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Coating of Low-Cost Asphaltenes-Derived Carbon Fibers with V<sub>2</sub>O<sub>5</sub> for Supercapacitor Application

2022· article· en· W4214680296 on OpenAlexafffund
Desirée Leistenschneider, Zahra Abedi, Douglas G. Ivey, Weixing Chen

Bibliographic record

VenueEnergy & Fuels · 2022
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Alberta
FundersAlberta InnovatesChina National Offshore Oil Corporation
KeywordsMaterials sciencePseudocapacitanceAnnealing (glass)SupercapacitorCoatingAsphalteneChemical engineeringCapacitancePorosityComposite materialElectrolyteElectrodeChemistry

Abstract

fetched live from OpenAlex

V2O5-coated, low-cost carbon fibers were produced using asphaltenes and V2O3 as precursors, both industrial byproducts. Asphaltenes can be used without any further treatment to fabricate low-cost carbon fibers. The carbon fibers were coated with V2O5 using a straightforward two-step coating procedure, that is, surface etching of fibers with NH4OH, followed by dipping into a V2O3 solution. This facile process represents a time-saving and low-cost alternative to other metal oxide coating techniques, such as atomic layer deposition, to create fiber-based electrode materials. After annealing, the fibers were homogeneously coated with V2O5 and exhibited porosity. Annealing time and temperature influenced the porosity and structure of the fiber material. The highest surface area (440 m2 g–1) was obtained at the highest annealing temperature. The coated fibers show both micropores and mesopores. The different fiber samples were then utilized as supercapacitors using a 1 M Li2SO4 electrolyte. Specific capacitances of up to 125 F g–1 at a rate of 0.25 A g–1 were achieved. Long-term cycling tests showed a capacitance retention of 89% after 10,000 cycles. A strong surface area dependence for the capacitance and increased pseudocapacitance for higher annealing temperatures are demonstrated.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.004
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.208
Teacher spread0.197 · 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.

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
Published2022
Admission routes2
Has abstractyes

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