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Record W4230574054 · doi:10.1002/ange.201605215

Visualizing Dealumination of a Single Zeolite Domain in a Real‐Life Catalytic Cracking Particle

2016· article· en· W4230574054 on OpenAlexafffund
Sam Kalirai, Pasi P. Paalanen, Jian Wang, Florian Meirer, Bert M. Weckhuysen

Bibliographic record

VenueAngewandte Chemie · 2016
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersNational Research Council CanadaEuropean Research CouncilNatural Sciences and Engineering Research Council of CanadaNederlandse Organisatie voor Wetenschappelijk Onderzoek
KeywordsZeoliteFluid catalytic crackingCatalysisCrackingParticle (ecology)Molecular sieveMaterials scienceChemical engineeringChemistryMineralogyInorganic chemistryPhysical chemistryOrganic chemistryGeology

Abstract

fetched live from OpenAlex

Abstract Fluid catalytic cracking (FCC) catalysts play a central role in the chemical conversion of crude oil fractions. Using scanning transmission X‐ray microscopy (STXM) we investigate the chemistry of one fresh and two industrially deactivated (ECAT) FCC catalysts at the single zeolite domain level. Spectro‐microscopic data at the Fe L 3 , La M 5 , and Al K X‐ray absorption edges reveal differing levels of deposited Fe on the ECAT catalysts corresponding with an overall loss in tetrahedral Al within the zeolite domains. Using La as a localization marker, we have developed a novel methodology to map the changing Al distribution of single zeolite domains within real‐life FCC catalysts. It was found that significant changes in the zeolite domain size distributions as well as the loss of Al from the zeolite framework occur. Furthermore, inter‐ and intraparticle heterogeneities in the dealumination process were observed, revealing the complex interplay between metal‐mediated pore accessibility loss and zeolite dealumination.

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 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 score0.006
Threshold uncertainty score0.324

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 head0.023
GPT teacher head0.255
Teacher spread0.232 · 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 teacher head, 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

Citations33
Published2016
Admission routes2
Has abstractyes

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