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Record W3134701711 · doi:10.1038/s42004-021-00472-8

Highly selective skeletal isomerization of cyclohexene over zeolite-based catalysts for high-purity methylcyclopentene production

2021· article· en· W3134701711 on OpenAlexafffund
Hao Xu, Zhaofei Li, Shijun Meng, Jack Jarvis, Hua Song

Bibliographic record

VenueCommunications Chemistry · 2021
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsUniversity of Calgary
FundersAlberta InnovatesNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsIsomerizationCatalysisZeoliteCyclohexeneSelectivityYield (engineering)ChemistryCobaltPhotochemistryMaterials scienceInorganic chemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

Cyclohexene skeletal isomerization towards methylcyclopentene is an economically favorable process due to the higher added value of the product. Traditional oxide-based catalysts face the challenge of achieving both high activity and stability. In this work, cyclohexene skeletal isomerization is achieved under mild conditions over designed zeolite-based catalysts with 96.8 wt.% liquid yield, 95.8 wt.% selectivity towards methylcyclopentene and satisfactory stability for multiple runs. The favorable performance is attributed to the unique acidic, structural and morphological features of the optimized cobalt/NaUZSM-5 catalyst. Further experimental data and DFT studies suggest that a carboncationic mechanism might be followed and that the reaction mainly occurs within the internal pores of the zeolite structures.

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 score0.004

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 head0.017
GPT teacher head0.265
Teacher spread0.249 · 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

Citations10
Published2021
Admission routes2
Has abstractyes

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