Catalyst development and kinetics for methanol fuel processing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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 it