Kinetics of Methanol Oxidation over Mesoporous Perovskite Catalysts
Bibliographic record
Abstract
Abstract By using the nanocasting method, a series of mixed metal perovskite oxides with general formula LaBO 3 (B=Mn, Co, Fe) were synthesized with use of ordered mesoporous KIT‐6 silica as a hard template. Even though the resulting materials were not found to be the exact replica of the template, extremely high values of Brunauer–Emmett–Teller specific surface areas (110–155 m 2 g −1 ) were obtained for the materials. The redox properties of nanocast mesoporous perovskites were determined by performing temperature‐programmed reduction and temperature‐programmed desorption of oxygen. Catalytic activity was monitored by using methanol oxidation as a model reaction over mesoporous LaMnO 3 , and the first kinetic model was developed for the same. Nanocast mesoporous LaMnO 3 catalysts were found to show the highest conversion efficiency for methanol under steady‐state conditions as compared with both LaCoO 3 and LaFeO 3 nanocasts and with LaMnO 3 samples prepared by using other methods. This result is clearly associated with the higher specific surface area of this nanocast perovskite. Furthermore, these materials were found to be stable under conditions prevailing in the reactor. Reaction rates obtained from the experimental conversions at various space velocities (19 500–78 200 h −1 ) for nanocast LaMnO 3 were found to follow a rate equation that depends on the partial pressure of methanol. Using the rate constants obtained, the value of activation energy and pre‐exponential factor were determined from the Arrhenius plot. The calculated values of conversions from the rates modified with surface areas were found to agree with the experimental conversions, which in turn reflect the proportionality of rates to the specific surface area.
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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".