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Record W4387568065 · doi:10.21203/rs.3.rs-3385607/v1

Highly Stable Two-Dimensional Carbon Allotropes Based on Azulenoid Kekulene

2023· preprint· en· W4387568065 on OpenAlexafffund
Rustam Z. Khaliullin, Zhenzhe Zhang, Hanh Pham

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldChemistry
TopicSynthesis and Properties of Aromatic Compounds
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsCarbon fibersMaterials scienceChemical engineeringBusinessComposite materialComposite numberEngineering

Abstract

fetched live from OpenAlex

Abstract Despite enormous interest in 2D carbon allotropes, discovering stable 2D carbon structures with practically useful electronic properties presents a significant challenge. This work shows that fusing azulene-derived macrocycles - azulenoid kekulenes (AK) - into graphene leads to the most stable 2D carbon allotropes reported to date, excluding graphene. DFT modeling predicts that placing the AK units in appropriate relative positions in the graphene lattice opens the 0.54eV electronic bandgap and leads to the appearance of the remarkable 0.80eV secondary gap between conduction bands - a feature that is exceedingly rare in 2D carbon allotropes but is known to enhance light absorption and emission in 3D semiconductors. Among porous AK structures, one material stands out as a stable narrow-multigap (0.36 and 0.56eV) semiconductor with light charge carriers (me = 0.17 m0, mh = 0.19 m0), whereas its boron nitride analog is a wide-multigap (1.51 and 0.82eV) semiconductor with mobile carriers (me = 0.39 m0, mh = 0.32 m0). The multigap engineering strategy proposed here can be applied to other carbon nanostructures creating novel 2D materials for electronic and optoelectronic applications.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.187
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.090
GPT teacher head0.347
Teacher spread0.257 · 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 designSimulation or modeling
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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