A Systematic Study of the Structural and Magnetic Properties of Mn-, Co-, and Ni-Doped Colloidal Magnetite Nanoparticles
Bibliographic record
Abstract
A series of colloidal M x Fe 3– x O 4 (M = Mn, Co, Ni; x = 0–1) nanoparticles with diameters ranging from 6.8 to 11.6 nm was synthesized by hydrothermal reaction in aqueous medium at low temperature (200 °C). Energy-dispersive X-ray microanalysis and inductively coupled plasma spectrometry confirm that the actual elemental compositions agree well with the nominal ones. The structural properties of the obtained nanoparticles were investigated by powder X-ray diffraction, Raman spectroscopy, Mössbauer spectroscopy, X-ray and neutron pair distribution function analysis, and electron microscopy. The results demonstrate that our synthesis technique leads to the formation of chemically uniform single-phase solid solution nanoparticles with cubic spinel structure, confirming intrinsic doping. The local structure of the Fe 3 O 4 NPs is distorted with respect to the cubic inverse-spinel structure, while chemical substitution of Fe by Mn or Ni partially eliminates the local distortions. Magnetic studies showed that, in comparison to nondoped Fe 3 O 4, the saturation magnetization ( M s ) of M x Fe 3– x O 4 (M = Mn, Ni) decreases with increasing dopant concentration, while Co-doped samples showed similar M s . On the other hand, whereas Mn- and Ni-doped nanoparticles exhibit superparamagnetic behavior at room temperature, ferrimagnetism emerges for Co x Fe 3– x O 4 nanoparticles, which can be tuned by the level of Co doping.
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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".