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Record W4379967282 · doi:10.1002/smll.202302145

Defect Engineering of Graphene for Dynamic Reliability

2023· article· en· W4379967282 on OpenAlexafffund
Boran Kumral, Pedro Guerra Demingos, Teng Cui, Peter Serles, Nima Barri, Chandra Veer Singh, Tobin Filleter

Bibliographic record

VenueSmall · 2023
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaFonds de recherche du Québec – Nature et technologiesAlliance de recherche numérique du CanadaUniversity of TorontoCanada Foundation for Innovation
KeywordsGrapheneMaterials sciencevan der Waals forceNanotechnologySlippageAdhesionPolymerComposite materialMolecular dynamicsContext (archaeology)MoleculeChemistry

Abstract

fetched live from OpenAlex

The interface between two-dimensional (2D) materials and soft, stretchable polymeric substrates is a governing criterion in proposed 2D materials-based flexible devices. This interface is dominated by weak van der Waals forces and there is a large mismatch in elastic constants between the contact materials. Under dynamic loading, slippage, and decoupling of the 2D material is observed, which then leads to extensive damage propagation in the 2D lattice. Herein, graphene is functionalized through mild and controlled defect engineering for a fivefold increase in adhesion at the graphene-polymer interface. Adhesion is characterized experimentally using buckling-based metrology, while molecular dynamics simulations reveal the role of individual defects in the context of adhesion. Under in situ cyclic loading, the increased adhesion inhibits damage initiation and interfacial fatigue propagation within graphene. This work offers insight into achieving dynamically reliable and robust 2D material-polymer contacts, which can facilitate the development of 2D materials-based flexible devices.

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 score0.174
Threshold uncertainty score0.163

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.021
GPT teacher head0.279
Teacher spread0.258 · 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
Published2023
Admission routes2
Has abstractyes

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