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Record W2924709012 · doi:10.1038/s42004-019-0141-4

Understanding zeolite deactivation by sulfur poisoning during direct olefin upgrading

2019· article· en· W2924709012 on OpenAlexafffund
Jonathan H. Harrhy, Aiguo Wang, Jack Jarvis, Peng He, Shijun Meng, Matthew M. Yung, Lijia Liu, Hua Song

Bibliographic record

VenueCommunications Chemistry · 2019
Typearticle
Languageen
FieldEngineering
TopicCatalysis and Hydrodesulfurization Studies
Canadian institutionsUniversity of Calgary
FundersAlberta Innovates
KeywordsOlefin fiberZeoliteSulfurCatalysisHydrodesulfurizationChemistryAdsorptionCatalyst poisoningOrganic chemistryInorganic chemistryCatalyst support

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.033
GPT teacher head0.221
Teacher spread0.188 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2019
Admission routes2
Has abstractyes

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