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Record W4226327848 · doi:10.1021/acs.chemmater.2c00294

Vanishing Electronic Band Gap in Two-Dimensional Hydrogen-Bonded Organic Frameworks

2022· article· en· W4226327848 on OpenAlexafffund
Chenghao Liu, Allan Wei, Maïline Fok Cheung, Dmitrii F. Perepichka

Bibliographic record

VenueChemistry of Materials · 2022
Typearticle
Languageen
FieldMaterials Science
TopicCovalent Organic Framework Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsStackingBand gapDensity functional theoryMaterials scienceTetragonal crystal systemAcceptorFerromagnetismElectronic band structureCondensed matter physicsDopingElectronic structureChemical physicsComputational chemistryCrystallographyChemistryCrystal structureOptoelectronicsPhysicsOrganic chemistry

Abstract

fetched live from OpenAlex

We introduce the concept of two-dimensional donor–acceptor (DA) hydrogen-bonded organic frameworks (HOFs) as a general design strategy to realize low-band-gap flat-band materials. We report the electronic structure calculations for a series of hexagonal and tetragonal DA-HOFs from aromatic quinones and cyclic fused oligopyrroles. Density functional theory calculations show that these HOFs possess reasonable binding energies (∼10 kcal/mol). The HOFs show very strong charge transfer interactions that enhance the DA abilities while maintaining localized orbital distributions. This leads to vanishingly low band gaps ( E g ∼ 0.02–0.07 eV) but with extremely flat bands (bandwidth <0.06 eV). The partial filling of these bands via p- or n-doping leads to Stoner ferromagnetism, with stabilization energies up to 100 meV. Stacking the layers results in the metallization of the porous networks with varying energy dispersions. The symmetry of the band structures can be easily modified by the chemical constituent to represent, for example, Kagome and Lieb lattices.

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 head0.009
GPT teacher head0.250
Teacher spread0.241 · 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

Citations18
Published2022
Admission routes2
Has abstractyes

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