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Record W2007701927 · doi:10.1002/cjce.5450830606

Catalytic Activity of Copper Oxide Impregnated HZSM‐5 in Methanol Conversion to Liquid Hydrocarbons

2005· article· en· W2007701927 on OpenAlexvenueno aff
Hasan Akhtar Zaidi, Kamal Kishore Pant

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2005
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsnot available
Fundersnot available
KeywordsBenzeneCatalysisMethanolTolueneSpace velocityHydrocarbonProduct distributionCopperInorganic chemistryXyleneChemistryCokeOxideYield (engineering)Copper oxideZeoliteNuclear chemistryMaterials scienceOrganic chemistrySelectivityMetallurgy

Abstract

fetched live from OpenAlex

Abstract A number of CuO/HZSM‐5 catalysts have been studied in a small scale fixed bed reactor for the conversion of methanol to gasoline range hydrocarbons at 673 K and at one atmospheric pressure. All the catalysts were prepared by wet impregnation technique. The copper oxide loading over HZSM‐5 (Si/Al=45) catalyst was studied in the range of 0 to 9 wt%. XRD, surface area analyzer, metal trace analyzer, SEM techniques and TGA were used to characterize the catalysts. Incorporation of CuO onto HZSM‐5 zeolite significantly increased conversion and liquid hydrocarbon product yields. The major liquid products of the reactions were ethyl benzene, toluene, xylene, isopropyl benzene, ethyl toluene, trimethyl benzene and tetramethyl benzene. The maximum methanol conversion and hydrocarbon product yield was obtained at a copper oxide loading of 7 wt%. Effect of run time on conversion and product distribution was also investigated to compare the performance of these catalysts and coke on the catalyst was determined. Effect of space‐time and temperature on methanol conversion and products yield with 7 wt% CuO/HZSM‐5 has also been investigated and analyzed qualitatively.

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 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 score0.002
Threshold uncertainty score0.437

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 studies0.0000.000
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 head0.196
Teacher spread0.188 · 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

Citations23
Published2005
Admission routes1
Has abstractyes

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