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Record W2006922543 · doi:10.1039/c3cp54376e

Carbon clusters on the Ni(111) surface: a density functional theory study

2013· article· en· W2006922543 on OpenAlexafffund
Jingde Li, Eric Croiset, Luis Ricardez‐Sandoval

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2013
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship Council
KeywordsCarbon fibersDensity functional theoryNucleationChemical physicsCluster (spacecraft)AdsorptionChemistryCluster chemistryBinding energyTransition metalComputational chemistryMetalMaterials sciencePhysical chemistryOrganic chemistryAtomic physicsCatalysis

Abstract

fetched live from OpenAlex

To understand the nucleation of carbon atoms to form carbon clusters on transition metal substrates during chemical vapor deposition (CVD) synthesis, the structure, energetics, and mobility of carbon intermediates up to 6 atoms on the Ni(111) surface were investigated using Density Functional Theory (DFT). Carbon clusters were found to be more thermodynamically stable than adsorbed atomic carbon, with linear carbon structures being more stable than branched and ring structures. Carbon chains were also found to have higher mobility than branched configurations. The interaction energy between carbon clusters and the Ni surface shows that branched carbon clusters have stronger interaction with the Ni substrate when compared with the carbon chains, supporting that carbon chains generally have higher mobility than branched clusters. The transition states and energy barriers for the formation of different carbon clusters were also studied. The results show that the formation of the branched configurations is kinetically favored as it presents lower energy barriers than those obtained for carbon chains.

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)0.000
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 score0.069
Threshold uncertainty score0.675

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.020
GPT teacher head0.252
Teacher spread0.231 · 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

Citations38
Published2013
Admission routes2
Has abstractyes

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