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Role of adatom defects in the adsorption of polyaromatic hydrocarbons on metallic substrates

2023· preprint· en· W4377241920 on OpenAlexafffund
Dushanthi S. Dissanayake, Yarra Hassan, Emilian Tuca, Irina Paci

Bibliographic record

VenueChemRxiv · 2023
Typepreprint
Languageen
FieldChemistry
TopicSynthesis and Properties of Aromatic Compounds
Canadian institutionsUniversity of British ColumbiaUniversity of Victoria
FundersWestern Canada Research GridCompute Canada
KeywordsCoroneneAdsorptionSubstrate (aquarium)MoleculeChemical physicsDensity functional theoryMonomerFlatteningMetalChemistryReactivity (psychology)HeliceneMaterials scienceComputational chemistryPhotochemistryPhysical chemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

The adsorption of polyaromatic hydrocarbons (PAHs) on metallic substrates is of interest in the field of optoelectronics, due to the possibility of designing complex materials with tunable properties through surface functionalization with organic molecules. Much of the modelling research in this field has focused on perfectly symmetrical (pristine) substrates. There is limited information on the effect of substrate irregularities, such as adatoms, on the binding of PAHs onto substrates. Here, we examine how the presence of substrate-bound adatoms affects the binding of coronene and hexahelicene monomers and dimers on Au(111) and Cu(111) substrates, using a density functional theory approach. We found that helicene monomers were more effectively able to adapt to the presence of the adatoms than coronene, by coiling around the adatoms. Whereas upon adsorption on a pristine (111) surface, coronene can establish significantly stronger dispersive interactions than helicenes, adatom defects reverse the trend. For helicenes, the extent of flattening near the surface and molecular coiling are strongly influenced by the size of the defect, as a result of the interplay between the molecule’s drive to maximize overlap with the underlying surface and the enhanced reactivity of the low-coordinated adatoms.

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 score0.025
Threshold uncertainty score0.807

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.034
GPT teacher head0.241
Teacher spread0.207 · 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

Citations0
Published2023
Admission routes2
Has abstractyes

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