Dimerization of Linear Butenes on Zeolite-Supported Ni<sup>2+</sup>
Bibliographic record
Abstract
Nickel- and alkali-earth-modified LTA based zeolites catalyze the dimerization of 1-butene in the absence of Brønsted acid sites. The catalyst reaches over 95% selectivity to n -octenes and methylheptenes. The ratio of these two dimers is markedly influenced by the parallel isomerization of 1-butene to 2-butene, shifting the methylheptene/octene ratio from 0.7 to 1.4 as the conversion increases to 35%. At this conversion, the thermodynamic equilibrium of 90% cis - and trans -2-butenes is reached. Conversion of 2-butene results in methylheptene and dimethylhexene with rates that are 1 order of magnitude lower than those with 1-butene. The catalyst is deactivated rapidly by strongly adsorbed products in the presence of 2-butene. The presence of π-allyl-bound butene and Ni-alkyl intermediates was observed by IR spectroscopy, suggesting both to be reaction intermediates in isomerization and dimerization. Product distribution and apparent activation barriers suggest 1-butene dimerization to occur via a 1′-adsorption of the first butene molecule and a subsequent 1′- or 2′-insertion of the second butene to form octene and methylheptene, respectively. The reaction order of 2 for 1-butene and its high surface coverage suggest that the rate-determining step involves two weakly adsorbed butene molecules in addition to the more strongly held butene.
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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) | 0.002 | 0.001 |
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".