Catalyst development and kinetics for methanol fuel processing
Bibliographic record
Abstract
Abstract The reactions involved in methanol fuel processing are discussed. It is stated that the direct reaction of methanol and steam to form carbon dioxide and hydrogen is the key hydrogen producing reaction. Catalysis by Cu/ZnO/Al 2 O 3 is described in general terms. The literature on the kinetics of methanol–steam reforming is described in detail. Early kinetic models that were adequate for a limited range of operating conditions are discussed. The importance of understanding the surface reaction mechanism for developing models that are valid over wide ranges of conditions is described, followed by a description of the evolution of surface mechanisms for the process leading to a comprehensive kinetic model. The usefulness of this mechanistically‐based model is described in detail. The relative importance of the water–gas shift reaction, in particular, is revealed. Subsequently, the limitations and operating problems associated with Cu‐based catalysts are discussed. Both deactivation and pyrophoric behavior are cited as major problems. Finally, alternatives to Cu‐base catalysts are discussed. These include Ni‐hydrotalcites and Pt on ceria. Although these catalysts have lower activity than Cu‐based catalysts at temperature below 300 °C, their thermal stability at temperatures as high as 390 °C makes them more practical in fuel processors for methanol‐fuelled fuel cell systems.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".