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Record W2966997256 · doi:10.1038/s41467-019-11700-0

Synthesis of radiaannulene oligomers to model the elusive carbon allotrope 6,6,12-graphyne

2019· article· en· W2966997256 on OpenAlexafffund
Martin Drøhse Kilde, Adrian H. Murray, Cecilie Lindholm Andersen, Freja Eilsø Storm, Katrin Schmidt, Anders Kadziola, Kurt V. Mikkelsen, Frank Hampel, Ole Hammerich, Rik R. Tykwinski, Mogens Brøndsted Nielsen

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaDeutsche ForschungsgemeinschaftUniversity of Alberta
KeywordsGraphyneTrimerBand gapMaterials scienceIcosahedral symmetryGrapheneMoleculeChemical physicsCarbon fibersNanotechnologyCrystallographyChemistryDimerOrganic chemistryOptoelectronics

Abstract

fetched live from OpenAlex

Graphyne allotropes of carbon are fascinating materials, and their electronic properties are predicted to rival those of the "wonder material" graphene. One allotrope of graphyne, having rectangular symmetry rather than hexagonal, stands out as particularly attractive, namely 6,6,12-graphyne. It is currently an insurmountable challenge, however, to design and execute a synthesis of this material. Herein, we present synthesis and electronic properties of molecules that serve as model compounds. These oligomers, so-called radiaannulenes, are prepared by iterative acetylenic coupling reactions. Systematic optical and redox studies indicate the effective conjugation length of the radiaannulene oligomers is nearly met by the length of the trimer. The HOMO-LUMO gap suggested by the series of oligomers is still, however, higher than that expected for 6,6,12-graphyne from theory, which predicts two nonequivalent distorted Dirac cones (no band gap). Thus, the radiaannulene oligomers present a suitable length in one dimension of a sheet, but should be expanded in the second dimension to provide a unique representation of 6,6,12-graphyne.

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 score0.071
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.001
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 head0.017
GPT teacher head0.294
Teacher spread0.277 · 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

Citations47
Published2019
Admission routes2
Has abstractyes

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