crystalline structure.In terms of magnetic properties, a reduction in coercive force following plasma exposure is discerned, thereby enhancing the ferrite's properties, which are applicable in transformer cores to minimize eddy currents.
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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".