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Record W4414298754 · doi:10.1002/cjce.70073

Alkaline–acid modification of <scp>HZSM</scp> ‐5 zeolites: Catalytic conversion of methanol in synergy with raffinate oil to light olefins

2025· article· en· W4414298754 on OpenAlexvenueno aff
Fenglan Wang, Xu Yarong

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2025
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsnot available
Fundersnot available
KeywordsRaffinateMethanolCatalysisSelectivityCrystallinityAdsorptionYield (engineering)DesorptionSpecific surface area

Abstract

fetched live from OpenAlex

Abstract HZSM‐5 zeolites were treated by alkaline and alkaline–acid composite modification via the hydrothermal method. A comprehensive series of characterization techniques, including X‐ray diffraction (XRD), scanning electron microscopy (SEM), N 2 adsorption–desorption, temperature‐programmed desorption of ammonia (NH 3 ‐TPD), and pyridine adsorption infrared spectroscopy (Py‐IR), were employed to investigate the physicochemical properties of the ZSM‐5 zeolites before and after modification. The results demonstrated that the relative crystallinity of the modified zeolites remained nearly intact, with the MFI structure being well‐preserved. Nevertheless, the total acid content of both modified samples decreased, accompanied by an increase in specific surface area and pore size. Notably, the zeolites treated with alkaline–acid composite modification showed a slight elevation in the total L‐acid content. When applied to the reaction of coupling methanol with raffinate oil for the production of light olefins, compared with the unmodified ZSM‐5 zeolite, the modified ZSM‐5 zeolites showed a significant increase in the selectivity and yield of triene. In particular, the ZSM‐5 zeolite A2 modified by the alkali–acid composite method exhibited remarkable enhancements in catalytic activity and stability. Its methanol conversion rate exceeded 99%, and when the selectivity for triene reached 60.26%, the yield reached a high value of 36.8%.

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)0.001
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 score0.010
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.006
GPT teacher head0.190
Teacher spread0.183 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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