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Record W4285497205 · doi:10.1149/ma2022-012189mtgabs

Modeling Approaches for the Description of the Carbon Black Particle Size in Batch and Continuous Dispersion Processes for Lithium-Ion Battery Slurries

2022· article· en· W4285497205 on OpenAlexaff
Julian Mayer, Arno Kwade

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEngineering
TopicAdvanced Battery Technologies Research
Canadian institutionsInstitute of Particle Physics
Fundersnot available
KeywordsCarbon blackMaterials scienceAgglomerateDispersion (optics)Lithium-ion batterySeparator (oil production)SlurryProcess engineeringMechanical engineeringBattery (electricity)Composite materialPower (physics)ThermodynamicsEngineeringPhysics

Abstract

fetched live from OpenAlex

Energy storage is a key technology for alternative power trains like electric and hybrid electric vehicles. Lithium-ion batteries (LIB) are widely used for this purpose due to their high energy density and elaborated developmental state. Also, the increasing usage of electrified transportation leads to ever-increasing demands on LIBs in terms of fast charging ability and power density. The fragmentation of carbon black (CB) aggregates and agglomerates, respectively, has high impact on the resulting microstructure and mechanical integrity as well as conductivity of electrodes and furthermore, the electrochemical performance of LIBs [1]. To achieve optimized cell performances and to reduce the process times or increase throughputs while conserving the slurry quality, a method to predict the CB particle sizes based on simulations and experiments has been developed. This work demonstrates how the dispersion process can be modelled, thus laying the foundation for an optimized microstructure of the electrode right at the beginning of the process chain. For this purpose, a high-intensity batch process in a planetary mixer is investigated and modelled for the description of the CB fragmentation, and the approach is then applied to a continuous twin-screw extruder. Through the use of computational fluid dynamics, theoretical equations and considerations, and experimentally obtained data, the models are parametrized and can be used to predict the result of the dispersion process in form of the CB particle size. The critical values here are the shear rate in the laminar flow and the viscosity of the slurry, which determine the transferred energy onto the particles during the process. By appropriately modelling these characteristic values, it is possible to transfer the models to machines of different types and designs. Furthermore, it is shown how the predicted particle size of CB affects the microstructure of the electrodes. For this purpose, an innovative structural parameter is calculated from mercury intrusion measurements of manufactured electrodes, which expresses the internal porosity of the CB particles, or of the conductive microstructure, respectively. Based on the modelled CB particle size, this parameter can already be used to estimate the developing microstructure of the electrodes depending on the selected process parameters during dispersion. Such an approach is the basis for further work in order to holistically depict and digitalize the entire process chain of the LIB production and its counteracting influences. The experimental process data (power uptake of dispersion machines, viscosities, CB particle sizes), electrode structures, electrode properties (adhesive tensile strengths, spec. resistances) and cell performance data are shown to illustrate the approaches and the value of the created models. All LIB cathodes investigated consist of industrially relevant formulations (> 95 wt% active material NMC622, < 2.5 wt% CB). Reference s : [1] Mayer, J. , Almar, L., Asylbekov, E., Haselrieder, W., Kwade, A., Weber, A. and Nirschl, H., Influence of the Carbon Black Dispersing Process on the Microstructure and Performance of Li‐ion Battery Cathodes, Energy Technol. (2019)

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.041
GPT teacher head0.241
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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".

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Citations0
Published2022
Admission routes1
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