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Record W4396605409 · doi:10.1021/acs.iecr.3c04276

Preparation and Evaluation of Resistance Coke Formation Catalysts in Combined Reforming of Methane Using Steam and Oxygen for the Production of Synthesis Gas

2024· article· en· W4396605409 on OpenAlexaff
Somaye Sadat Miri, Fereshteh Meshkani, Mehran Rezaei, Ali Rastegarpanah

Bibliographic record

VenueIndustrial & Engineering Chemistry Research · 2024
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsUniversity of Calgary
FundersUniversity of Kashan
KeywordsCokeSteam reformingMethaneCatalysisSyngasOxygenMethane reformerChemistryAmmonia productionChemical engineeringWaste managementHydrogen productionEnvironmental scienceOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Ni-based catalysts with different promoters, such as Fe, La, Zr, Ce, and Ca, were prepared using the one-step sol–gel method and tested for their performance in the combined reforming of methane using steam and oxygen. The catalysts were characterized by XRD, SEM, TPO, BET, and TPR analysis. The consequences revealed that deposited carbon was lower on the surface of the 10% Ni-3% Fe-MgO·Al 2 O 3 catalyst in oxygen-combined CH 4 reforming. The Fe 2 O 3 -doped sample depicted the highest CH 4 conversion, more than 65%, and showed a higher catalytic stability for O 2 reforming of methane. In addition, introducing steam into the feeds benefited the performance of catalysts due to lower coke formation, and the H 2 /CO ratio was changed from 1.5 to 3 at 700 °C. It is concluded that the high catalytic efficiency of the Fe 2 O 3 -doped sample in combined reforming of methane was associated with its high reducibility and more reduction of NiO species.

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)0.005
metaresearch head score (Gemma)0.005
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 score0.208
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.005
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 head0.121
GPT teacher head0.382
Teacher spread0.261 · 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

Citations3
Published2024
Admission routes1
Has abstractyes

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