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Record W4309461906 · doi:10.3390/app122211702

Performance Analysis and Monitoring of Vanadium Redox Flow Battery via Polarization curves

2022· article· en· W4309461906 on OpenAlexaff
Kannika Onyu, Rungsima Yeetsorn, Jeff T. Gostick, Saksitt Chitvuttichot

Bibliographic record

VenueApplied Sciences · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsFlow batteryVanadiumNafionElectrolyteRedoxElectrodeOhmic contactPolarization (electrochemistry)Materials scienceAnalytical Chemistry (journal)VoltageProton exchange membrane fuel cellConcentration polarizationBattery (electricity)Chemical engineeringChemistryMembraneElectrical engineeringInorganic chemistryThermodynamicsEngineeringElectrochemistryChromatographyPhysical chemistry

Abstract

fetched live from OpenAlex

This article proposes the demonstration and deployment of a hand-tailored vanadium redox flow battery test station to investigate the effect of applied voltages on charging performance for electrolyte preparation and the effect of reactant flow rates on the balance of system capacity. Herein, the two different specifications of membranes and a number of electrode layers playing pivotal roles in the discharging characteristics of the VRFB were observed as well. Results indicated that 1.70 V of the charging voltage was suitable, when optimized voltage was considered from charging time, current, and the mole of electrons. The optimized flow rate (10 mL/min) must be controlled since it corresponds to mass transfer and electrolyte diffusion, resulting in reaction ability on electrode surfaces. The number of layers influenced active areas and the diffusion of electrolytes. Nafion 212 provided superior performance to Nafion 117, because it possessed lower ohmic resistance and allowed for easier proton transfer.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.103
Threshold uncertainty score0.274

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations16
Published2022
Admission routes1
Has abstractyes

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