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Record W4407960559 · doi:10.1149/1945-7111/adba8f

Operando 3D Imaging of Electrolyte Motion in Cylindrical Li-Ion Cells

2025· article· en· W4407960559 on OpenAlexfundno aff
Toby Bond, Sergey Gasilov, Reid Dressler, Rémi Petibon, Sunny Hy, J. R. Dahn

Bibliographic record

VenueJournal of The Electrochemical Society · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsnot available
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health ResearchCanada Foundation for Innovation
KeywordsElectrolyteIonMotion (physics)Materials scienceChemistryAnalytical Chemistry (journal)PhysicsClassical mechanicsPhysical chemistryElectrodeChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

Electrolyte motion in commercial Li-ion batteries has become an important topic as researchers seek to understand patterns of degradation that occur in large-format cells. Recent work has linked the motion of excess electrolyte to Li plating on the anode of large-format cells after repeated fast charging - an effect known as electrolyte motion induced salt inhomogeneity (EMSI). Mapping the distribution and flow patterns of electrolyte in the cell is critical to understanding these phenomena and predicting the patterns of Li plating that can result. In this work, we used time-resolved, synchrotron computed tomography (CT) to directly image the flow of electrolyte in two commercial 18650 cells during cycling, with one cell containing SiOx in the negative electrode and the other containing only graphite. The former cell shows significantly more electrolyte “pumping” during charge and discharge as well as asymmetric redistribution of salt along the jelly roll after hundreds of cycles. The results yield new insights into how electrolyte motion and its effects are influenced by the composition, geometry, and orientation of the cell.

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 score0.215
Threshold uncertainty score0.491

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 head0.004
GPT teacher head0.240
Teacher spread0.236 · 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

Citations27
Published2025
Admission routes1
Has abstractyes

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