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Record W2790968643 · doi:10.1002/cjce.23201

Development and characterization of novel combinations of Ce‐Ni‐MFI solids for water gas shift reaction

2018· article· en· W2790968643 on OpenAlexafffundvenue
Sarah Alamolhoda, Gerardo Vitale, Azfar Hassan, Nashaat N. Nassar, Pedro Pereira Almao

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsCatalysisCeriumPhysisorptionWater-gas shift reactionNickelMethaneCerium oxideInorganic chemistryMaterials scienceCharacterization (materials science)Chemical engineeringChemistryMetallurgyNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract In this work, novel catalysts with different low percentages of nickel and cerium (0–3 % g/g) inside a crystalline silica framework were prepared to be tested in catalytic water gas shift reaction (WGSR) at low temperatures. The developed preparation method is distinctive as it does not require the impregnation of metals, drying, and further calcinations steps, and produces the anticipated catalysts in an efficient way. The produced solids were characterized using various characterization methods including XRD, TPR‐TPO, TPD, SEM/EDX, and N 2 physisorption. Results indicated that these solids benefit from good reproducibility and ease of mass production. WGSR experiments over the produced solids at 503 K showed that Ce‐MFI solids were inactive at this temperature; however, Ni‐MFI and Ce‐Ni‐MFI solids catalyzed the reaction. At this low temperature, the catalysts containing cerium presented higher activity compared to catalysts with only nickel content. Furthermore, no methane production was observed using these catalysts.

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 score0.003
Threshold uncertainty score0.182

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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

Citations13
Published2018
Admission routes3
Has abstractyes

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