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Record W2010212541 · doi:10.1021/jp710420k

Supercapacitance of Solid Carbon Nanofibers Made from Ethanol Flames

2008· article· en· W2010212541 on OpenAlexaff
Qiaoliang Bao, Shu‐Juan Bao, Chang Ming Li, Xiang Qi, Chunxu Pan, Jianfeng Zang, Zhisong Lu, Yibin Li, Ding Y. Tang, Sam Zhang, Keryn Lian

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2008
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
FundersFoundation for the Author of National Excellent Doctoral Dissertation of the People's Republic of China
KeywordsSupercapacitorMaterials scienceCarbon nanofiberCapacitanceChemical engineeringMicrostructureCrystallinityElectrochemistryChemical vapor depositionNanofiberSpecific surface areaPorosityCarbon nanotubeNanotechnologyComposite materialElectrodeChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Solid carbon nanofibers (CNFs) made from ethanol flames were used to prepare supercapacitors. Their microstructure, crystallinity, porosity, chemical properties, and electrochemical activity were compared with the multiwalled carbon nanotubes (MWCNTs) synthesized by chemical vapor deposition. The produced CNFs have a unique microstructure with a solid core and porous surface. The specific surface area of CNFs was comparable to that of MWCNTs because of their larger amount of micropores on the surface. The synthesis environment also resulted in abundant functional groups absorbed on the surface of the CNFs. Electrochemical characterization shows that CNFs have much larger capacitance than that of MWCNTs. The capacitance of CNFs consists of both double-layer capacitance contributed by micropores and pseudo-capacitance produced from redox reactions of the absorbed oxygen functional groups. In comparison to the reported MWCNTs-based supercapacitors, the CNF demonstrates more promising potential in energy storage applications because of its larger electrochemical capacitance.

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 score0.002
Threshold uncertainty score0.329

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 head0.015
GPT teacher head0.233
Teacher spread0.217 · 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

Citations87
Published2008
Admission routes1
Has abstractyes

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