Steam catalytic cracking of coal tar over iron‐containing mixed metal oxides
Bibliographic record
Abstract
Abstract Steam catalytic cracking is deemed as a promising method for the catalytic cracking of coal tar. In this study, a serial of iron‐containing mixed metal oxides including ceria, zirconia, alumina, cobalt, nickel, and barium oxides were prepared for steam catalytic cracking of coal tar and characterized by N2 adsorption‐desorption, X‐ray diffraction, H2 temperature programmed reduction, and Raman spectroscopy, respectively. It was found that the specific surface area and pore volume increase while the crystal size decreases when Fe2O3 was doped with the investigated metal oxides. Among these, iron‐alumina mixed oxide (FeAl) shows the highest specific surface area and pore volume. The H2‐TPR analysis indicated that most of the mixed metal oxides have lower reduction temperature, especially for iron‐ceria mixed oxide (FeCe). The reduction temperature of FeCe is around 500 °C, and its reduction peak shifts to a lower temperature and becomes narrower as compared to other iron‐containing mixed metal oxides. The steam catalytic cracking of coal tar at 550 °C showed that the light tar content (below 360 °C) over FeCe, FeCo, and FeAl are 68.5, 67.0, and 66.5 %, respectively. The increase of CO2 and H2 over iron‐containing mixed metal oxides suggested that oxygen species from iron‐containing mixed metal oxides and steam could participate in the steam catalytic cracking of coal tar.
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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".