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Record W2740269560 · doi:10.1002/cssc.201701211

Voronoi‐Tessellated Graphite Produced by Low‐Temperature Catalytic Graphitization from Renewable Resources

2017· article· en· W2740269560 on OpenAlexafffund
Leyi Zhao, Xiuyun Zhao, Luke Burke, Craig Bennett, R. A. Dunlap, M. N. Obrovac

Bibliographic record

VenueChemSusChem · 2017
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsAcadia UniversityDalhousie University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada
KeywordsGraphiteMaterials scienceCarbon fibersVoronoi diagramParticle (ecology)Chemical engineeringPerpendicularComposite materialComposite numberGeometryGeology

Abstract

fetched live from OpenAlex

A highly crystalline graphite powder was prepared from the low temperature (800-1000 °C) graphitization of renewable hard carbon precursors using a magnesium catalyst. The resulting graphite particles are composed of Voronoi-tessellated regions comprising irregular sheets; each Voronoi-tessellated region having a small "seed" particle located near their centroid on the surface. This suggests nucleated outward growth of graphitic carbon, which has not been previously observed. Each seed particle consists of a spheroidal graphite shell on the inside of which hexagonal graphite platelets are perpendicularly affixed. This results in a unique high surface area graphite with a high degree of graphitization that is made with renewable feedstocks at temperatures far below that conventionally used for artificial graphites.

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 head0.004
GPT teacher head0.177
Teacher spread0.173 · 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

Citations37
Published2017
Admission routes2
Has abstractyes

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