Understanding zeolite deactivation by sulfur poisoning during direct olefin upgrading
Bibliographic record
Abstract
Abstract The presence of sulfur contaminants in bitumen derived crude oils can lead to rapid catalyst deactivation and is a major problem faced by downstream refiners. Whilst expensive hydrotreating steps may remove much of the sulfur content, it is important to understand how catalyst deactivation by sulfur poisoning occurs and how it may be mitigated. Here we report a mechanistic study of sulfur poisoning over a zeolite catalyst promoted with silver and gallium Lewis acids. Olefin upgrading, an essential process in the refinement of heavy oils, is used as a model reaction. Access to the zeolite inner pores is blocked by bulky, weakly adsorbed sulfur species. Pore access and thus catalyst activity is restored by increasing the reaction temperature. We also show that a simple alkaline treatment greatly improves both the sulfur tolerance and performance of the catalyst. These findings may enhance the rational design of heterogenous catalysts for olefin upgrading.
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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".