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Record W4386853297 · doi:10.1149/ma2023-01452483mtgabs

Li Transport in Defected LiNiO<sub>2</sub> from First Principles: Diffusion, Nucleation, and Charge Transfer

2023· article· en· W4386853297 on OpenAlexaff
Penghao Xiao

Bibliographic record

VenueECS Meeting Abstracts · 2023
Typearticle
Languageen
FieldEngineering
TopicAdvancements in Battery Materials
Canadian institutionsDalhousie University
Fundersnot available
KeywordsKinetic Monte CarloNucleationThermodynamicsElectrochemistryMaterials scienceKinetic energyDiffusionElectrolyteCluster expansionDensity functional theoryChemical physicsChemistryElectrodeMonte Carlo methodComputational chemistryPhysical chemistryPhysics

Abstract

fetched live from OpenAlex

Electrochemical intercalation of Li in battery electrodes involves a few types of kinetic processes, including diffusion in inhomogeneous media, phase nucleation/transition, and surface charge transfer. They span several length and time scales and are often coupled with each other, which makes it challenging to identify the rate-limiting step and extract kinetic parameters by fitting an empirical model. Using layered cathode LiNiO2 as an example, I will present how all these processes can be incorporated in one atomistic model without empirical inputs, which reals the kinetic competition between different processes. In this scheme, the energetics of various local environments were calculated by the density functional theory (DFT). And the obtained data were used to train a surrogate Hamiltonian with the cluster expansion method. Then the kinetic Monte Carlo (KMC) simulations were conducted with ion hopping barriers updated on-the-fly based on the Brønsted−Evans−Polanyi (BEP) relation. The charge transfer barriers at the electrode-electrolyte interface were calculated separately using the constant-voltage eNEB method1. The simulation results shown below largely reproduce the experimental voltage profile of LiNiO2 2. Both the hysteresis and the irreversible capacity loss (IRC) at the first discharge are captured. The rate limiting step switches as the Li concentration changes during cycling. Moreover, the layered LiNiO2 is never perfect and there are always some excess Ni in the Li layer, which significantly affects the electrochemical performances. My simulations peek into the atomistic origins of these effects. The excess Ni formed in the bulk during synthesis increase IRC by impeding Li diffusion3; the excess Ni near the surface formed during cycling due to oxygen loss traps Li in the fatigued particles by suppressing Li-poor phase nucleation below certain Li concentration4. References: Duan, Z. & Xiao, P. Simulation of Potential-Dependent Activation Energies in Electrocatalysis: Mechanism of O-O Bond Formation on RuO2. Journal of Physical Chemistry C 125, 15243–15250 (2021). Kurzhals, P. et al. The LiNiO2 Cathode Active Material: A Comprehensive Study of Calcination Conditions and their Correlation with Physicochemical Properties. Part I. Structural Chemistry. J Electrochem Soc 168, 110518 (2021). Phattharasupakun, N. et al. Correlating Cation Mixing with Li Kinetics: Electrochemical and Li Diffusion Measurements on Li-Deficient LiNiO2 and Li-Excess LiNi0.5Mn0.5O2. J Electrochem Soc 168, 090535 (2021). Xu, C. et al. Bulk fatigue induced by surface reconstruction in layered Ni-rich cathodes for Li-ion batteries. Nat Mater 20, 84–92 (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 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)0.000
metaresearch head score (Gemma)0.000
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: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.015
GPT teacher head0.215
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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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