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Record W4409741285 · doi:10.1149/1945-7111/add095

In Situ Deagglomeration of Cathode Particles in Electrode Slurries

2025· article· en· W4409741285 on OpenAlexfundno aff
Laurie Carrier, M. N. Obrovac

Bibliographic record

VenueJournal of The Electrochemical Society · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicRecycling and Waste Management Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSlurryCathodeIn situElectrodeMaterials scienceChemical engineeringComposite materialChemistryOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Synthesis of single-crystal cathode materials for lithium-ion batteries usually results in secondary particles consisting of agglomerations of single crystal primary particles. Deagglomeration is required to obtain good electrochemical performance. Here a deagglomeration method is described where the secondary cathode particles are deagglomerated in situ during the electrode slurry making process. By this in situ slurry milling (ISM) method, the electrode slurry was dispersed while single-crystal lithium nickel manganese cobalt oxide (NMC) cathode particles were simultaneously fully deagglomerated by wet milling in NMP. Compared to dry milling methods, ISM was the most efficient at deagglomerating single-crystal NMC particles while inducing the least lattice microstrain. This resulted in ISM-processed NMC having improved electrochemical performance compared to NMC processed by dry methods. These results show the importance of particle deagglomeration and maintaining low lattice strain in obtaining good cathode cycling performance and that the described ISM method is effective for producing high performance deagglomerated cathode materials.

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)0.001
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 score0.017
Threshold uncertainty score0.170

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.005
GPT teacher head0.243
Teacher spread0.238 · 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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