MétaCan
Menu
Back to cohort
Record W2114465859 · doi:10.1139/cjc-2013-0528

Density functional theory studies of methanol adsorption and decomposition mechanism on Al<sub>13</sub> clusters

2014· article· en· W2114465859 on OpenAlexvenueno aff
Caichao Ye, Fengqi Zhao, Siyu Xu, Xue‐Hai Ju

Bibliographic record

VenueCanadian Journal of Chemistry · 2014
Typearticle
Languageen
FieldPhysics and Astronomy
TopicAdvanced Chemical Physics Studies
Canadian institutionsnot available
FundersGovernment of Jiangsu Province
KeywordsChemistryAdsorptionMoleculeDensity functional theoryBond-dissociation energyDissociation (chemistry)MethanolDecompositionBond energyPhysical chemistryBond cleavageComputational chemistryActivation energyReaction rate constantCrystallographyKineticsCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

The adsorption and decomposition of the CH3OH molecule on Al13 clusters were investigated by generalized gradient approximation of the density functional theory. The strong attractive forces between the CH3OH molecule and aluminum atoms induce the breaking of the H–O and C–O bonds of CH3OH. Subsequently, the dissociated CH3O and OH radical fragments oxidize the aluminum clusters. The largest adsorption energy is –205.4 kJ/mol. We also investigated five reaction pathways of the CH3OH molecule on the Al13 clusters. The activation energies are in the range of 10.3−113.1 kJ/mol. Compared with the bond dissociation energies of the C–O and O–H bonds in the isolated methanol, Al13 performs very well in decreasing the bond break barrier of CH3OH. In addition, although the C–O bond is slightly weaker than the O−H bond, the O−H bond is even easier to decompose on the Al13 surface. The rate constants of five adsorption paths over the temperature range 300−700 K are presented.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.029

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.232
Teacher spread0.223 · 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 designSimulation or modeling
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

Citations3
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of ChemistrySame topicAdvanced Chemical Physics StudiesFrench-language works237,207