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Record W2231868853 · doi:10.1021/acsnano.5b05771

Interfacial Shear Strength of Multilayer Graphene Oxide Films

2016· article· en· W2231868853 on OpenAlexafffund
Matthew Daly, Changhong Cao, Hao Sun, Yu Sun, Tobin Filleter, Chandra Veer Singh

Bibliographic record

VenueACS Nano · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of New BrunswickUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of TorontoCanada Foundation for Innovation
KeywordsGrapheneMaterials scienceOxideMonolayerMolecular dynamicsNanotechnologyComposite materialAtomic force microscopyWork (physics)Shear strength (soil)Chemical physicsComputational chemistryThermodynamicsChemistryPhysics

Abstract

fetched live from OpenAlex

Graphene oxide (GO) is considered as one of the most promising layered materials with tunable physical properties and applicability in many important engineering applications. In this work, the interfacial behavior of multilayer GO films was directly investigated via GO-to-GO friction force microscopy, and the interfacial shear strength (ISS) was measured to be 5.3 ± 3.2 MPa. Based on high resolution atomic force microscopy images and the available chemical data, targeted molecular dynamics simulations were performed to evaluate the influence of functional structure, topological defects, and interlayer registry on the shear response of the GO films. Theoretical values for shear strength ranging from 17 to 132 MPa were predicted for the different structures studied, providing upper bounds for the ISS. Computational results also revealed the atomic origins of the stochastic nature of friction measurements. Specifically, the wide scatter in experimental measurements was attributed to variations in functional structure and topological defects within the sliding volume. The findings of this study provide important insight for understanding the significant differences in strength between monolayer and bulk graphene oxide materials and can be useful for engineering topological structures with tunable mechanical properties.

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 score0.002
Threshold uncertainty score0.916

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)0.0010.000

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 head0.020
GPT teacher head0.279
Teacher spread0.260 · 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

Citations83
Published2016
Admission routes2
Has abstractyes

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