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Record W2319808431 · doi:10.1021/jz301207g

Electrochemical Supercapacitor Electrodes from Sponge-like Graphene Nanoarchitectures with Ultrahigh Power Density

2012· article· en· W2319808431 on OpenAlexafffund
Zhanwei Xu, Zhi Li, Chris Holt, Xuehai Tan, Huanlei Wang, Babak Shalchi Amirkhiz, Tyler Stephenson, David Mitlin

Bibliographic record

VenueThe Journal of Physical Chemistry Letters · 2012
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsNational Institute for NanotechnologyUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercapacitorMaterials scienceIonic liquidGrapheneElectrochemistryCapacitancePower densityNanotechnologyCarbonizationElectrodeChemical engineeringCarbon nanotubeHorizontal scan rateIonic bondingOptoelectronicsCyclic voltammetryComposite materialPower (physics)Organic chemistryIonChemistry

Abstract

fetched live from OpenAlex

We employed a microwave synthesis process of cobalt phthalocyanine molecules templated by acid-functionalized multiwalled carbon nanotubes to create three-dimensional sponge-like graphene nanoarchitectures suited for ionic liquid-based electrochemical capacitor electrodes that operate at very high scan rates. The sequential "bottom-up" molecular synthesis and subsequent carbonization process took less than 20 min to complete. The 3D nanoarchitectures are able to deliver an energy density of 7.1 W·h kg(-1) even at an extra high power density of 48 000 W kg(-1). In addition, the ionic liquid supercapacitor based on this material works very well at room temperature due to its fully opened structures, which is ideal for the high-power energy application requiring more tolerance to temperature variation. Moreover, the structures are stable in both ionic liquids and 1 M H2SO4, retaining 90 and 98% capacitance after 10 000 cycles, respectively.

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
Bibliometrics0.0000.000
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 head0.005
GPT teacher head0.194
Teacher spread0.189 · 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

Citations191
Published2012
Admission routes2
Has abstractyes

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