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Record W3011528695 · doi:10.1021/acsnano.0c01281

Redox Flow Batteries: How to Determine Electrochemical Kinetic Parameters

2020· article· en· W3011528695 on OpenAlexafffund
Hao Wang, Sayed Youssef Sayed, Erik J. Luber, Brian C. Olsen, Shubham M. Shirurkar, Sankaranarayanan Venkatakrishnan, Ushula M. Tefashe, Anna K. Farquhar, Eugene S. Smotkin, Richard L. McCreery, Jillian M. Buriak

Bibliographic record

VenueACS Nano · 2020
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsNational Research Council CanadaNational Institute for NanotechnologyUniversity of Alberta
FundersWestern Economic Diversification CanadaNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsUniversity of AlbertaAlberta InnovatesAlberta Innovates - Technology Futures
KeywordsOverpotentialRedoxEnergy storageMaterials scienceElectrochemical energy conversionRenewable energyElectrochemistryNanotechnologyChemistryPower (physics)ThermodynamicsElectrodeElectrical engineering

Abstract

fetched live from OpenAlex

Redox flow batteries (RFBs) are promising energy storage candidates for grid deployment of intermittent renewable energy sources such as wind power and solar energy. Various new redox-active materials have been introduced to develop cost-effective and high-power-density next-generation RFBs. Electrochemical kinetics play critical roles in influencing RFB performance, notably the overpotential and cell power density. Thus, determining the kinetic parameters for the employed redox-active species is essential. In this Perspective, we provide the background, guidelines, and limitations for a proposed electrochemical protocol to define the kinetics of redox-active species in RFBs.

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)0.006
metaresearch head score (Gemma)0.017
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: Methods · Consensus signal: Methods
Teacher disagreement score0.006
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0030.009
Open science0.0030.001
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0050.007

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 head0.019
GPT teacher head0.238
Teacher spread0.220 · 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
GenreMethods

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

Citations253
Published2020
Admission routes2
Has abstractyes

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