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Record W3085309965 · doi:10.1021/acsnano.0c05310

van der Waals Epitaxy of Soft Twisted Bilayers: Lattice Relaxation and Mass Density Waves

2020· article· en· W3085309965 on OpenAlexafffund
Cong Jin, Brian C. Olsen, Erik J. Luber, Jillian M. Buriak

Bibliographic record

VenueACS Nano · 2020
Typearticle
Languageen
FieldMaterials Science
TopicThermal properties of materials
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAlberta Innovates - Technology Futures
Keywordsvan der Waals forceCondensed matter physicsMaterials scienceStackingEpitaxyFerromagnetismSuperstructureVicinalBilayerChemical physicsNanotechnologyPhysicsChemistry

Abstract

fetched live from OpenAlex

Interfaces comprising incommensurate or twisted hexagonal lattices are ubiquitous and of great interest, from adsorbed organic/inorganic interfaces in electronic devices, to superlubricants, and more recently to van der Waals bilayer heterostructures (vdWHs) of graphene and other 2D materials that demonstrate a range of properties such as superconductivity and ferromagnetism. Here we show how growth of 2D crystalline domains of soft block copolymers (BCPs) on patterned hard hexagonal lattices provide fundamental insights into van der Waals heteroepitaxy. At moderate registration forces, it is experimentally found that these BCP-hard lattice vdWHs do not adopt a simple moiré superstructure, but instead adopt local structural relaxations known as mass density waves (MDWs). Simulations reveal that MDWs are a primary mechanism of energy minimization and are the origin of the observed preferential twist angle between the 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 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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.549

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.028
GPT teacher head0.231
Teacher spread0.203 · 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

Citations9
Published2020
Admission routes2
Has abstractyes

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