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Record W2991120291 · doi:10.1038/s41598-019-54089-y

Hierarchical H-ZSM5 zeolites based on natural kaolinite as a high-performance catalyst for methanol to aromatic hydrocarbons conversion

2019· article· en· W2991120291 on OpenAlexfundno aff
Ahmad Asghari, Sayed Habib Kazemi

Bibliographic record

VenueScientific Reports · 2019
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsnot available
FundersMcGill University
KeywordsMethanolCatalysisChemical engineeringMesoporous materialCrystallinityZeoliteMaterials scienceAromatic hydrocarbonHydrocarbonSelectivityChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The present work introduces a good prospect for the development of hierarchical catalysts with excellent catalytic performance in the methanol to aromatic hydrocarbons conversion (MTA) process. Hierarchical H-ZSM5 zeolites, with a tailored pore size and different Si/Al ratios, were synthesized directly using natural kaolin clay as a low-cost silica and aluminium resource. Further explored for the direct synthesis of hierarchical HZSM-5 structures was the steam assisted conversion (SAC) with a cost-effective and green affordable saccharide source of high fructose corn syrup (HFCS), as a secondary mesopore agent. The fabricated zeolites exhibiting good crystallinity, 2D and 3D nanostructures, high specific surface area, tailored pore size, and tunable acidity. Finally, the catalyst performance in the conversion of methanol to aromatic hydrocarbons was tested in a fixed bed reactor. The synthesized H-ZSM5 catalysts exhibited superior methanol conversion (over 100 h up to 90%) and selectivity (over 85%) in the methanol conversion to aromatic hydrocarbon products.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.047
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0000.000
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 head0.231
Teacher spread0.223 · 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

Citations43
Published2019
Admission routes1
Has abstractyes

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