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Record W2737076292 · doi:10.1002/smll.201701926

High‐Modulus Low‐Cost Carbon Fibers from Polyethylene Enabled by Boron Catalyzed Graphitization

2017· article· en· W2737076292 on OpenAlexaff
Bryan E. Barton, Michael Behr, Jasson T. Patton, Eric J. Hukkanen, Brian Landes, Weijun Wang, Nicholas Horstman, James E. Rix, Denis T. Keane, Steven Weigand, Mark A. Spalding, Chris Derstine

Bibliographic record

VenueSmall · 2017
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsDow Chemical (Canada)
Fundersnot available
KeywordsPolyacrylonitrileMaterials scienceCarbonizationUltimate tensile strengthPolyethyleneComposite materialBoronModulusCarbon fibersYoung's modulusSpinningPolymerComposite numberOrganic chemistryChemistry

Abstract

fetched live from OpenAlex

Currently, carbon fibers (CFs) from the solution spinning, air oxidation, and carbonization of polyacrylonitrile impose a lower price limit of ≈$10 per lb, limiting the growth in industrial and automotive markets. Polyethylene is a promising precursor to enable a high-volume industrial grade CF as it is low cost, melt spinnable and has high carbon content. However, sulfonated polyethylene (SPE)-derived CFs have thus far fallen short of the 200 GPa tensile modulus threshold for industrial applicability. Here, a graphitization process is presented catalyzed by the addition of boron that produces carbon fiber with >400 GPa tensile modulus at 2400 °C. Wide angle X-ray diffraction collected during carbonization reveals that the presence of boron reduces the onset of graphitization by nearly 400 °C, beginning around 1200 °C. The B-doped SPE-CFs herein attain 200 GPa tensile modulus and 2.4 GPa tensile strength at the practical carbonization temperature of 1800 °C.

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 head0.005
GPT teacher head0.180
Teacher spread0.175 · 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

Citations61
Published2017
Admission routes1
Has abstractyes

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