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Record W2015244315 · doi:10.1021/ie020486n

Methane Nonoxidative Aromatization over Ru−Mo/HZSM-5 at Temperatures up to 973 K in a Palladium−Silver/Stainless Steel Membrane Reactor

2002· article· en· W2015244315 on OpenAlexaff
Maria C. Iliuta, Bernard P. A. Grandjean, Faı̈çal Larachi

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2002
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMethaneSpace velocityCatalysisMembrane reactorBenzeneMembranePalladiumAromatizationIncipient wetness impregnationChemistryInorganic chemistryAnaerobic oxidation of methaneChemical engineeringOxygenMaterials scienceNuclear chemistryOrganic chemistrySelectivity

Abstract

fetched live from OpenAlex

Pd-based composite membranes supported on porous stainless steel offer high permeability and good mechanical stability. They represent an appropriate alternative in catalytic membrane reactors. In this study, Pd−Ag/porous stainless steel membranes prepared by electroless plating were used in the oxygen-free methane aromatization over Ru−Mo/HZSM-5 in a catalytic membrane reactor at temperatures up to 973 K. The 0.5% Ru−3% Mo/HZSM-5 catalyst, prepared by incipient-wetness co-impregnation, was highly selective toward benzene production. Despite attained methane conversions well beyond the thermodynamic conversion, the continuous withdrawal of coproduced H 2 promoted the formation and deposition of low-H/C carbonaceous species especially at high temperatures. At a methane space velocity of 270 mL(STP)·h -1 ·g -1 and a temperature of 873 K, a maximum conversion of methane to benzene equal to ca. 8% was obtained. When the temperature was raised to 973 K, the maximum conversion culminated at ca. 17% but was accompanied with time by a faster falloff in methane conversion compared to that observed at 873 K.

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)0.006
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 score0.003
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.070
GPT teacher head0.322
Teacher spread0.251 · 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

Citations50
Published2002
Admission routes1
Has abstractyes

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