NiFe<sub>2</sub>O<sub>4</sub> production from α‐Fe<sub>2</sub>O<sub>3</sub> via improved solid state reaction: Application as catalyst in CH<sub>4</sub> dry reforming
Bibliographic record
Abstract
A new, less‐material‐intensive, and fast protocol was developed to synthesize NiFe 2 O 4 spinel from hematite α‐Fe 2 O 3 . This is a solid state reaction at relatively low severity and short milling time (2–10 min). The product was characterized by X‐ray diffraction (XRD), scanning electron microscopy coupled with energy dispersive X‐ray spectroscopy, temperature‐programmed reduction, thermogravimetric analysis, and BET specific surface area. The produced NiFe 2 O 4 was tested as a methane dry reforming catalyst (CH 4 + CO 2 ) to compare its activity with similar Ni‐Fe spinels reported in the literature. XRD revealed that under stoichiometric conditions, the resulting formulation contains only pure NiFe 2 O 4 , in a crystalline spinel structure. The catalytic performance of NiFe 2 O 4 during CH 4 dry reforming, at 800 °C for 4 h and a stoichiometric molar ratio of CO 2 /CH 4 = 1, is described by the following results: CH 4 conversion rose to about 40 % after 30 min over time‐on‐stream (TOS), then decreased more slowly to 25 % after 4 h of TOS. Over the same period, hydrogen (H 2 ) yield increased to 50 % during the first 1 h of TOS, then following the same pattern as CH 4 conversion, it dropped to 30 % over the next 4 h of TOS. These results show that the tested NiFe 2 O 4 is better than those reported in the literature for similar catalytic use of nanometric NiFe 2 O 4 .
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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".