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Record W2441030547 · doi:10.1021/acsami.5b02367

Mesoporous Mn- and La-Doped Cerium Oxide/Cobalt Oxide Mixed Metal Catalysts for Methane Oxidation

2015· article· en· W2441030547 on OpenAlexafffund
S.M. Vickers, Rahman Gholami, Kevin J. Smith, Mark J. MacLachlan

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCobalt oxideMaterials scienceCobaltCerium oxideInorganic chemistryMesoporous materialOxideCatalysisCeriumMixed oxideChemical engineeringChemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

New precious-metal-free mesoporous materials were investigated as catalysts for the complete oxidation of methane to carbon dioxide. Mesoporous cobalt oxide was first synthesized using KIT-6 mesoporous silica as a hard template. After removal of the silica, the cobalt oxide was itself used as a hard template to construct cerium oxide/cobalt oxide composite materials. Furthermore, cerium oxide/cobalt oxide composite materials doped with manganese and lanthanum were also prepared. All of the new composite materials retained the hierarchical long-range order of the original KIT-6 template. Temperature-programmed oxidation measurements showed that these cerium oxide/cobalt oxide and doped cerium oxide/cobalt oxide materials are effective catalysts for the total oxidation of methane, with a light-off temperature (T50%) of ∼400 °C observed for all of the nanostructured materials.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0010.000

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 head0.025
GPT teacher head0.276
Teacher spread0.251 · 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 source (direct Gemma or distilled Codex), 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

Citations70
Published2015
Admission routes2
Has abstractyes

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