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 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 | 0.000 | 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 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".