MétaCan
Menu
Back to cohort
Record W4200322074 · doi:10.1002/admi.202101659

Interfacial Assembly of Graphene Oxide: From Super Elastic Interfaces to Liquid‐in‐Liquid Printing

2021· article· en· W4200322074 on OpenAlexafffund
Milad Kamkar, Elnaz Erfanian, Parisa Bazazi, Ahmadreza Ghaffarkhah, Farbod Sharif, Ganhua Xie, Aadithya Kannan, Mohammad Arjmand, S. Hossein Hejazi, Thomas P. Russell, Gerald G. Fuller, Uttandaraman Sundararaj

Bibliographic record

VenueAdvanced Materials Interfaces · 2021
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of CalgaryUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsMaterials scienceGrapheneElastic modulusOxideRheologyNanotechnologyElasticity (physics)FluidicsMicrofluidicsInterface (matter)Chemical engineeringElectronicsComposite materialWettingChemistry

Abstract

fetched live from OpenAlex

Abstract Tailoring the oil/water (O/W) interface is a prerequisite for structuring these two immiscible liquids into prescribed architectures, i.e., liquid‐in‐liquid printing, which is an emerging area in material science. Here, assemblies of graphene oxide (GO) at O/W and air/W interfaces are characterized using a wide range of interfacial rheological techniques. It is shown that the GO nanosheets assemble at the interface, even at extremely low concentrations as low as 0.04 vol%, significantly increasing the elasticity at O/W or air/W interfaces. This is attributed to the combined hydrophobic and hydrophilic nature of GO. Interfacial elastic modulus of ≈1800 mN m −1 is achieved, making GO assemblies exceptional candidates for applications where stabilization of the O/W interface is indispensable. Herein, the super elastic character of the O/W interface with GO assemblies is capitalized for all‐liquid 2D printing in a low viscous oil, underscoring the utility of these all‐liquid systems for electronics, fluidic devices, and controlled release systems.

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 score0.002

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 head0.016
GPT teacher head0.280
Teacher spread0.264 · 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

Citations27
Published2021
Admission routes2
Has abstractyes

Explore more

Same venueAdvanced Materials InterfacesSame topicPickering emulsions and particle stabilizationFrench-language works237,207