Suppressing Dehydroisomerization Boosts <i>n</i>‐Butane Dehydrogenation with High Butadiene Selectivity
Bibliographic record
Abstract
Abstract Butadiene (BD) is a critical raw material in chemical industry, which is conventionally produced from naphtha cracking. The fast‐growing demand of BD and the limited oil reserve motivate chemists to develop alternative methods for BD production. Shale gas, which mainly consists of light alkanes, has been considered as cheap raw materials to replace oil for BD production via n‐butane direct dehydrogenation (n‐BDH). However, the quest for highly‐efficient catalysts for n‐BDH is driven by the current drawback of low BD selectivity. Here, we demonstrate a strategy for boosting the selectivity of BD by suppressing dehydroisomerization, an inevitable step in the conventional n‐BDH process which largely reduces the selectivity of BD. Detailed investigations show that the addition of alkali‐earth metals (e. g., Mg and Ca) into Pt‐Ga2O3/S10 catalysts increases Pt dispersity, suppresses coke deposition and dehydroisomerization, and thus leads to the significant increase of BD selectivity. The optimized catalyst displays an initial BD selectivity of 34.7 % at a n‐butane conversion of 82.1 % at 625 °C, which outperforms the reported catalysts in literatures. This work not only provides efficient catalysts for BD production via n‐BDH, but also promotes the researches on catalyst design in heterogeneous catalysis.
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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 | 0.001 |
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".