Can Metal Promotion of SAPO‐34 Genuinely Improve Its Catalytic Performance in Methanol Conversion to Light Olefins Reaction?
Bibliographic record
Abstract
Abstract To genuinely assess the effect of secondary metal promotion on improving the SAPO‐34 catalytic performance in MTO reaction, a broad spectrum of metals from different groups of the periodic table (alkali and alkaline earth metals, transition metals, rare earth metals, and basic metals) were investigated. Metals were added through a direct incorporation route with Me/Al2O3 molar ratio of 0.05. Some metals seamlessly incorporated into the SAPO‐34 framework and replaced the Si and Al atoms, while others partially merged or even failed to be combined with SAPO and emerged as amorphous phases. Although, in some cases, the surface area of the metal‐promoted samples increased due to enhanced nucleation rate and smaller particle formation, the majority of the promoted samples suffered from a severe loss in crystallinity that resulted in inferior catalytic performance. It was also illustrated that hydrogen co‐feeding with methanol (H2/MeOH molar ratio of 1.5) at ambient pressure could extend the catalyst lifetime by 27 % due to hydrogenation and cracking of the coke species and improve the light olefins selectivity.
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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".