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Measuring Electrolyte Salt Concentration Gradients in Lithium-Ion Batteries using Benchtop μ-XRF

2025· article· en· W4412990860 on OpenAlexafffund
Brittany Pelletier‐Villeneuve, Bastian Krueger, Jonathan Ralph Adsetts, M. Ricard, Ben Ruchte, Steen B. Schougaard

Bibliographic record

VenueACS electrochemistry. · 2025
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsMcGill UniversityUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsElectrolyteLithium (medication)Salt (chemistry)IonMaterials scienceChemistryAnalytical Chemistry (journal)Environmental chemistryElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

The electrolyte salt’s concentration gradients formed during fast (dis)charging limit Li-ion battery power performance. As such, studying them is highly desirable for battery development. In this study, a benchtop μ-X-ray fluorescence (μ-XRF) instrument was used to track electrolyte salt concentrations during charge–discharge cycling of a Li-ion half-cell. We show that time-dependent mapping of the cell using the benchtop instrument can yield temporal and spatial resolution reaching an analytical quality comparable to the state-of-the-art synchrotron-based μ-XRF measurements. As such, this proof-of-concept shows the possibility of obtaining critical electrolyte concentrations without the need for synchrotron radiation.

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 categoriesMeta-epidemiology (narrow)
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 score0.008
Threshold uncertainty score1.000

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 communication0.0000.000
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 head0.013
GPT teacher head0.236
Teacher spread0.224 · 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.

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

Citations2
Published2025
Admission routes2
Has abstractyes

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