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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.117
Threshold uncertainty score0.713

Codex and Gemma teacher scores by category

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)0.0000.000

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