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Record W4410980583 · doi:10.1149/1945-7111/ade010

Quantifying Electrolyte Motion in Cylindrical Li-Ion Cells using Rotational Inertia Measurements

2025· article· en· W4410980583 on OpenAlexaff
Emily J. Butler, Nicholas Kowalski, J. Jäger, Michel B. Johnson, Jessie Harlow, Michael Metzger, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsElectrolyteInertiaIonMotion (physics)Rotation around a fixed axisMaterials scienceMechanicsMoment of inertiaChemistryPhysicsClassical mechanicsPhysical chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

State-of-the-art cylindrical Li-ion cells can be susceptible to degradation in the form of accelerated inhomogeneous lithium plating on the negative electrode, which has recently been shown to be caused by an electrolyte motion-induced salt inhomogeneity in the electrolyte. Electrolyte motion is caused by the cyclic expansion and contraction of the electrode active materials at different states of charge, which displaces some of the electrolyte back and forth between the pores of the electrodes and the empty and inactive areas located at the ends of the cylindrical cell and in the core of the electrode winding. To study electrolyte motion, rotational inertia measuring (RIM) instruments have been developed, which are torsional oscillators that detect changes in the resonant frequency of a cylindrical cell as its mass distribution changes during cycling. Resonant frequency changes are used to calculate the amount of displaced electrolyte in relation to the state of charge of the cell. RIM instruments are effective for studying electrolyte motion in a variety of cylindrical cell formats, with the goal of understanding and reducing the impact of electrolyte motion on cell degradation.

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 score0.221
Threshold uncertainty score0.480

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.036
GPT teacher head0.301
Teacher spread0.265 · 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

Citations6
Published2025
Admission routes1
Has abstractyes

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