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Record W2806987075 · doi:10.1021/acs.langmuir.8b01574

Rheo-optical Analysis of Functionalized Graphene Suspensions

2018· article· en· W2806987075 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueLangmuir · 2018
Typearticle
Languageen
FieldEngineering
TopicElectrohydrodynamics and Fluid Dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaSchweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung
KeywordsGrapheneMaterials scienceComposite materialSuspension (topology)Dispersion (optics)Flow (mathematics)Shear flowNanotechnologyOpticsMechanicsPhysics

Abstract

fetched live from OpenAlex

Wet processing of graphene sheets is a potentially interesting route for the economically viable creation of graphene-based composites. In the present work, flow dichroism and small-angle light scattering are used to investigate the dispersion of functionalized graphene sheets in a suspension and their response to shear flow. In line with expectations from scaling theory at rest, the functionalized graphene sheets are present as Brownian flat sheets, and there is no evidence of significant crumpling. More surprisingly, we find that the rate-dependent orientation of these molecularly thin sheets can be described by numerical predictions for hard spheroidal sheets, making quantitative predictions of the flow-induced orientation possible. Further comparison of the flow-induced orientation of thick gold decahedra with the thin graphene sheets shows that, except for effects of polydispersity, the flow-induced orientation is predicted well quantitatively. Adequate prediction of the effects of flow on the orientation of graphene sheets makes it possible to design wet processed graphene-based composite materials.

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.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.312

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.210
Teacher spread0.204 · 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