Bifunctional catalyst of mordenite‐ and alumina‐supported platinum for isobutane hydroisomerization to <i>n</i> ‐butane
Bibliographic record
Abstract
Abstract n ‐Butane has higher value‐added applications than isobutane in the petrochemical process. To improve the catalytic performance of isobutane isomerization catalysts, catalysts of mordenite‐ and alumina‐supported platinum were prepared. The intimacy between metal and acid sites and platinum content was investigated. The effect of reaction conditions on the catalytic performance of the catalyst was analyzed by response surface methodology. The characterization results of X‐ray diffraction (XRD) patterns, specific surface area, and transmission electron microscope (TEM) confirmed high dispersion of platinum on the catalysts. The results of temperature‐programmed desorption of ammonia (NH 3 ‐TPD) indicated that more acid sites existed on millimetre scale and centimetre scale catalysts than on Pt‐HM/Al 2 O 3 and Pt‐Al 2 O 3 /HM, which led to low selectivity of n ‐butane. The best yield of n ‐butane was attained on microscale samples, as the larger diffusion distance inhibited the cracking reaction. The dispersion of reaction products indicated that the increase of platinum content slightly increased the selectivity of n ‐butane. Compared with Pt‐HM/Al 2 O 3 , Pt‐Al 2 O 3 /HM showed high stability due to fewer carbon deposits in the catalytic reaction process. The optimal reaction condition in isobutane isomerization was T = 413.4°C, liquid hourly space velocity (LHSV) = 5.31 h −1 , and P = 2.57 MPa.
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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 | 0.000 |
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".