Positive Catalytic Effect and Mechanism of Iron on the Gasification Reactivity of Coke using Thermogravimetry and Density Functional Theory
Bibliographic record
Abstract
The catalytic gasification characteristics and kinetics of metallurgical coke by iron were investigated by non-isothermal thermogravimetry using volumetric (VM), unreacted core (URCM), and random pore (RPM) models. Density functional theory (DFT) calculations were used to analyse the interaction mechanism of CO2 on the iron catalyst surface. Carbon conversion curves were shifted to a lower-temperature zone upon iron addition, indicating the strong catalytic effect of iron on carbon gasification. Kinetic analysis showed that RPM described coke gasification better than VM and URCM, with an RPM activation energy of 197.1–218.1 kJ/mol. DFT calculations indicated that CO2 molecules parallel to the crystal surface can easily interact with the iron surface. Three stable adsorption configurations with energies of −0.59, −0.62, and −0.78 eV were obtained. In the Löwdin population analysis, the C atom acts as a major electron acceptor from Fe. The C and O orbitals overlap with Fe 3d, 4s, and 4p, indicating stronger hybridisation and demonstrating that Fe (001) can activate CO2.
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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".