Robust Ni<sub><i>x</i></sub>Sn/ZSM-12 Catalysts with Zeolite as the Support and Sn as the Promoter for Acetylene Semi-hydrogenation
Bibliographic record
Abstract
The synthesis of ethylene from semi-hydrogenation of acetylene is significant for the upgrading of coal. Different kinds of materials (SiO 2, γ-Al 2 O 3, and ZSM-12)-supported Ni catalysts were prepared and evaluated for the acetylene hydrogenation. It demonstrated that the Ni/ZSM-12 catalyst with synergistic effect of B (Brönsted) acid and L (Lewis) acid sites can promote semi-hydrogenation of acetylene better than those over Ni/SiO 2 and Ni/γ-Al 2 O 3 catalysts, in which both only have L acid sites. However, the pure Ni/ZSM-12 still exhibited low selectivity of ethylene and poor stability; thus, the Sn promoter was introduced into the Ni/ZSM-12 catalyst. Due to the geometric and electronic effects, the Ni 7 Sn/ZSM-12 sample achieves higher yield of ethylene (92.51%) with ethylene selectivity of 92.51% and acetylene conversion of 100% at 250 °C, which is an increase of 19.38% over the samples without Sn addition. Moreover, the stability of the Ni 7 Sn/ZSM-12 catalyst (100 h) is much better than those catalysts without Sn (14 h). This is because the carbon deposition over the Sn-containing catalyst was light hydrocarbons that could be removed at high temperature while the pure Ni/ZSM-12 catalyst produced heavy hydrocarbons. The outstanding performance of Ni 7 Sn/ZSM-12 was further illustrated by DFT calculations, which originates from its facilely accessible hydrogen dissociation, lower ethylene adsorption energy, and higher energy barrier for the formation of C 2 H 5 * intermediate.
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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) | 0.001 | 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 | 0.001 | 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".