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Record W2025098211 · doi:10.1021/jp805106d

Catalytic Oxidation of H<sub>2</sub> by N<sub>2</sub>O in the Gas Phase: O-Atom Transport with Atomic Metal Cations

2008· article· en· W2025098211 on OpenAlexaff
Voislav Blagojevic, Galina Orlova, Diethard K. Böhme

Bibliographic record

VenueThe Journal of Physical Chemistry A · 2008
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsSt. Francis Xavier UniversityYork University
Fundersnot available
KeywordsCatalysisChemistryInorganic chemistryMetalOxideHydrogenAlkali metalAtom (system on chip)Physical chemistryAnalytical Chemistry (journal)

Abstract

fetched live from OpenAlex

Twenty-five atomic cations, M (+), that lie within the thermodynamic window for O-atom transport catalysis of the oxidation of hydrogen by nitrous oxide, have been checked for catalytic activity at room temperature with kinetic measurements using an inductively-coupled plasma/selected-ion flow tube (ICP/SIFT) tandem mass spectrometer. Only 4 of these 25 atomic cations were seen to be catalytic: Fe (+), Os (+), Ir (+), and Pt (+). Two of these, Ir (+) and Pt (+), are efficient catalysts, while Fe (+) and Os (+) are not. Eighteen atomic cations (Cr (+), Mn (+), Co (+), Ni (+), Cu (+), Ge (+), Se (+), Mo (+), Ru (+), Rh (+), Sn (+), Te (+), Re (+), Pb (+), Bi (+), Eu (+), Tm (+), and Yb (+)) react too slowly at room temperature either in their oxidation with N 2O to form MO (+) or in the reduction of MO (+) by H 2. Many of these reactions are known to be spin forbidden and a few actually may lie outside the thermodynamic window. Three alkaline-earth metal monoxide cations, CaO (+), SrO (+), and BaO (+), were observed to favor MOH (+) formation in their reactions with H 2. A potential-energy landscape is computed for the oxidation of H 2 with N 2O catalyzed by Fe (+)( (6)D) that vividly illustrates the operation of an ionic catalyst and qualitatively accounts for the relative inefficiency of this catalyst.

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 score0.002
Threshold uncertainty score0.510

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 studies0.0000.001
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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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

Citations23
Published2008
Admission routes1
Has abstractyes

Explore more

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