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Record W4411213825 · doi:10.1007/s44373-025-00036-8

Systematic refinement of experimental practices to improve repeatability in flow battery cycling

2025· article· en· W4411213825 on OpenAlexfundno aff
Hugh O’Connor, Alexander Quinn, Fikile R. Brushett, Oana M. Istrate, Stephen Glover, Josh J. Bailey, Peter Nockemann

Bibliographic record

VenueDiscover Electrochemistry. · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
FundersQueen's University BelfastRoyal SocietyInvest Northern IrelandQueen's UniversityDepartment for Employment and Learning, Northern IrelandShell Global Solutions InternationalShellAlfred P. Sloan FoundationNational Science Foundation
KeywordsCyclingRepeatabilityBattery (electricity)Reliability engineeringEnvironmental scienceComputer scienceEngineeringMathematicsThermodynamicsStatisticsPhysics

Abstract

fetched live from OpenAlex

Flow batteries represent one of the leading options for large-scale, long-duration energy storage. In recent years, research into this technology has accelerated, with numerous innovative studies focusing on electrolytes, membranes, and electrode materials. Despite this, there is presently no clear set of testing protocols followed during full-cell testing of flow batteries and the experimental techniques detailed in published literature are often insufficient to reproduce results. Furthermore, testing to quantify the repeatability of experiments is not often reported. In this work, various aspects of an experimental procedure developed from the peer-reviewed literature are refined, with voltage efficiency, coulombic efficiency, energy efficiency, and electrolyte utilization used as indicators of repeatability. A set of improved testing protocols are presented for researchers to consider when conducting charge–discharge testing, and additional factors to be reported and studied in the context of repeatability are suggested.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.704

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 communication0.0000.000
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 head0.008
GPT teacher head0.300
Teacher spread0.292 · 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 designBench or experimental
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

Citations1
Published2025
Admission routes1
Has abstractyes

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