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Record W4210661359 · doi:10.1002/slct.202103149

Mesostructured Zn/ZSM‐5 Zeolite as Catalyst for Furan Deoxygenation,

2022· article· en· W4210661359 on OpenAlexaff
Rouholamin Biriaei, Sara Madadi, Serge Kaliaguine

Bibliographic record

VenueChemistrySelect · 2022
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMicroporous materialCatalysisMesoporous materialZeoliteZincDeoxygenationMaterials scienceFuranInorganic chemistryX-ray photoelectron spectroscopyChemistryNuclear chemistryChemical engineeringOrganic chemistryMetallurgyComposite material

Abstract

fetched live from OpenAlex

Abstract The furan conversion into aromatics over zinc promoted ZSM‐5 zeolites was studied in a continuous‐flow fixed‐bed reactor. Two series of microporous and mesoporous catalysts were prepared at different zinc loadings of 2 and 5 wt.%. It was intended to optimize the overall aromatics production and control coke accumulation over novel synthesized mesoporous ZSM‐5 catalyst, in a continuous flow reactor. The catalysts were characterized using X‐ray diffraction (XRD), nitrogen adsorption/desorption, ammonia temperature programmed desorption (TPD) and X‐ray photoelectron spectroscopy (XPS). The zinc loaded mesoporous materials exhibit an XRD pattern that matches with the ZSM‐5 XRD reference pattern. No extra peaks were observed in the XRD results indicating the high dispersion of zinc species. The initial furan conversion rate was higher over the microporous catalysts, and increased upon increasing metal loading, however contrary to the mesoporous samples, the microporous ones deactivated rapidly over 2 hours of time on‐stream. The results indicated that around 40 % of carbon in the fed furan, was recovered in aromatics when the new mesostructured zeolite catalyst was used, whereas this could be further increased to 50 % by the addition of zinc in the catalyst, benzene being the major product.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.169
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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 head0.009
GPT teacher head0.229
Teacher spread0.221 · 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.

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

Citations5
Published2022
Admission routes1
Has abstractyes

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