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Record W2048606495 · doi:10.1038/ncomms7251

One-shot K-region-selective annulative π-extension for nanographene synthesis and functionalization

2015· article· en· W2048606495 on OpenAlexfundno aff
Kyohei Ozaki, Katsuaki Kawasumi, Mari Shibata, Hideto Ito, Kenichiro Itami

Bibliographic record

VenueNature Communications · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
FundersExploratory Research for Advanced TechnologyJapan Society for the Promotion of ScienceQueen's UniversityMinistry of Education, Culture, Sports, Science and Technology
KeywordsMaterials scienceAtom (system on chip)Surface modificationChemical physicsExtension (predicate logic)SiliconEnhanced Data Rates for GSM EvolutionApex (geometry)Topology (electrical circuits)NanotechnologyCombinatorial chemistryChemistryOptoelectronicsComputer scienceGeometryPhysical chemistryCombinatoricsMathematics

Abstract

fetched live from OpenAlex

The optoelectronic nature of two-dimensional sheets of sp(2)-hydridized carbons (for example, graphenes and nanographenes) can be dramatically altered and tuned by altering the degree of π-extension, shape, width and edge topology. Among various approaches to synthesize nanographenes with atom-by-atom precision, one-shot annulative π-extension (APEX) reactions of polycyclic aromatic hydrocarbons hold significant potential not only to achieve a 'growth from template' synthesis of nanographenes, but also to fine-tune the properties of nanographenes. Here we describe one-shot APEX reactions that occur at the K-region (convex armchair edge) of polycyclic aromatic hydrocarbons by the Pd(CH3CN)4(SbF6)2/o-chloranil catalytic system with silicon-bridged aromatics as π-extending agents. Density functional theory calculations suggest that the complete K-region selectivity stems from the olefinic (decreased aromatic) character of the K-region. The protocol is applicable to multiple APEX and sequential APEX reactions, to construct various nanographene structures in a rapid and programmable manner.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.379
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
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 head0.125
GPT teacher head0.359
Teacher spread0.234 · 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 designTheoretical or conceptual
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

Citations202
Published2015
Admission routes1
Has abstractyes

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