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Record W4400680474 · doi:10.3390/batteries10070250

Numerical Modeling of a Low-Cobalt All-Solid-State Cell with Ceramic Electrolyte Using a Deformable Geometry

2024· article· en· W4400680474 on OpenAlexaff
D. Nadeau, Lionel Roué, François Allard

Bibliographic record

VenueBatteries · 2024
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsMaterials scienceAnodePolarization (electrochemistry)ElectrolyteCeramicElectrodeMechanicsComposite materialComputer scienceChemistry

Abstract

fetched live from OpenAlex

All-solid-state batteries with a lithium negative electrode and a ceramic electrolyte are key toward high energy density. To ensure a safe, fast, accurate, and cost-effective development of this technology, the experimental methodology must be supported by the numerical modeling approach. This work proposes and describes an electrochemical model of a Li7La3Zr2O12 (LLZO) and Ni-rich NMC-based lithium cell with a deformable lithium negative electrode. Simulations were computed using the finite element method at different operating conditions to demonstrate the scope of the modeling work. Discharge rate tests, deformation tracking, geometric defect investigation, and polarization decomposition are described. Theoretical validation of the mass balance, the stripping rate, the ohmic polarization, and the mesh deformation demonstrated the consistency of the volumetric deformation strategy. We demonstrated in this study a deformable modeling strategy, which was found to be useful for the electrostripping analysis of anodic geometry defects during discharge. Non-uniformity in the lithium stripping rate was found along the anodic interface with defects, and this non-uniformity was accentuated with a higher discharge rate. The cell’s discharge potential was decomposed by considering the equilibrium potential and the polarizations of the main components of the cell. This post-processing was found to be useful for the understanding of the cell’s behavior.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.183
Threshold uncertainty score0.925

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 head0.011
GPT teacher head0.244
Teacher spread0.232 · 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 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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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