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Record W4403814052 · doi:10.1039/d4ra04713c

Reliability studies of vanadium redox flow batteries: upper limit voltage effect

2024· article· en· W4403814052 on OpenAlexafffund
Rajankumar L. Patel, Qian Huang, Bin Li, Alasdair Crawford, Bhuvaneswari M. Sivakumar, Chaojie Song, Zhengming Jiang, Alison Platt, Khalid Fatih, David C. Reed

Bibliographic record

VenueRSC Advances · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsBC Innovation CouncilNational Research Council Canada
FundersPacific Northwest National LaboratoryNational Research Council CanadaOffice of ElectricityBattelleU.S. Department of Energy
KeywordsFlow batteryVanadiumRedoxReliability (semiconductor)Limit (mathematics)VoltageFlow (mathematics)Degradation (telecommunications)Battery (electricity)Materials scienceReliability engineeringChemistryMechanicsEnvironmental scienceElectrical engineeringEngineeringPower (physics)ThermodynamicsPhysicsMetallurgyMathematics

Abstract

fetched live from OpenAlex

cycles). The findings indicate that higher upper voltages significantly decrease capacity and voltage efficiencies. Although electrolyte remixing can restore the majority of the capacity, it only partially recovers voltage efficiency at 1.7 V and 1.8 V, suggesting substantial cell degradation. Analysis reveals that the overpotential increase induced degradation is mainly contributed by the anode during charging and the cathode during discharging. Increased upper voltage amplifies degradation, with the anode being more affected. As confirmed by electrochemical impedance spectroscopy (EIS) and polarization curves, elevated voltages lead to significant resistance increases, driven by charge transfer resistance (mostly from the anode). Moreover, the morphological, surficial, and electrochemical characterization results of cycled electrodes suggest that the degree and mode of degradation were contingent upon the cutoff voltage. For instance, the cathode experienced severe surface degradation at the maximal upper voltage of 1.8 V. This work highlights the importance of optimizing voltage limits to improve the lifetime of VRFBs and offers valuable insights into the development of predictive models through using accelerated stressor lifetime testing (ASLT) protocols for VRFBs.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.751
Threshold uncertainty score0.852

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.014
GPT teacher head0.299
Teacher spread0.284 · 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 designOther design
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

Citations14
Published2024
Admission routes2
Has abstractyes

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