Exploring the Roles of Chelating/Fuel Agents in Shaping the Properties of Copper Ferrites
Bibliographic record
Abstract
In this study, copper ferrite nanoparticles, a type of ferrimagnetic spinel ferrite, were synthesized using the sol-gel auto-combustion method with three different fuels: citric acid, urea, and ethylene glycol. The crystal structures of the synthesized samples were analyzed using X-ray diffraction (XRD), and the growth of secondary phases like Fe2O3 and CuO for samples prepared with urea and ethylene glycol indicated the presence of impurities. Additionally, we observed that the particle size varied significantly with the type of fuel, being the smallest for citric acid and the largest for urea. The electrical and magnetic properties showed strong correlations with the particle size and the presence of impurities. In particular, the optical band gap values, derived from UV-Vis spectroscopy, varied significantly with the choice of fuel, ranging from 2.06 to 3.75 eV. The highest band gap of 3.75 eV was observed in samples synthesized with citric acid. Magnetic properties were measured using a vibrating sample magnetometer (VSM), and it was found that the copper ferrite synthesized with citric acid exhibited the highest values of magnetic saturation and coercivity. These findings demonstrate that the choice of fuel during the synthesis process has substantial impacts on the structural, optical, and magnetic properties of CuFe2O4 nanoparticles.
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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) | 0.000 | 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".