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Record W4391662713 · doi:10.1149/ma2023-022285mtgabs

In-Situ Formation of Low-Strain and Defect-Free Single Crystal NMC in Electrode Slurries

2023· article· en· W4391662713 on OpenAlexaff
Laurie Carrier, M. N. Obrovac

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Resonator Technologies
Canadian institutionsDalhousie University
Fundersnot available
KeywordsIn situMaterials scienceSlurryElectrodeStrain (injury)Single crystalCrystallographyComposite materialChemistryAnatomyBiology

Abstract

fetched live from OpenAlex

Synthesis of cathode materials for lithium-ion batteries usually results in agglomerations of single crystals. [1][2] Jet milling is commonly used in industry to deagglomerate cathode material powders. [3] However, dry grinding techniques can often lead to the introduction of defects, requiring a reheating step to reform pristine NMC. Wet milling in water has also been proposed, however, this results in lithium loss and crystal defects, again requiring additional drying and reheating steps are required to reform pristine NMC. [4][5] Here we report a low-strain and defect-free NMC deagglomeration method that is performed in-situ during the electrode slurry dispersion process. In this method, cathode material, conductive additive and binder are planetary milled in N-methyl-2-pyrrolidone (NMP). This results in both the deagglomeration of cathode secondary particles and the formation of a well-dispersed electrode slurry. Figure 1(a-d) compares the SEM images of NMC samples deagglomerated by different dry grinding techniques and the in-situ slurry milling method. Figure 1(e) and (f) show lattice strains and the first cycle voltage curves of the same samples. In comparison to the dry grinding techniques, in-situ slurry milling was found to be more effective in secondary particle deagglomeration than dry methods and resulted in a single crystal NMC with the lowest lattice strain, lowest irreversible capacity, and highest coulombic efficiency. In fact, in-situ milling was found to reduce lattice strain compared to the original agglomerated cathode material, by relieving internal strains created at the grain boundaries of secondary particles. We believe that in-situ slurry milling is an effective method for NMC particle deagglomeration and improving NMC performance. References: [1] Zheng, L.; Bennett, J.C.; Obrovac, M.N. J. Electrochem. Soc. 2020 , 167 , 130536. [2] Lee, S.-Y.; Park, G.-S.; Jung, C.; Ko, D.-S.; Park, S.-Y.; Goo Kim, H.; Hong, S.-H.; Zhu, Y.; Kim, M. Adv. Sci. 2019 , 6 , 1800843. [3] Gommeren, H.J.C.; Heitzmann, D. A.; Molenaar, J. A. C.; Scarlett, B . Powder Technol. 2000 , 18 , 147-154. [4] Kumakura, S.; Paulsen, J.; Yang, T.; Yang, H. Han, S.-Y. US 2020/0381727 A1, 2020. [5] Paulsen, J.; Kumakura, S.; Yang, T.; Kim, D.-H.; Yang, H. US 2021/0143423 A1, 2021. Figure 1

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)0.001
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 score0.107
Threshold uncertainty score0.561

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.010
GPT teacher head0.210
Teacher spread0.200 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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