Suppressing Dehydroisomerization Boosts <i>n</i>‐Butane Dehydrogenation with High Butadiene Selectivity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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‐Ga 2 O 3 /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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 it