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Record W3025529278 · doi:10.1149/ma2020-018739mtgabs

Fabrication and Plastic Deformation of Graphene Oxide Paper

2020· article· en· W3025529278 on OpenAlexaff
Siyu Liu, Kaiwen Hu, Marta Cerruti, François Barthelat

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldMaterials Science
TopicElectrospun Nanofibers in Biomedical Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceNanosheetComposite materialGrapheneOxideUltimate tensile strengthFlexural modulusHydrogen bondvan der Waals forceNanotechnologyMetallurgyOrganic chemistryChemistryMolecule

Abstract

fetched live from OpenAlex

Graphene oxide (GO) derives from graphite through oxidation, with subsequent dispersion and exfoliation in suitable solvents [1]. GO contains various oxygen-containing functional groups, mainly including hydroxyl, carboxyl, carbonyl and epoxy groups [2]. These oxygen-containing groups make GO highly hydrophilic and can form stable aqueous colloids to assemble paper-like materials by simple and inexpensive solution processes [3]. A monolayer GO nanosheet has high Young’s modulus (207.6 GPa [4]) and ultimate strength (63 GPa [5]). However when individual GO nanosheets are assembled into mesoscopic sheets or “paper”, these mechanical properties decrease significantly because GO sheets are only weakly connected by hydrogen bonds and van der Waals interactions [6]. GO paper also has low plasticity due to the limited movement of dislocation dipoles in a hexagonal lattice [7-8], so it is very brittle and cannot form complex 3D structures. The fabrication of GO paper thus needs to be optimized to achieve better mechanical properties and wider applications. In this study we fabricated ultra-stiff and strong GO paper with controlled thicknesses by directed-flow vacuum filtration. Young’s modulus and ultimate strength were improved by three methods: (i) borax cross-linking, to form a combined hydrogen and covalent bonding system; (ii) thermal annealing, to evaporate inter-sheet water to make GO paper more compact; (iii) sonication, to disperse GO nanosheet more uniformly before filtration. Mechanical tests were performed by both flexion (three-point bending) and uniaxial tension. We found that the flexural modulus of GO paper is significantly lower than the tensile modulus because of interlayer shearing, micro-buckling and delamination during flexural deformation. The stiffest material we made has a tensile modulus of 109.9 GPa and a flexural modulus of 45.7 GPa, which is among the strongest and stiffest GO papers in the open literature (Fig.1) [9]. Another challenge is to make GO paper plastically deformable, a requirement to form 3D structures from flat paper-like materials. Here we developed a low-cost and eco-friendly method to “plasticize” GO paper by the addition of a slurry of cellulose fibers. After mixing 25 wt.% cellulose slurry with GO suspension, the filtrated composite paper can retain 85% of Young’s modulus but shows around three times larger fracture strain than pure GO paper. Using the GO paper reinforced by office paper slurry, we successfully formed semi-spheres with smooth and compact surfaces using an embossing method. The stiffness of the deformed structure was further improved by immersion cross-linking in borax solution. The stiff and lightweight semi-spheres can be used as mechanical structures such as acoustic diaphragms and protective layers. After reduction either by chemical or thermal method, the application can be further expanded to supercapacitors, actuators and electrode materials. References [1] Dreyer D.R.; Park S.; Bielawski C.W.; Ruoff R.S. The chemistry of graphene oxide. Chemical Society Reviews 2010, 39, 228-240. [2] Eda, G.; Chhowalla, M. Chemically derived graphene oxide: towards large-area thin-film electronics and optoelectronics. Advanced Materials 2010, 22, 2392-2415. [3] Pei S.; Cheng H.M. The reduction of graphene oxide. Carbon 2012, 50, (9), 3210-3228. [4] Suk J.W.; Piner R.D.; An J.; Ruoff R.S. Mechanical properties of monolayer graphene oxide. ACS Nano 2010, 4, (11), 6557-6564. [5] Paci J.T.; Belytschko T.; Schatz G.C. Computational studies of the structure, behavior upon heating, and mechanical properties of graphite oxide. The Journal of Physical Chemistry C 2007, 111, (49), 18099-18111. [6] Medhekar N.V.; Ramasubramaniam A.; Ruoff R.S.; Shenoy V.B. Hydrogen bond networks in graphene oxide composite paper: structure and mechanical properties. ACS Nano 2010, 4, (4), 2300-2306. [7] Chen S.; Chrzan D.C. Continuum theory of dislocations and buckling in graphene. Physical Review B 2011, 84, 214103(1-5). [8] Warner J.H.; Margine E.R.; Mukai M.; Robertson A.W.; Giustino F.; Kirkland A.I. Dislocation-driven deformations in graphene. Science 2012, 337, 209-212. [9] Liu S.; Hu K.; Cerruti M.; Barthelat F. Ultra-stiff graphene oxide paper prepared by directed-flow vacuum filtration. Carbon. In press. DOI: 10.1016/j.carbon.2019.11.007. Figure 1

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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 score0.003

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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