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Record W2299301843 · doi:10.1149/ma2014-02/3/157

Invited: To be or Not to be Pseudocapacitive

2014· article· en· W2299301843 on OpenAlexaff
Thierry Brousse, Annaïg Le Comte, Daniel Bélanger

Bibliographic record

VenueECS Meeting Abstracts · 2014
Typearticle
Languageen
FieldMaterials Science
TopicSupercapacitor Materials and Fabrication
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsSupercapacitorMaterials scienceElectrolyteCapacitanceEnergy storageElectrodePower densityCapacitorNanotechnologyVoltageElectrochemistryNitrideIonic liquidPorosityOptoelectronicsElectrical engineeringComposite materialPower (physics)ChemistryLayer (electronics)

Abstract

fetched live from OpenAlex

Electrochemical capacitors (ECs), so-called supercapacitors are energy storage device that combine a high power density with long cycle life. Their main drawback is their moderate energy density that usually hardly exceeds 5 Wh/kg. This limitation becomes even worse when reported as volumetric energy density, a critical parameter in many applications. Indeed, ECs often have a given volume due to standardization of the size of electrochemical energy storage devices, and the challenge is to optimize the energy stored in such volume. Carbon based device are currently commercialized, but due to their low density and high porosity, the capacitance cannot lead to very high energy density. Their major advantage is the possibility to operate such device with cell voltage up to 2.7V in organic based electrolyte and even higher with ionic liquids. The optimization of the cell capacitance C using carbon electrodes is a dilemma since high porosity is required to enhance electrode/electrolyte interaction but an increase in porosity often translates in a decrease in the density of carbon electrodes. The use of oxide or nitride based pseudocapacitive materials as electrodes also leads to a dilemma since the cell capacitance is usually enhanced but at the expense of the cell voltage, since most of these alternative electrodes can only be operated in aqueous electrolytes. An option is to couple different oxide or nitride based electrodes in order to enhance the cell voltage, playing on HER and EOR overpotentials at the negative and positive side respectively. The main advantage of using such alternative materials to carbon is that they combine fast and reversible surface redox reactions which provide the pseudocapacitive properties and make the electrodes "look like" carbon, but with usually much higher capacitance. However, upon the past 5 years, many studies have targetted the use of electrode materials that definitely do not exhibit pseudocapative behavior. A typical example is the use of Ni(OH)2, a well known alkaline battery material for positive electrode in NiMH cells for example, which is now presented as a pseudocapacitive electrode for supercapacitor. The origin of that probably lies in the use of Ni(OH)2 in hydrid device using a negative carbon electrode (capacitive) and a positive faradaic electrode (Ni(OH)2). Many authors now believe that battery type electrode can be turned in supercapacitor type electrode simply by making composite electrode with various carbons such as graphene or carbon nanotubes. The situation is of course again a dilemna since the resulting composite electrode usually does not fulfill the requirement of pseudocapacitive materials which are concommitant to their electrochemical properties: long term cycling efficiency, power capability, etc... In this communication, the aim will be to clearly show the difference between battery type electrodes and pseudocapacitive materials, based for example on the capacitance calculation which gives tremendously high values in the case of Ni(OH)2 but these values are dependant on the width of the potential window used for the calculation, unlike most of the pseudocapacitive materials identified up to now. Strategies to improve volumetric energy and power densities will also be detailed.

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)0.004
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: Commentary · Consensus signal: none
Teacher disagreement score0.197
Threshold uncertainty score0.660

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0020.001
Scholarly communication0.0030.003
Open science0.0010.002
Research integrity0.0050.005
Insufficient payload (model declined to judge)0.1970.146

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 head0.041
GPT teacher head0.278
Teacher spread0.237 · 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
GenreCommentary

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

Citations7
Published2014
Admission routes1
Has abstractyes

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