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 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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".