Presulfurized Hydrotreating Catalysts Prepared from Tetrathiotungstate- Intercalated NiZnAl Layered Double Hydroxides
Bibliographic record
Abstract
Zn was introduced into the sheets of WS 4 2– -intercalated NiAl layered double hydroxides (LDHs), followed by calcination under N 2 to prepare a series of presulfurized NiZnWAl hydrotreating (HT) catalysts. The influences of calcination temperature and Ni/Zn ratio on the properties, hydrodesulfurization (HDS) activity for dibenzothiophene (DBT), and hydrodearomatization (HDA) activity for tetralin (THN) of the catalysts were investigated. The incorporation of Zn led to the formation of ZnS on the surface and reduced the thermal stability of the LDHs. During the decomposition of LDHs, WS 3 and NiWS phases were formed successively, which are respectively related to HDA and HDS. Both WS 3 and NiWS decreased as the calcination temperature increased, due to the deep decomposition and the aggregation of metal sulfides. The catalyst calcined at 300 °C shows the highest HDS and HDA activities. Zn-containing catalysts exhibited much higher HDS activity but lower HDA activity compared to the catalyst without Zn. The rate constants k HDS and k HDA of the catalyst with a Ni/Zn ratio of 0.2/1.8 were, respectively, 2.14 times higher and 1.97 times lower than those of the catalyst without Zn. For Zn-containing LDHs, the decomposition of interlayer WS 4 2– was accelerated, leading to a decrease in WS 3 . Moreover, ZnS on the surface could enhance spillover hydrogen (H so ) to form coordinately unsaturated sites (CUS) and further induces highly unsaturated CUS to depress the HYD activity. This work might uncover a novel and convenient way to tune the HDS and HDA activities of LDH-based presulfurized HT catalysts by introducing Zn, which is promising for deep HDS and controllable HDA of diesel fractions.
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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) | 0.000 | 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 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".