Catalytic Abatement of VOCs: Aerobic Combustion of Methane or Ethane over Alumina-Supported Metal Oxides Recovered from Spent Catalysts
Bibliographic record
Abstract
γ-Al2O3 supported Cu, Cu-Zn and Cu-Ni-Fe-Zn oxide catalysts were prepared using leachate transition metal nitrate and sulfate aqueous solutions from commercial spent catalysts. A bench-scale rig was used to investigate the combustion activity of these catalysts toward methane or ethane in the air stream (1000 ppmv) at a space velocity of 20,000 h-1. The Cu-Ni-Fe-Zn oxides/γ-Al2O3 catalyst proved to be the most active catalyst for the combustion of methane in the temperature range 290-575°C and of ethane in the lower temperature range of 275-525oC as compared to Cu and Cu-Zn oxide loaded catalysts. X-ray powder diffractograms indicated that the metal oxide species were highly dispersed or amorphous on the alumina surface in all the catalysts except for the detection of a minority phase of monoclinic CuO on the Cu-containing mono-metallic catalysts. The co-existence of ZnO in the CuO catalysts suppresses the activity of the copper oxide species and, therefore, the conversion of methane or ethane was reduced. The present research endeavor provides proof-of-concept that relatively inexpensive metal oxide-based heterogeneous catalysts for VOCs abatement can be recovered from spent catalysts. Hence, environmental and health threats of improper handling of VOCs or spent catalysts may be alleviated.
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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".