SAPO-34 Catalyst: Synthesis Optimization by Template Alteration and In Situ Coke Evolution Analysis in Methanol Conversion to Light Olefins
Bibliographic record
Abstract
The template’s role in the physicochemical properties of SAPO-34 was investigated using the four most popular templates (tetraethylammonium hydroxide (TEAOH), morpholine (MOR), triethylamine (TEA), and diethylamine (DEA)) in both single- and mixed-template methods to reduce the consumption of costly TEAOH. Catalyst activity in methanol conversion to light olefins was examined at 425 °C and WHSV of 1 g MeOH g Cat –1 h –1 . The TEA-based sample proved to be a good substitute for TEAOH in single template SAPO, and both TEAOH/DEA and TEAOH/TEA-based samples were successfully produced with the sole existence of the SAPO-34 phase and exhibited acceptable performance. Possessing a high external surface area (macro and mesopores) was recognized to be the dominant parameter affecting the catalyst’s lifetime while having the optimum acidity and the small particle size played minor roles. In-situ measurement of coke formation over SAPO-34 was also conducted in a thermogravimetric reactor and led to the proposal of the coke formation mechanism, which was confirmed by 13 C NMR spectroscopy and gas chromatography–mass spectrometry techniques.
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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".