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Record W4404632120 · doi:10.1021/acsami.4c13247

Production of Carbon Fibers Using a Molten Cu–In Catalyst for Methane Pyrolysis

2024· article· en· W4404632120 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACS Applied Materials & Interfaces · 2024
Typearticle
Languageen
FieldEngineering
TopicFiber-reinforced polymer composites
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationBritish Columbia Knowledge Development FundPacific Institute for Climate Solutions
KeywordsMaterials sciencePyrolysisMethaneCatalysisCarbon fibersChemical engineeringOrganic chemistryComposite materialComposite number

Abstract

fetched live from OpenAlex

Molten metal catalysts for methane pyrolysis and dry reforming are becoming recognized for their potential in decarbonization efforts. Their use in bubble column reactors facilitates continuous operation by allowing the produced carbon to float to the surface for removal. While most reported molten metals produce low-value amorphous carbon or graphitic sheets containing some metals, our study introduces a Cu-In alloy that selectively produces high-purity carbon nanofibers. These nanofibers are tubular and have a smooth or bamboo-like segmented structure with a diameter of approximately 100 nm. We have identified a droplet-based pathway for the growth of these fibers and removal of the droplets, observed consistently across a bubble column reactor, a surface reactor, and both in the absence and presence of carbon dioxide for pyrolysis and dry reforming. The molten Cu-In system is shown to outperform other molten metal catalysts, producing fibers with a purity greater than 99.9% after heat treatment.

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.

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 score0.030
Threshold uncertainty score0.898

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 head0.013
GPT teacher head0.238
Teacher spread0.226 · 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