A Comparative Study of Copper-Promoted Water−Gas-Shift (WGS) Catalysts
Bibliographic record
Abstract
We studied the catalytic water−gas shift reaction over commercial Cu−ZnO−Al 2 O 3 (LTS-C) and Cu-promoted Fe−Cr oxide (HTS-C) catalysts as well as a high-temperature Cu-promoted (UFR-C) catalyst developed in our laboratory. The catalysts were characterized by different techniques such as X-ray diffraction, thermogravimetry/differential scanning calorimetry, temperature-programmed reduction, and sorption analyses. The activities of these catalysts were evaluated in the compositions of mole fractions having 2.6−16.8% CO, 45% H 2 O, and the balance nitrogen in the range of 473−973 K. It was observed that the reduction temperature, the ratio of the mass of the catalyst to the mass flow rate of CO (g (cat) h/mol of CO), and CO concentrations have significant effects on the catalytic activities. In the presence of 2.6% CO, the Cu−ZnO−Al 2 O 3 catalyst was most active at 473 K while the Cu-promoted Fe−Cr oxide catalyst was most active at 773 K. The catalytic activities of HTS-C and LTS-C were compared with that of UFR-C in the presence of both CO in different concentrations and reformate streams as a feed. The results showed that ceria−zirconia-supported non-noble metal catalysts can give very high water−gas shift activity at very short contact times compared to that of the commercial water−gas shift catalyst.
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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".