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Record W2735904128 · doi:10.1149/ma2017-02/3/197

Hair-Based Flexible All-Solid-State Supercapacitor with Wide Operating Voltage and Ultra-High Rate Capability

2017· article· en· W2735904128 on OpenAlexaff
Wenwen Liu, Kun Feng, Gregory Lui, Ricky Tjandra, Lucas Lim, Aiping Yu

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSupercapacitorFlexibility (engineering)Wearable computerEnergy storageElectronicsWearable technologyMaterials sciencePower densityElectrical engineeringComputer sciencePower (physics)EngineeringEmbedded systemElectrodeElectrochemistry

Abstract

fetched live from OpenAlex

Fiber-shaped supercapacitors (FSCs) are a promising candidate as power source or energy storage unit in wearable/stretchable electronics. However, it is still a significant challenge to design FSCs with excellent electrochemical performance while maintaining good flexibility to meet the requirement of wearable/stretchable electronics. Here, a human hair-based flexible all-solid-state asymmetric FSCs has been rationally designed and successfully prepared. Importantly, the as-obtained FSCs show extraordinary flexibility and outstanding electrochemical performance with a wide potential window, excellent rate capability (up to 20,000 mV·s-1), fast frequency response (τ0=55 ms), high volumetric energy density, and long cycle stability. The strategy presented here not only provides a reference for the construction of high-performance flexible FSCs, but also paves a new way to explore the next-generation portable/wearable 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 score0.001
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 head0.022
GPT teacher head0.261
Teacher spread0.239 · 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
Published2017
Admission routes1
Has abstractyes

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