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Record W2119327314 · doi:10.1002/celc.201500057

Vanadium Pentoxide Nanorods Anchored to and Wrapped with Graphene Nanosheets for High‐Power Asymmetric Supercapacitors

2015· article· en· W2119327314 on OpenAlexafffund
Ge Li, Xiaolei Wang, Fathy M. Hassan, Matthew Li, Rasim Batmaz, Xingcheng Xiao, Aiping Yu

Bibliographic record

VenueChemElectroChem · 2015
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of Waterloo
KeywordsMaterials scienceSupercapacitorGrapheneNanorodPentoxideAnodeCapacitancePower densityElectrodeNanotechnologyVanadium oxideOxideChemical engineeringVanadiumMetallurgyChemistryPower (physics)

Abstract

fetched live from OpenAlex

Abstract Asymmetric lithium‐ion supercapacitors have been obtaining great attention, owing to their significantly increased energy density. However, transition‐metal‐oxide‐based electrode materials with poor conductivity and sluggish electrode kinetics often sacrifice power performance and cycling stability. Herein, we report the in situ growth of V2O5 nanorods on highly conductive graphene sheets as anode materials with high specific capacitance and excellent rate capability, which is mainly attributed to the intimate contact between the nanorods and graphene sheets. The asymmetric supercapacitor based on graphene/V2O5 composites and activated carbon is fabricated and evaluated, where a high energy density of approximately 50 Wh kg−1 can be achieved at a power density of 136.4 W kg−1, as well as long cycling stability. This work provides an effective and efficient method for the fabrication of high‐performance asymmetric supercapacitors.

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 score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.015
GPT teacher head0.229
Teacher spread0.214 · 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

Citations31
Published2015
Admission routes2
Has abstractyes

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