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Asphaltene-Based Discontinuous Carbon Fiber

2024· article· en· W4400866747 on OpenAlexafffund
Md. Minhajul Islam, Jiawei Chen, Idaresit Ekaette, Khandaker Akib Shahriar, Shahrad Khodaei Booran, Tri-Dung Ngo, Tian Tang, Mark T. McDermott, Cagri Ayranci

Bibliographic record

VenueEnergy & Fuels · 2024
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsUniversity of Alberta
FundersAlberta Innovates
KeywordsAsphalteneCarbon fibersFiberMaterials scienceChemical engineeringChemistryEnvironmental scienceComposite materialOrganic chemistryComposite numberEngineering

Abstract

fetched live from OpenAlex

The requirements for affordable feedstock, reduced production time, and cost-effective manufacturing are some of the key challenges in carbon fiber (CF) production. Notably, the high cost of petroleum-based polyacrylonitrile feedstock remains a primary obstacle in this field. This study explores the potential of asphaltene, a crude oil byproduct having high aromaticity, as an economical alternative feedstock for CF production enabled by a solution processing fiber production technique utilizing the extensional properties of the polymeric solution. The CFs produced from the asphaltene-based precursor display an average diameter of 3.5 ± 1.6 μm. Tensile tests on single short CFs reveal an ultimate tensile strength of 283.9 ± 92.2 MPa and an elastic modulus of 20.9 ± 5.4 GPa, indicating their suitability for a wide range of material applications.

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 score0.003

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 head0.192
Teacher spread0.186 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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