Alkali-Promoted Trimetallic Co−Rh−Mo Sulfide Catalysts for Higher Alcohols Synthesis from Synthesis Gas: Comparison of MWCNT and Activated Carbon Supports
Bibliographic record
Abstract
Multiwalled carbon nanotubes (MWCNTs) and activated carbon were used as supports for the Co (4.5 and 6 wt %) promoted K (9 wt %) modified Rh−Mo catalysts (1.5 wt % Rh and 15 wt % Mo). The catalysts were extensively characterized in both oxide and sulfide phases. A drastic fall in surface area over the activated carbon-supported catalysts was observed after impregnating with metal species. Diffraction peaks were observed in the X-ray diffraction (XRD) patterns of the sulfided alkali-modified trimetallic catalysts, due to the characteristic reflections of the K−Mo−S mixed phase. H 2 -temperature programmed reduction (TPR) profiles showed that the reduction behavior of metal species was improved with the addition of Co. The activated carbon-supported trimetallic catalysts showed less activity and selectivity compared to the MWCNT-supported catalyst, and metal dispersions were higher on the MWCNT-supported catalysts. The MWCNT-supported, alkali-promoted trimetallic catalyst with 4.5 wt % Co showed the highest total alcohols yield of 0.244 g/(g cat h), ethanol selectivity of 20.1%, and higher alcohols selectivity of 31.4% at 320 °C and 8.28 MPa using a gas hourly space velocity (GHSV) of 3.6 m 3 (STP)/(kg catalyst h). A maximum total alcohol yield of 0.261 g/(g cat h) and a selectivity of 42.9% were obtained on the 4.5 wt % Co−Rh−Mo−K/MWCNT catalyst, at a temperature of 330 °C. The total alcohol yield increased from 0.163 to 0.256 g/(g cat h) with increased pressure from 5.52 MPa (800 psig) to 9.65 MPa (1400 psig) over the 4.5 wt % Co−Rh−Mo−K/MWCNT 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".