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Record W2613653884 · doi:10.1149/ma2017-01/7/604

Direct Growth of Carbon Nanofibers on Nickel Foam and Its Application As Electrochemical Supercapacitor Electrodes

2017· article· en· W2613653884 on OpenAlexaff
Deepak Sridhar, Sasha Omanovic, Jean‐Luc Meunier

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsMcGill University
Fundersnot available
KeywordsSupercapacitorCarbon nanofiberMaterials scienceNanotechnologyCarbonizationCarbon fibersElectrospinningElectrolyteNanofiberElectrodeChemical vapor depositionCurrent collectorCarbon blackNanomaterialsChemical engineeringCarbon nanotubeElectrochemistryComposite materialPolymerChemistryComposite number

Abstract

fetched live from OpenAlex

Carbon nanofibers (CNF) are useful for many applications such as polymer reinforcement, electrochemical devices like sensors, batteries, and supercapacitors. The most common method of producing them is by catalytic chemical vapor deposition (CVD) or by electrospinning. CNF growth using CVD is a single step process, unlike electro spinning which requires an additional carbonization step at higher temperatures. In CVD the catalyst is either incorporated directly onto the substrate or introduced with the feed gases. This adds extra cost and set limits to the design of the systems used for the CNF growth process, particularly in cases where porous structures are envisioned. In supercapacitors, the electrode material is most often painted or deposited with an ink of carbon particles. Directly growing the carbon nanomaterial from the supporting surface would eliminate this step and allow complex geometries. It would also help in achieving a higher capacitance by removing the requirement of a binder material and eliminate the occurrence of agglomeration of the active material inherent in the ink deposition process. Another advantage is the higher conductivity of CNF compared to the commonly used active carbon material. With CNF growing directly of the current collector, the addition of conductive carbon black can possibly be eliminated. Finally, an increase access of the electrolyte to the electrode can be expected due to the natural 3-D nano-architecture formed by the CNF. On the other hand, the micro-scale 3-D structure of nickel foams have been the most sought after substrates (current collectors) in the battery and supercapacitor research as they offer more surface area for the deposition of the required active material, together with being non-corrosive in alkali medium. CNF grown directly on Ni-foams are thus expected to extend the performances of supercapacitor electrodes. In this work, we have found a novel method of growing dense CNF on nickel foam. Cyclic voltammetry, galvanotactic charge discharge, and electrochemical impedance spectroscopy was made to electrochemically characterize these CNF/Ni-foam supercapacitor electrodes. When tested as a supercapacitor electrode in 6M KOH, our directly grown CNF gives a good aerial capacitance of about 60 mF/cm2 and an energy density of 16 mWh/m2. We believe this nickel foam with CNF to be very useful in supercapacitors and batteries in decreasing the resistance of the electrochemical device and provide additional surface area.

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
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.241
Teacher spread0.229 · 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

Citations2
Published2017
Admission routes1
Has abstractyes

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