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Record W2332915255 · doi:10.1021/sc500086v

A Study of Carbonized Lignin as an Alternative to Carbon Black

2014· article· en· W2332915255 on OpenAlexaff
Michael R. Snowdon, Amar K. Mohanty, Manjusri Misra

Bibliographic record

VenueACS Sustainable Chemistry & Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsCarbon blackCarbonizationLigninMaterials scienceCarbon fibersBall millPyrolysisChemical engineeringComposite materialOrganic chemistryChemistryScanning electron microscopeNatural rubberComposite number

Abstract

fetched live from OpenAlex

High Resolution Image Download MS PowerPoint Slide The production of biobased carbonaceous powder from bioethanol coproduct lignin for use as a substitute for fossil fuel-derived conductive carbon black filler is examined. The synthesis procedure used for the formation of biobased carbon black is studied in order to obtain properties similar to conventional carbon black. Characterization of the carbon material after varying carbonization temperatures and ball milling times was investigated to optimize carbon size, surface area, and thermal and electrical conductivity. The optimized carbonized ball milled lignin had a carbon content greater than 90% with the majority of the carbon atoms in the sp 2 hybridized state. The carbonized ball milled lignin exhibited a surface area 882% larger and a thermal conductivity 36% greater in comparison to the conductive carbon black tested, while the electrical conductivity was 9.5 S m –1 lower for the carbonized ball milled lignin. This research has demonstrated the possibility of producing biobased carbon black as a potential substitute for commercial carbon black by using lignin as a precursor material.

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 score0.004

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 head0.199
Teacher spread0.195 · 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

Citations153
Published2014
Admission routes1
Has abstractyes

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