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Record W3132286609 · doi:10.13140/rg.2.2.29387.62243

Carbon-nanofiber electrodes directly grown on a nickel foam current collector for electrochemical energy storage devices

2020· article· en· W3132286609 on OpenAlexfundno aff
Deepak Sridhar

Bibliographic record

VenueeScholarship@McGill (McGill) · 2020
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaMcGill University
KeywordsElectrodeNickelCurrent (fluid)Materials scienceEnergy storageCurrent collectorCarbon nanofiberElectrochemistryNanotechnologyCarbon nanotubeChemistryMetallurgyElectrical engineeringElectrolyteEngineering

Abstract

fetched live from OpenAlex

Supercapacitors are gaining interest in the energy storage sector and are working their way to replace batteries or to provide a high-power supplement to the battery energy. Despite their high-power density and long cycle life, their energy density is low presently. Even with the use of high surface area materials like graphene, activated carbon, and metal oxides, there has not been a considerable increase in the energy density, mostly due to the electrode preparation methods. This thesis has focused on preparing carbon nano fibers (CNF) that are directly grown on nickel foam current collectors, resulting in electrodes that are not only mechanically stable but also have an extended micro-porous structure on the macro-porous nickel foam. This leads to a electrochemical active surface area ca. 400 m2/g and thus provides a good architecture for enhanced performance of surface-limited electrochemical reactions. The electrode production process does not require an additional catalyst coating step, like other processes for production of carbon nano tubes (CNT) and CNF. Also, the CNF growth process yielding the dense CNF forest is performed at a very low temperature of 400 oC when compared to typical CNT growth processes. This largely simplifies fabrication of the overall structure.The unique structure of the CNF with diameter ca. 50 nm on nickel foam was amalgamated with hydrous ruthenium oxide to achieve higher specific energy and power. Ruthenium oxide was coated on the directly grown CNF on nickel foam using a thermal decomposition technique. The underlying enhanced surface area allowed the coated ruthenium oxide to have a higher surface area, which in turn resulted in large pseudocapacitance. It was also observed that the preparation temperature greatly affected the capacitance.iiiThe prepared materials were characterized using a Scanning electron microscope (SEM), X-ray photon spectroscopy (XPS) and Energy-dispersive X-ray spectroscopy (EDS) to investigate the surface morphology and elemental composition. A thorough electrochemical characterization using cyclic voltammetry, galvanostatic charge-discharge, and electrochemical impedance spectroscopy was done to investigate the material performance as a supercapacitor electrode.The as-grown CNF on nickel foam showed steady retention of capacitance of ca. 140 ± 7 mF/cm2 even when current density increased from 3 to 20 mA/cm2. This is unique to this material as for most electrodes, capacitance significantly drops with increasing current. These electrodes also showed a 100 % retention in capacitance even after 10000 cycles at 10 mA/cm2.For the hydrous ruthenium oxide coated electrodes, a capacitance of 822 ± 4 mF/cm2 at a current density 20 mA/cm2 was achieved, which makes them among the best-performing electrodes reported in the literature. These electrodes also exhibited excellent cycle life with 94 % initial capacitance retention after 5000 charge-discharge cycles. A unique capacitance trend with oxide formation temperature was observed for the ruthenium oxide-CNF electrodes when compared to the trend for ruthenium oxide alone.Overall, this PhD project clearly shows that the hierarchical structures of CNF directly grown on nickel foam provide excellent electrode materials for high specific power supercapacitors, and it also acts as a good support material to form metal-oxide coating to yield high supercapacitor performance metrics. The overall electrochemical behavior of these materials also makes them an ideal candidate for other applications which require high surface area, like hydrogen 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 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 score0.002
Threshold uncertainty score0.007

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.022
GPT teacher head0.233
Teacher spread0.211 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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