A facile approach for obtaining NiFe2O4/C nanocomposites and their magnetic properties assessment
Bibliographic record
Abstract
Abstract NiFe2O4/C nanocomposites were prepared using the two-step process, including reverse microemulsion followed by thermochemical vapour deposition (TCVD). Micelles made in the first step worked well and NiFe2O4 nanoparticles owing sizes smaller than 10 nanometer with narrow size distribution were obtained. Acetylene gas was used as a carbon source for deposition on the surface of NiFe2O4 nanoparticles. Coating applied for 1 and 2 h at 700°C and resulted in an enhancement of NiFe2O4 nanoparticle size to a level of 30 nanometer. Powder X-ray diffraction (PXRD) patterns revealed the spiel formation for all samples; however, the crystallinity of them has been increased as much as temperature rose to a level of coating. Fourier transform infrared spectroscopy (FT-IR) verified the metal-oxygen bonding linked to the octahedral and tetrahedral vibrations. Micro-Raman spectra for the coated samples were recorded as well, and the results confirmed the existence of ordered and disordered carbon-based materials in nanocomposites. It is noteworthy to mention that, in the sample with 2 h coating time, the defect densities were lower than the sample with an hour coating time. The vibration sample magnetometer (VSM) was also used to investigate the magnetic properties. The as-prepared sample behaves like a superparamagnetic matter; however, the coated ones behave like ferromagnetic materials. The saturation magnetization and coercivity of coated samples were lower than their bulk counterparts which was due to their smaller size.
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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".