Dispersed Ni and Co Promoted MoS <sub>2</sub> Catalysts with Magnetic Greigite as a Core: Performance and Stability in Hydrodesulfurization
Bibliographic record
Abstract
Abstract Core‐shell composites MoS 2 /Fe 3 S 4 , CoMoS/Fe 3 S 4 , NiMoS/Fe 3 S 4 with greigite as the core were synthesized and compared to MoS 2 /Fe 3 O 4 for hydrodesulfurization (HDS). The composites with a greigite core were found to have higher hydrogenation (HYD) selectivity in HDS of dibenzothiophene (DBT) compared to the one with a magnetite core and showed higher stability during different cycles of DBT HDS test. Cobalt and Nickel as a promoter are uniformly dispersed over MoS 2 and decorated on the layer‐structured MoS 2 . NiMoS/Fe 3 S 4 showed the highest activity in the hydrodesulfurization of DBT and 4,6‐Dimethyldibenzothiophene (4,6‐DMDBT) followed by CoMoS/Fe 3 S 4 and MoS 2 /Fe 3 S 4 . For the promoted catalysts, the direct desulfurization (DDS) pathway was found to be more selectively enhanced than the HYD route in HDS of DBT. HYD pathway was the dominant route in HDS of 4,6‐dimethyldibenzothiophene (4,6‐DMDBT). Moreover, they have high stability, and the magnetic properties associated with these nano‐composites makes them an excellent choice for hydroprocessing of heavy petroleum oils in slurry reactors as they can be easily separated and reused.
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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".