Kinetics of nonisothermal reduction and carburization of WO<sub>3</sub>─NiO nano‐composite powders by CO─CO<sub>2</sub>
Bibliographic record
Abstract
Abstract The kinetics of simultaneous reduction and carburization of WO3─NiO nanocomposite powders by CO/CO2 was studied using a nonisothermal thermogravimetric analysis. The experiments were carried out at heating rates between 5 and 15°C/min, showing that the thermochemical processes can be divided into four steps, each dominated by a reaction, as following: NiO → Ni, WO3 → WO2, WO2 → W, and W → WC. The apparent activation energy for each step was obtained based on the Flynn‐Wall‐Ozawa isoconversional method for the individual steps, and the kinetic model was assessed by fitting master plots of various kinetic models for these steps at different heating rates. The Avrami‐Erofeev kinetic model was found to fit to the third and fourth steps and main part of the first, and the geometric contracting model fitted the best for the second step. Changing the heating rate did not affect the master plots of the third step. However, for the first step, increasing the heating rate made the Avrami‐Erofeev model the best‐fitting correlation and also for the second step the matching model changed at the highest heating rate (15°C/min) from a two‐dimensional contracting model (cylindrical particles) to a three‐dimensional contracting model (spherical particles).
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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".