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Record W1983037400 · doi:10.1002/cjce.21635

Kinetic and catalytic performance of a BI‐porous composite material in catalytic cracking and isomerisation reactions

2012· article· en· W1983037400 on OpenAlexvenueaboutno aff
S. Al‐Khattaf, Taiwo Odedairo

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2012
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsnot available
FundersKing Fahd University of Petroleum and MineralsKing Abdullah University of Science and Technology
KeywordsCatalysisZeoliteMesoporous materialFluid catalytic crackingIsomerizationMicroporous materialMaterials scienceCrackingComposite numberChemical engineeringXyleneReactivity (psychology)PorosityTolueneComposite materialChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Catalytic behaviour of pure zeolite ZSM‐5 and a bi‐porous composite material (BCM) were investigated in transformation of m‐xylene, while zeolite HY and the bi‐porous composite were used in the cracking of 1,3,5‐triisopropylbenzene (TIPB). The micro/mesoporous material was used to understand the effect of the presence of mesopores on these reactions. Various characterisation techniques, that is, XRD, SEM, TGA, FT‐IR and nitrogen sorption measurements were applied for complete characterisation of the catalysts. Catalytic tests using CREC riser simulator showed that the micro/mesoporous composite catalyst exhibited higher catalytic activity as compared with the conventional microporous ZSM‐5 and HY zeolite for transformation of m‐xylene and for the catalytic cracking of TIPB, respectively. The outstanding catalytic reactivity of m‐xylene and TIPB molecules were mainly attributed to the easier access of active sites provided by the mesopores. Apparent activation energies for the disappearance of m‐xylene and TIPB over all catalysts were found to decrease in the order: E BCM > E ZSM‐5 and E BCM > E HY , respectively. © 2012 Canadian Society for Chemical Engineering

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 score0.294

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 head0.008
GPT teacher head0.183
Teacher spread0.175 · 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

Citations6
Published2012
Admission routes2
Has abstractyes

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