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Record W2904042080 · doi:10.1002/cctc.201801545

Influence of Carbon and Oxygen Chemical Potentials on the Hydrogen Donor Identity During Methanation on Ni, Co, and Ni‐Co Clusters

2018· article· en· W2904042080 on OpenAlexafffund
Petar T. Lachkov, Ya-Huei Cathy Chin

Bibliographic record

VenueChemCatChem · 2018
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMethanationOxygenHydrogenCarbon fibersChemistryTransition metalInorganic chemistryCobaltCatalysisCarbon monoxidePhotochemistryMaterials scienceOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Rate measurements in the kinetically controlled regime and equilibrium carbon and oxygen chemical titrations show two distinct mechanistic paths during CO x methanation reactions on first‐row transition metal clusters. On Ni and, for a limiting set of conditions, Ni−Co clusters, the reaction occurs via the addition of a hydrogen adatom into CH 3 * intermediates on clusters partially covered with carbon. On Co and, in a subset of conditions, Ni−Co clusters, it occurs via the donation of hydrogen from OH* to CH 3 * on clusters partially covered with reactive oxygen adatoms (O*). The [CO] 2 ‐to‐[CO 2 ] and [CO 2 ]‐to‐[CO] operating ratios are the surrogates of carbon and oxygen chemical potentials, respectively, as a consequence of water‐gas shift equilibration. These ratios, together with the carbon and oxygen binding energies, determine the relative surface coverages of carbon and oxygen, the involvement of H* vs. OH* in the kinetically‐relevant step, and in turn, the rate dependencies. Stronger carbon and oxygen binding energies lead to more stabilized transition states of the kinetically relevant steps and larger methanation rates.

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 score0.004
Threshold uncertainty score0.607

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.013
GPT teacher head0.268
Teacher spread0.255 · 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

Citations4
Published2018
Admission routes2
Has abstractyes

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