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Record W2906965843 · doi:10.1109/ifetc.2018.8583951

Sustainable Materials for Solid Flexible Supercapacitors

2018· article· en· W2906965843 on OpenAlexafffund
Keryn Lian, Haoran Wu, Matthew Genovese, Alvin Virya, Jak Li, Kevin Ton

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSupercapacitorMaterials scienceElectrolyteNanotechnologyEnergy storageSolid-stateFast ion conductorCarbon fibersCapacitive sensingPolymerActivated carbonElectrodeCapacitanceElectrical engineeringEngineering physicsComposite materialEngineeringPower (physics)ChemistryComposite number

Abstract

fetched live from OpenAlex

Solid, thin and flexible supercapacitors have been investigated leveraging sustainable and low-cost biomass-based carbon electrodes and a series of solid polymer electrolytes. The performance of these solid flexible devices was systematically compared to commercial activated carbon (AC) and liquid electrolyte baseline. Solid-state devices especially chitosan AC enabled supercapacitors were shown to closely resemble the highly capacitive behavior and high rate performance of their liquid counterparts. This demonstrates that high surface area, intricately porous activated carbon networks can still be readily accessible to polymer electrolytes, which is important to the transition of supercapacitor devices from liquid to solid-state. These materials and systems represent simple, sustainable and cost-effective approaches for next-generation solid thin, flexible energy storage devices.

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 score0.005
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.280
Teacher spread0.257 · 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

Citations0
Published2018
Admission routes2
Has abstractyes

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