MétaCan
Menu
Back to cohort
Record W2008518018 · doi:10.5539/mas.v2n3p42

First Row Transition Metal Oxide Based Catalysts for the In-situ Reactions of Methanation and Desulfurization in the Removal of Sour Gases from Simulated Natural Gas

2008· article· en· W2008518018 on OpenAlexvenueno aff
Wan Azelee Wan Abu Bakar, Mohd. Yusuf Othman, Ching Kuan Yong, Junaidi Mohd. Nasir

Bibliographic record

VenueModern Applied Science · 2008
Typearticle
Languageen
FieldEngineering
TopicIndustrial Gas Emission Control
Canadian institutionsnot available
Fundersnot available
KeywordsMethanationCatalysisSubstitute natural gasFlue-gas desulfurizationX-ray photoelectron spectroscopyTransition metalInorganic chemistryAdsorptionSulfurOxideNon-blocking I/OWater-gas shift reactionChemistryMaterials scienceChemical engineeringSyngasPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

The objective of this novel catalyst development is to achieve both low temperature and high conversion of sour gases of H2S and CO2 present in the natural gas. The results showed that the conversion of H2S to elemental sulfur on all of the potential catalysts was achieved 100 %. However, methanation of CO2 in the presence of H2S yielded 0.7 % CH4 over Fe/ Zn/ Cu/ Ti-Al2O3 catalyst, 1.1 % CH4 over Fe/ Zn/ Cu-Al2O3 catalyst and the highest is 6.1 % CH4 over Pr/ Co/ Ni-Al2O3 catalyst at maximum studied temperature of 300 oC. The catalysts were further characterized by X-rays Photoelectron Spectroscopy and Nitrogen Adsorption analysis. XPS results revealed Ni2+ ion in the NiO and Ni3+ in Ni2O3 species, spinel compound of Co3O4 on the Pr/ Co/ Ni-Al2O3 catalyst. N2 adsorption-desorption analysis illustrated 7.9 % increment of surface area over the spent Pr/ Co/ Ni-Al2O3 catalyst, which assumed to be responsible for the dramatical increased of the methanation activity of this catalyst at the reaction temperature of 300 oC.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.419
Threshold uncertainty score0.220

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.023
GPT teacher head0.229
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations1
Published2008
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicIndustrial Gas Emission ControlFrench-language works237,207