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Record W2542212132 · doi:10.11159/icnnfc16.132

A Facile and Effective Method for Size Sorting of Large Flake Graphene Oxide

2016· article· en· W2542212132 on OpenAlexvenueno aff
Ece Özçakır, Volkan Eskizeybek

Bibliographic record

VenueProceedings of the World Congress on Recent Advances in Nanotechnology · 2016
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsnot available
FundersTürkiye Bilimsel ve Teknolojik Araştırma KurumuÇanakkale Onsekiz Mart Üniversitesi
KeywordsFlakeGrapheneSortingMaterials scienceOxideNanotechnologyComputer scienceComposite materialAlgorithmMetallurgy

Abstract

fetched live from OpenAlex

The size of the building blocks fundamentally governs physical performances of the macro-scale graphene based structures since larger building blocks usually yields better mechanical and electrical properties. Density gradient centrifugation method has emerged as a versatile and scalable method for sorting colloidal 2D nanomaterials. This paper provides a facile and effective size sorting approach to graphene oxide (GO) flakes with large sizes up to 40 m. The GO flakes were dispersed within distilled water. The centrifugation process parameters were calculated with respect to specific size ranges of GO flakes. Scanning electron microscopy utilized to prove the effectiveness of the separation process. Image processing analysis showed GO flakes with specific size ranges can be separate from aqueous suspension by controlling rotational speed and centrifugation time. The process was performed by using common benchtop centrifuges with low intensity centrifugal fields which requires low investment for a scalable process.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.081
Threshold uncertainty score0.313

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
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 head0.008
GPT teacher head0.311
Teacher spread0.303 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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