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Record W2965607392 · doi:10.1002/cjce.23607

Effect of hydrogen addition on formation of hydrogen and carbon from methane decomposition over Ni/Al<sub>2</sub>O<sub>3</sub>

2019· article· en· W2965607392 on OpenAlexvenueno aff
Jiaofei Wang, Lijun Jin, Guangsuo Yu, Haoquan Hu

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2019
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsnot available
FundersNational Natural Science Foundation of China
KeywordsHydrogenMethaneDecompositionCarbon fibersChemistryHydrogen productionInorganic chemistryCatalysisCryo-adsorptionChemical engineeringMaterials scienceHydrogen storageOrganic chemistryComposite number

Abstract

fetched live from OpenAlex

Abstract The effects of hydrogen addition on the formation of hydrogen and carbon from methane decomposition over Ni/Al 2 O 3 were studied. The results show that the added hydrogen in methane greatly affects the methane conversion, hydrogen output rate, and the properties of the carbon deposits on the surface of the Ni/Al 2 O 3 . The methane conversion and hydrogen output rate are significantly improved by the addition of hydrogen. As the flowrate of hydrogen increases from 0 to 25 mL/min, the initial activity of Ni/Al 2 O 3 decreases sharply, while the stability increases first and then decreases due to the suppression of hydrogen to CH 4 decomposition in the thermodynamics equilibrium. When the addition flowrate of the hydrogen is 15 mL/min, that is, 37.5% of the methane flowrate, a much higher methane conversion and the best stability of Ni/Al 2 O 3 are obtained. The addition of a specific amount of hydrogen benefits the methane decomposition; however, the excessive hydrogen will suppress the decomposition. Most of the carbon that deposits on the surface of Ni/Al 2 O 3 is filamentous carbon when hydrogen is added to the methane, however, encapsulated carbon is mainly produced when no hydrogen is added. In addition, the formation of encapsulated carbon, which deactivates the catalyst, is inhibited by the added hydrogen.

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)0.000
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 score0.033
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.192
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

Citations10
Published2019
Admission routes1
Has abstractyes

Explore more

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