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Record W3210739762 · doi:10.1038/s42004-021-00590-3

The interactive role of methane beyond a reactant in crude oil upgrading

2021· article· en· W3210739762 on OpenAlexafffund
Hao Xu, Zhaofei Li, Yimeng Li, Hua Song

Bibliographic record

VenueCommunications Chemistry · 2021
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsUniversity of Calgary
FundersWestern Economic Diversification CanadaBritish Columbia Knowledge Development FundNatural Sciences and Engineering Research Council of CanadaSimon Fraser UniversityAlberta InnovatesCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsMethaneCrude oilEnvironmental sciencePetroleum engineeringWaste managementChemistryGeologyEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Crude oil upgrading under methane has been reported to be an economically and environmentally promising process, while the advantageous effect of methane beyond a reactant is not fully explained. In this work, the catalytic performances, physicochemical properties and regenerability of used catalysts after crude oil upgrading under methane and nitrogen are investigated by n-butylbenzene model compound studies, catalyst characterizations and density functional theory calculations. Comparing to nitrogen, methane exhibits a protective effect on the charged catalyst despite the limited conversion, leading to better product quality and catalyst stability. This protective effect is attributed to the interaction between methane and catalytic active sites, which mainly occurs in the internal pores of the zeolitic catalyst support, resulting in unique coke distribution and inhibition of metal deposition. The interactive role of methane beyond a reactant, which is previously underestimated, is suggested to be critical for better performances of catalysts in relevant reaction processes.

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 score0.002
Threshold uncertainty score0.004

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 head0.015
GPT teacher head0.304
Teacher spread0.289 · 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

Citations13
Published2021
Admission routes2
Has abstractyes

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