Bauxite residue as an iron‐based catalyst for catalytic cracking of naphthalene, a model compound for gasification tar
Bibliographic record
Abstract
Abstract An iron‐based catalyst from bauxite residue (aka BR and red mud) was developed for removing biomass gasification tar. Its performance was investigated with naphthalene as the model tar compound. This was achieved by measuring the catalytic naphthalene conversion at five space velocities and at four temperatures in the 500°C to 800°C range, both in a N2 environment and in 13 vol% H2 with the balance N2 for 14 hours to determine the long‐term performance. The physical and chemical characteristics of the catalyst were studied prior to and after exposure to naphthalene to track the evolution of the catalyst as a result of the chemical reaction. In addition, the effects of calcination temperature and reduction with H2 on the surface characteristics were investigated. The bauxite residue catalyst was shown to be significantly active for naphthalene cracking, with its activity comparable to that of an industrial Ni catalyst. Activity measurements over 14 hours of testing showed that the catalyst activity decreased from 98% to 65% naphthalene conversion with time as a result of catalyst deactivation when tested in a N2 reaction environment. In the presence of 13 vol% H2; however, the activity maintained >95% conversion for the entire duration of the experiment.
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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".