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Chemistry and Properties of Carbon Fiber Feedstocks from Bitumen Asphaltenes

2023· article· en· W4324323674 on OpenAlexaff
Martha L. Chacón‐Patiño, Anika Neumann, Christopher P. Rüger, Paolo G. Bomben, Lukas Friederici, Ralf Zimmermann, Erik Frank, Philipp Kreis, Michael R. Buchmeiser, Murray R. Gray

Bibliographic record

VenueEnergy & Fuels · 2023
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsUniversity of AlbertaAlberta Innovates
FundersH2020 Research InfrastructuresDivision of Materials ResearchDeutsche ForschungsgemeinschaftFlorida Department of StateFlorida State University
KeywordsAsphalteneAlkylChemical engineeringChemistryOrganic chemistryCarbon fibersSolubilityMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Carbon fibers are materials of paramount importance for composites applied in fields such as aerospace engineering, medicine, and renewable energy. Currently, most of the production of carbon fibers uses polyacrylonitrile, which incurs significant greenhouse gas emissions and high production costs. Therefore, carbon fiber manufacturing from asphaltene-enriched feedstocks is attractive as it could add value to extra-heavy fossil fuels and cut down precursor costs by ∼90%. Recent studies indicate that some asphaltene-rich samples feature rheological properties that make them optimal for melt-spinning and carbon fiber production; in other cases, the spun asphaltene feedstocks are unsuitable for such applications. In this work, bitumen asphaltenes were subjected to upgrading processes under three distinctive conditions to tweak their rheological properties in order to produce carbon fibers. The treated samples were comprehensively studied by separations based on solubility and extrography, and subsequently characterized by ultrahigh-resolution mass spectrometry, gas-phase fragmentation, and thermogravimetric analysis. The results indicate that upon thermal processing, asphaltene-rich feedstocks produced a mixture with diverse solubility, i.e., maltenes, asphaltenes, and toluene-insoluble material. The molecular composition of remnant asphaltenes suggests that thermal treatment dramatically decreased molecular polydispersity in terms of the content of heteroatoms, alkyl chains, and structural motifs, i.e., single-core vs multicore, also known as island vs archipelago. The processed asphaltenes revealed high abundances of alkyl-depleted island species. Such thermally treated samples produced no stable carbon fibers. Conversely, a feedstock treated with molten sodium in a proprietary process designed to remove sulfur, revealed increased content of alkyl-side chains and archipelago structural motifs. This sample produced stable carbon fibers. Furthermore, thermal analysis coupled with mass spectrometry (TGA-HRMS) was conducted to understand thermal desorption and pyrolysis profiles for the samples, as well as the presence of occluded compounds and carbon residue formation. The TGA-HRMS results are consistent with extrography and IRMPD FT-ICR MS studies and confirmed the ultrahigh abundance of multicore compounds in the desulfurized sample. Although the sample size is limited, and thus, correlations between molecular composition and rheology properties are not achieved, this is the first study that aims to understand the role of feed composition in the ability to generate carbon fibers from asphaltene-enriched feedstocks. Collectively, the results indicate that samples comprised of abundant highly aromatic asphaltenes, dominant in alkyl-depleted single-core structures, are unlikely optimal for carbon fiber applications. Conversely, a sample with a marked increase in H/C, a significant decrease in S content, and abundant multicore species could generate stable carbon fibers. More studies are underway to find correlations between molecular features and carbon fiber production.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.043
Threshold uncertainty score0.495

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.012
GPT teacher head0.206
Teacher spread0.194 · 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

Citations25
Published2023
Admission routes1
Has abstractyes

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