Effects of particle size on the catalytic performance of MWCNTs supported alkalized MoS<sub>2</sub> catalysts promoted by Ni and Co in higher alcohols synthesis
Bibliographic record
Abstract
Abstract A series of nano‐sized alkalized MoS2 catalysts promoted by Ni and Co supported on multi‐walled carbon nanotubes (MWCNTs) were prepared via the reverse microemulsion technique with water to surfactant (W/S) molar ratios (molwater · mol−1surfactant) of 1–12. All prepared catalysts were extensively characterized by ICP, BET, CO chemisorption, XRD, TPR, and TEM techniques. They were assessed in a fixed‐bed micro‐reactor to find their performance in higher alcohol synthesis from syngas. The results were compared with those of the catalyst prepared by the incipient wetness impregnation method with similar elemental composition. The results showed the relationship of catalyst physicochemical properties and performances to preparation method and average metal particle sizes. Calculated average particle sizes for prepared catalysts via microemulsion showed a decreasing trend with a decrease in W/S ratio. It decreased from 17.4 to 5.26 nm for W/S ratios of 12 to 1, respectively. Similarly, the average particle size for prepared catalysts with the impregnation method was 18.78 nm. The nano‐catalyst prepared via microemulsion with W/S of 1 showed the best performance for CO conversion and higher alcohol selectivity of 66.83 and 37.76, respectively. It was concluded that decreased particle sizes increased the active surface area of the Ni and Co promoted alkalized MoS2/MWCNTs catalysts and increased the fraction of metal particles located inside the CNTs, which in turn improved the catalyst performance.
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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".