MétaCan
Menu
Back to cohort
Record W2956852548 · doi:10.1021/acs.iecr.9b02156

One-Step Alkylation of Benzene with Syngas over Non-Noble Catalysts Mixed with Modified HZSM-5

2019· article· en· W2956852548 on OpenAlexaff
Fan Yang, Yuehua Fang, Xiangyu Liu, David Muir, Aimee Maclennan, Xuedong Zhu

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2019
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsCanadian Light Source (Canada)
FundersNankai UniversityNational Natural Science Foundation of China
KeywordsSyngasXyleneBenzeneTolueneCatalysisAlkylationZeoliteChemistryp-XyleneSelectivityInorganic chemistryZincOrganic chemistry

Abstract

fetched live from OpenAlex

para-Xylene is an important chemical for industry. In this work, alkylation of benzene with syngas (ABS) is carried out to prepare toluene and xylene over zinc/chromium (Zn/Cr) oxide and modified HZSM-5. The catalyst with a Zn/Cr ratio of 1.6 and 20% (wt) silicate-1 zeolite (S1) coated HZSM-5 has the best catalytic performance, exhibiting 34.4% benzene conversion and 94.7% total selectivity of toluene and xylene, with 73.2% of xylene as para-xylene. The catalyst is carefully characterized; results showed that the activity was enhanced by excessive zinc atoms, which would replace the surface chromium atoms, and this substitution led to more oxygen vacancies. Besides, by coating a layer of S1 on HZSM-5, the production of trimethylbenzene was suppressed and the selectivity of para-xylene among all xylene was increased, due to the coverage of zeolite surface acid sites. These findings are helpful for understanding the one-pot syngas conversion and benzene alkylation 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 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)0.002
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 score0.017
Threshold uncertainty score0.930

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.046
GPT teacher head0.289
Teacher spread0.243 · 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
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueIndustrial & Engineering Chemistry ResearchSame topicCatalytic Processes in Materials ScienceFrench-language works237,207