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Record W4409280223 · doi:10.1002/cssc.202500282

Investigating Bubble Formation and Evolution in Vanadium Redox Flow Batteries via Synchrotron X‐Ray Imaging

2025· article· en· W4409280223 on OpenAlexaff
Kangjun Duan, Kerstin Köble, Alexey Ershov, Monja Schilling, Alexander Rampf, A. Cecilia, Tomáš Faragó, M. S. Zubér, Tilo Baumbach, Pang‐Chieh Sui, Roswitha Zeis

Bibliographic record

VenueChemSusChem · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of TorontoUniversity of Victoria
FundersChina Scholarship CouncilDeutsche ForschungsgemeinschaftFonds der Chemischen Industrie
KeywordsElectrodeBubbleSynchrotronElectrolyteVanadiumChemical physicsLiquid bubbleMaterials scienceRedoxElectrochemistryHydrogenPorosityAnalytical Chemistry (journal)ChemistryNanotechnologyChemical engineeringMetallurgyMechanicsOpticsComposite materialPhysical chemistry

Abstract

fetched live from OpenAlex

The parasitic hydrogen evolution reaction (HER) hinders electrolyte transport. It reduces the effective electrochemical surface area in the negative half-cell of vanadium redox flow batteries (VRFBs), resulting in substantial efficiency losses. This study investigates the formation and evolution of hydrogen bubbles within VRFB electrodes through comprehensive experimental characterization and a detailed analysis of the resolved bubbles. The electrode is imaged using synchrotron X-ray tomography, and gas bubbles in the images are identified and characterized using a deep learning model combined with a morphological analysis tool. The HER intensity increases at more negative working electrode potentials, causing residual bubbles to grow and fuse in the electrode central region. In contrast, independent bubbles predominantly form at the electrode edges. Furthermore, bubble growth leads to the gradual development of irregular shapes. These observations provide insights into bubble formation and evolution rules, contributing to a better understanding of the system.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.457
Threshold uncertainty score0.659

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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

Citations4
Published2025
Admission routes1
Has abstractyes

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