Selective oligomerization of isobutylene in mixed <scp>C<sub>4</sub></scp> catalyzed by supported <scp>Fe(NO<sub>3</sub>)<sub>3</sub>/β</scp> catalyst
Bibliographic record
Abstract
Abstract BACKGROUND Methyl tert‐butyl ether (referred to as MTBE) is harmful to the environment and hence its use is limited in China. To avoid the waste of isobutylene as MTBE raw material and improve the utilization rate of isobutylene in mixed C4 fraction, supported Fe(NO3)3/β molecular sieve catalysts with different active components were prepared by the equal volume impregnation method. The catalysts were analyzed by X‐ray diffraction (XRD), Thermogravimetric analysis (TG), Temperature programmed desorption of NH3, Brunauer–Emmett–Teller analysis and scanning electron microscopy, and the catalytic performance of catalysts with mixed C4 fractions as raw materials and different active component loadings for selective oligomerization of isobutylene was investigated in a fixed bed reactor. RESULTS The results show that the catalyst has the best catalytic performance when the active component loading was 6%, the reaction temperature was 60 °C, the reaction pressure was 1 MPa and the reaction space velocity was 1.5 h−1. CONCLUSION The conversion rate of isobutylene was >90%, the selectivity of C8 olefin was ≈80% and there was almost no loss of n‐butene. © 2021 Society of Chemical Industry (SCI).
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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".