Role of promoters and catalyst supports for selective synthesis of higher alcohols over molybdenum carbides
Bibliographic record
Abstract
Abstract The synthesis of higher alcohols from syngas is a very promising avenue as syngas can be derived from renewable (biomass) and non‐renewable feedstock (coal, natural gas). To date no commercial process exists for the conversion of syngas into higher alcohols due to poor alcohols yield and selectivity. The search for selective catalysts requires fundamental insight into how to enhance active sites for the formation of alcohols. In the present work, the dispersion of K2CO3 promoted molybdenum carbides over three different supports i.e., highly acidic (ɣ‐alumina), neutral (activated carbon), and basic (magnesium oxide) has been studied. The results revealed that acid sites of ɣ‐alumina expedited the dispersion of the K2CO3 promoter over the molybdenum carbide catalyst and facilitated carbon monoxide dissociation to form C2+ alcohols. The effect of the incorporation of cobalt into the textural and catalytic properties of alumina supported K‐Mo2C was also investigated. The elemental mapping of Co into the K‐Mo2C structure showed the presence of segregated Co and Mo2C islands, but an interaction was observed at the molecular level, resulting in a different H2 temperature‐programmed desorption pattern. With the decreased availability of surface adsorbed hydrogen of the Co promoted K‐Mo2C/Al2O3, the concentration of methanol was significantly reduced and the product selectivity shifted more towards higher alcohols.
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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".