MétaCan
Menu
← Back to cohort
Record W2907346390 · doi:10.1109/ifetc.2018.8584015

Bending Properties of Solid Thin Flexible Energy Storage Devices

2018· article· en· W2907346390 on OpenAlexaff
Haoran Wu, Julian Rosas, Keryn Lian

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceBendingBend radiusComposite materialElectrolyteElectrodeSeparator (oil production)Delamination (geology)Leakage (economics)CapacitorOptoelectronicsElectrical engineeringVoltageChemistry

Abstract

fetched live from OpenAlex

The performance of solid, thin and flexible electrochemical capacitors (ECs) under different bending conditions were investigated. The bending parameters include bending angle, bending radius and bending cycle. While the bending angle does not affect the performance of the solid EC cells significantly, small bending radius increases the cell resistance from a delamination at the current collector/electrode interface. A large bending cycle causes a severe self-discharging by losing mechanical protection at the electrolyte/separator layer. The layer is pierced through, creating localized contacts between electrodes which lead to a high leakage current. The electrode/electrolyte interface remains relatively intact under various bending conditions. The investigation of these parameters together with cross-sectional analyses provide a systematic understanding of the failure mechanism of thin and flexible ECs under bending. Although the approach was demonstrated on a sandwiched solid EC cell with a commercial activated carbon and a neutral pH polymer electrolyte, it can be extended for quantified investigations of mechanical properties of general solid flexible electrochemical 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)0.001
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.038
GPT teacher head0.257
Teacher spread0.219 · 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 routes1
Has abstractyes

Explore more

Same topicSupercapacitor Materials and Fabrication→French-language works237,207→