Issues Affecting the Synthetic Scalability of Ternary Metal Ferrite Nanoparticles
Bibliographic record
Abstract
Ternary Mn-Zn ferrite (MnxZn1-xFe2O4) nanoparticles (NPs) have been prepared by the thermal decomposition of an oleate complex, sodium dodecylbenzenesulfonate (SDBS) mediated hydrazine decomposition of the chloride salts, and triethylene glycol (TREG) mediated thermal decomposition of the metal acetylacetonates. Only the first method was found to facilitate the synthesis of uniform, isolable NPs with the correct Mn : Zn ratio (0.7 : 0.3) as characterized by small angle X-ray scattering (SAXS), transmission electron microscopy (TEM), and inductively coupled plasma-optical emission spectroscopy (ICP-OES). Scaling allowed for retention of the composition and size; however, attempts to prepare Zn-rich ferrites did not result in NP formation. Thermogravimetric analysis (TGA) indicated that the incomplete decomposition of the metal-oleate complexes prior to NP nucleation for Zn-rich compositions is the cause.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".