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Record W2923652976 · doi:10.1039/c9cp00284g

Structural analysis of helicene molecules adsorbed on symmetric surfaces

2019· article· en· W2923652976 on OpenAlexafffund
Emilian Tuca, Irina Paci

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2019
Typearticle
Languageen
FieldEngineering
TopicSurface Chemistry and Catalysis
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridCompute Canada
KeywordsHeliceneIntramolecular forceSteric effectsIntermolecular forceMonolayerMoleculeChemistryMonomerAdsorptionChemical physicsCrystallographyComputational chemistryStereochemistryOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Helicenes are chiral polyaromatic hydrocarbon molecules which self-assemble into ordered monolayers on solid substrates, and are of current interest in the study of supramolecular systems and the development of smart materials. In this work we investigate the geometry of helicene monomers and stacked dimers on (111) facets of coinage metals. The geometry of the adsorbed molecules is shaped by the coupling of intermolecular dispersive forces, intramolecular steric repulsion between end rings and surface-molecule interactions. Thus, binding and stereospecificity outcomes vary broadly depending on the identity of molecule/surface pair. Overall, homochiral interactions are found to be more effective than heterochiral stacking, due to a better fit between the helical structures in like dimers. On a surface, this effect is enhanced by the flattening of surface-proximal molecular rings. However, our results show that the "sandwich" effect of the second molecular layer increases molecular footprints in the first layer, with potentially large implications in monolayer organization and surface commensuration.

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 categoriesMeta-epidemiology (narrow)
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 score0.057
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.002
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 head0.007
GPT teacher head0.212
Teacher spread0.205 · 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.

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

Citations13
Published2019
Admission routes2
Has abstractyes

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