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Record W4412749583 · doi:10.1016/j.memsci.2025.124505

Is vanadium ion permeability independent of membrane thickness?

2025· article· en· W4412749583 on OpenAlexaff
Monja Schilling, Vincent Christanto, Muhammad Mara Ikhsan, Roswitha Zeis, Dirk Henkensmeier

Bibliographic record

VenueJournal of Membrane Science · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced battery technologies research
Canadian institutionsUniversity of Toronto
FundersKorea Institute of Science and TechnologyDeutscher Akademischer Austauschdienst
KeywordsVanadiumMembranePermeability (electromagnetism)IonMaterials scienceChemical engineeringChemistryInorganic chemistryOrganic chemistryEngineeringBiochemistry

Abstract

fetched live from OpenAlex

Testing the diffusion-driven crossover of vanadium ions in diffusion cells is a standard characterization method for membranes used in vanadium redox flow batteries. For better comparability, the flux is recalculated into a permeability value by normalizing the flux for membrane thickness, area, the cell volume, and the concentration difference between the donating and receiving cells. The assumption is that permeability is an intrinsic material property, independent of membrane thickness. However, this work reveals that this assumption is not correct. By analyzing the vanadium crossover through membranes of different thickness, it is demonstrated that the transport of VO 2+ ions across membranes is controlled by two transport resistances: R IF (a resistance hindering ion transport over the membrane/solution interface into and out of the membrane) and R bulk (the transport resistance through the membrane). Comparable permeability values are only obtained when membrane thicknesses are so large that R bulk >> R IF . Consequently, the permeability data reported in the literature might have low accuracy. R IF emerges as an important new development target, which, if well understood, can lead to breakthroughs in membrane performance.

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)0.002
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 score0.012
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.016
GPT teacher head0.294
Teacher spread0.278 · 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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