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Record W2109204526 · doi:10.1063/1.2227396

Oxygen adsorption on gold nanofacets and model clusters

2006· article· en· W2109204526 on OpenAlexafffund
Thierry Visart de Bocarmé, Thoi-Dai Chau, Frederik Tielens, Juán Andrés, Pierre Gaspard, R. L. C. Wang, H. J. Kreuzer, Norbert Kruse

Bibliographic record

VenueThe Journal of Chemical Physics · 2006
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsDalhousie University
FundersUniversitatea "Lucian Blaga" din SibiuDalhousie UniversityEuropean Commission
KeywordsDissociation (chemistry)OxygenDesorptionField desorptionAdsorptionElectric fieldChemistryMoleculeIonIonizationMass spectrometryDensity functional theoryBinding energyOxygen atomAtom (system on chip)Mass spectrumIonization energyAnalytical Chemistry (journal)Chemical physicsAtomic physicsPhysical chemistryComputational chemistryPhysics

Abstract

fetched live from OpenAlex

We have studied oxygen interaction with Au crystals (field emitter tips) using time-resolved (atom-probe) field desorption mass spectrometry. The results demonstrate no adsorption to take place on clean Au facets under chosen conditions of pressures (p < 10(-4) m/bar) and temperatures (T = 300-350 K). Steady electric fields of 6 V/nm do not allow dissociating the oxygen molecule. The measured O2+ intensities rather reflect ionization of O2 molecules at critical distances above the Au tip surface. Certain amounts of Au-O2 complex ions can be found at the onset of Au field evaporation. Calculations by density functional theory (DFT) show weak oxygen end-on interaction with Au10 clusters (Delta E = 0.023 eV) and comparatively stronger interaction with Au1/Au(100) model surfaces (Delta E = 0.25 eV). No binding is found on {210} facets. Including (positive) electric fields in the DFT calculations leads to an increase of the activation energy for oxygen dissociation thus providing an explanation for the absence of atomic oxygen ions from the field desorption mass spectra.

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 score0.048
Threshold uncertainty score0.261

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 head0.012
GPT teacher head0.237
Teacher spread0.225 · 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

Citations51
Published2006
Admission routes2
Has abstractyes

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