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Record W3044144087 · doi:10.1039/d0nr03372c

Large-extended 2D supramolecular network of dipoles with parallel arrangement on a Si(111)–B surface

2020· article· en· W3044144087 on OpenAlexafffund
Irma Čustović, Damien Teyssieux, Judicaël Jeannoutot, Simon Lamare, Frank Palmino, Hamed Abbasian, Alain Rochefort, Frédéric Chérioux

Bibliographic record

VenueNanoscale · 2020
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSurface and Thin Film Phenomena
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaAgence Nationale de la Recherche
KeywordsSupramolecular chemistryDipoleMoleculeHomogeneousSurface (topology)Chemical physicsMaterials scienceCrystallographyNanotechnologyChemistryPhysicsGeometryOrganic chemistry

Abstract

fetched live from OpenAlex

We have investigated the self-assembly of a strong dipolar molecule (LDipCC) on the semiconducting Si(111)-B surface with scanning tunneling microscopy (STM), density functional theory (DFT) calculations and STM simulations. Although the formation of an extended two-dimensional network was clearly revealed by STM under ultra-high vacuum, the assignment of a specific STM signature to the different terminal groups from the LDipCC molecular unit required a complete analysis by numerical simulations. The overall observed assembly is explained in terms of STM contrasts associated with the molecular structure of LDipCC and the molecule-surface interactions. To distinguish the relative arrangement of the dipolar molecules within the assembly, a rational combination of experimental results and electronic structure calculations allows us to identify a single adsorbed LDipCC phase in which the molecular dipoles are homogeneously arranged into a parallel fashion on the Si(111)-B surface.

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.011
GPT teacher head0.219
Teacher spread0.209 · 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 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

Citations2
Published2020
Admission routes2
Has abstractyes

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