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Record W2291385635 · doi:10.1002/cjce.22472

Preparation of nanocrystalline Ni/Al<sub>2</sub>O<sub>3</sub> catalysts with the microemulsion method for dry reforming of methane

2016· article· en· W2291385635 on OpenAlexvenueno aff
Mohammad Hossein Aboonasr Shiraz, Mehran Rezaei, Fereshteh Meshkani

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2016
Typearticle
Languageen
FieldChemical Engineering
TopicCatalysts for Methane Reforming
Canadian institutionsnot available
FundersUniversity of KashanIran National Science Foundation
KeywordsNanocrystalline materialCatalysisCarbon dioxide reformingSyngasMaterials scienceMesoporous materialMicroemulsionMethaneSpecific surface areaSpace velocityScanning electron microscopeCrystalliteTemperature-programmed reductionChemical engineeringNickelBET theoryInorganic chemistryNuclear chemistryNanotechnologyChemistryMetallurgySelectivityOrganic chemistryPulmonary surfactantComposite material

Abstract

fetched live from OpenAlex

Abstract Mesoporous nanocrystalline nickel‐alumina catalysts with high surface area were prepared by a microemulsion (ME) method and were employed in methane reforming with carbon dioxide for syngas production. The catalysts were characterized by X‐ray diffraction (XRD), Brunauer‐Emmett‐Teller surface area analysis (BET), temperature‐programmed reduction (TPR), temperature‐programmed oxidation (TPO), and scanning electron microscopy (SEM) techniques. The results showed that the catalysts possessed mesoporous structure with high surface area (&gt; 250 m 2 · g −1 ) and small crystallite size (∼5 nm). The catalytic results revealed high activity and stability for the prepared catalysts. In addition, the effect of feed ratio and GHSV on catalytic performance was investigated.

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)0.001
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 score0.039
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
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 head0.007
GPT teacher head0.232
Teacher spread0.224 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

Explore more

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