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Record W2801659244 · doi:10.1149/ma2018-01/7/736

Aqueous Based Asymmetrical-Bipolar Electrochemical Capacitor with a 2.4 V Operating Voltage

2018· article· en· W2801659244 on OpenAlexaff
Haoran Wu, Keryn Lian

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAqueous solutionElectrolyteElectrodeElectrochemistryCapacitanceCapacitorFaraday efficiencyMaterials scienceStandard electrode potentialChemistrySupercapacitorAnalytical Chemistry (journal)VoltageElectrical engineeringChromatographyPhysical chemistry

Abstract

fetched live from OpenAlex

A novel aqueous-based asymmetrical-bipolar electrochemical capacitor (EC) was developed. The EC device has leveraged the contributions from a Zn-CNT asymmetrical electrode and a KOH-H2SO4 dual-pH electrolyte. The positive and the negative electrodes operate in their respective electrolytes that have different pH values, leading to a cell voltage of 2.4 V. The potential tracking of both electrodes reveals that the Zn negative electrode maintains a potential of -1.2 V, while the CNT positive electrode is charged and discharged reversible up to +1.2 V. A bipolar ion exchange membrane effectively separates the acid and the alkaline from neutralizing. As a result, the aqueous-based asymmetrical-bipolar EC device has shown stable performance with a capacitance retention of 94 % and a coulombic efficiency of 99% over 10,000 cycles. The asymmetrical-bipolar design overcomes the thermodynamic limitation of water decomposition of the aqueous-based electrolyte, paving a new avenue towards aqueous-based ECs and energy storage systems with both high energy and power density. Figure 1

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.241
Teacher spread0.229 · 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
Published2018
Admission routes1
Has abstractyes

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