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Record W2738381806 · doi:10.1149/ma2017-02/7/605

(Invited) Pseudocapacitive Vs Battery Type Electrodes: Two Distinctive Aspects of Fast Electrochemical Processes and Devices

2017· article· en· W2738381806 on OpenAlexaff
Thierry Brousse, Jeffrey W. Long, Daniel Bélanger

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPseudocapacitanceSupercapacitorMaterials scienceCapacitorEnergy storageElectrodeElectrochemistryPower densityBattery (electricity)NanotechnologyCapacitive sensingElectrochemical energy conversionOptoelectronicsElectrical engineeringVoltagePower (physics)Chemistry

Abstract

fetched live from OpenAlex

Electrochemical capacitors (ECs, also sometimes denoted as “supercapacitors” or “ultracapacitors”) [1,2] are energy-storage devices that bridge the performance gap between the high energy density provided by batteries and the high power density (but very limited energy density) derived from dielectric capacitors. Commercially available electrochemical capacitors exhibit gravimetric energy density up to 8.5 Wh.kg-1 and usable power density up to 9.0 kW.kg-1. In the field of electrochemical capacitors there is often confusion between the electrical parameters of a full device and the electrochemical properties of the individual electrodes that comprise the cell [3]. The focus of this communication is to describe the distinctions between these various devices and their constituents, starting with a comparison of dielectric capacitors versus electrochemical capacitors, followed by discussion of other electrochemical energy storage devices with regard to their electrical properties. The electrochemical behavior of common electrode materials used in ECs and related devices will be discussed in terms of capacitive, pseudocapacitive [3] and Faradic charge-storage mechanisms, as well as recommended methods with which such electrodes should be characterized. The distinctions between carbon-based capacitive electrodes [4] that are commonly found in commercial ECs, and pseudocapacitive electrodes such as RuO2 [5,6], or MnO2[7,8], that have the electrochemical signature of a capacitive electrode but express different charge-storage mechanisms, will be highlighted. Finally, the important distinctions between high-power battery-type electrodes and pseudocapacitive electrodes will be described. New emerging concepts such as extrinsic pseudocapacitance [9] or intercalation pseudocapacitance [10] will be discussed at the light of recent results in the field. [1] Conway BE. Electrochemical Capacitors: Scientific Fundamentals and Technology Applications. New-York:Kluwer Academic/Plenum Publishers;1999. [2] Béguin F, Frackowiak E. Supercapacitors: Materials, Systems, and Applications, Weinheim, Germany Wiley-VCH Verlag GmbH & Co.; 2013. [3] Brousse T, Bélanger D, Long JW. To be or not to be pseudocapacitive? J Electrochem Soc 2015;62(5): A5185-9. [4] Simon P, Gogotsi Y. Materials for electrochemical capacitors. Nature Materials 2008; 7: 845-854. [5] Ardizzone S, Fregonara G, Trasatti S. “Inner” and “outer” active surface of RuO2 electrodes. Electrochim Acta 1990;35(1):263-7. [6] Zheng JP, Cygan PJ, Jow TR, Hydrous ruthenium oxide as an electrode material for electrochemical capacitors. J Electrochem Soc 1995;142(8):2699-703. [7] Lee HY, Goodenough JB., Supercapacitor Behavior with KCl Electrolyte. J Solid State Chem 1999;144(1):220-3. [8] Toupin M, Brousse T, Bélanger D. Charge storage mechanism of MnO2 electrode used in aqueous electrochemical capacitor. Chem Mater 2004;16:3184-90. [9] Augustyn V, Simon P, Dunn B. Pseudocapacitive oxide materials for high-rate electrochemical energy storage. Energy Environ Sci.2014;7:1597-1614. [10] Simon P, Gogotsi Y, Dunn B., Where do batteries end and supercapacitors begin? Science 2014;343 :1210-1.

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)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.048
Threshold uncertainty score0.160

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0480.026

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 head0.016
GPT teacher head0.261
Teacher spread0.245 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations2
Published2017
Admission routes1
Has abstractyes

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