2021
DOI: 10.1021/acs.jpcc.1c08022
|View full text |Cite
|
Sign up to set email alerts
|

Insights from Computational Studies on the Anisotropic Volume Change of LixNiO2 at High States of Charge (x < 0.25)

Abstract: The need for high-capacity Li-ion battery cathodes has favored the increase of Ni content in commercial battery cells. However, at high states of charge (SOCs), Ni-rich materials undergo a phase transition and volume collapse with deleterious effects on battery performance. It is uncertain whether this drastic volume change is caused by the phase transition or not. To provide more insight into the volume-phase transition relationship in the high Ni cathode Li x NiO 2 , we performed density functional theory ca… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
7
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
7

Relationship

1
6

Authors

Journals

citations
Cited by 9 publications
(9 citation statements)
references
References 44 publications
0
7
0
Order By: Relevance
“…This maximum c ‐lattice parameter drops to 13.56 Å with further delithiation up to x = 0.16, resulting in c ‐lattice contraction of ≈5.1%. [ 47 ] In contrast, the maximum a ‐lattice parameter change was less than 0.01% (from 5.62 to 5.60 Å) for the same degree of delithiation. These non‐symmetrical lattice parameter changes imply anisotropic strain generation in the secondary particles that are typically comprised of randomly oriented primary particles.…”
Section: Resultsmentioning
confidence: 97%
“…This maximum c ‐lattice parameter drops to 13.56 Å with further delithiation up to x = 0.16, resulting in c ‐lattice contraction of ≈5.1%. [ 47 ] In contrast, the maximum a ‐lattice parameter change was less than 0.01% (from 5.62 to 5.60 Å) for the same degree of delithiation. These non‐symmetrical lattice parameter changes imply anisotropic strain generation in the secondary particles that are typically comprised of randomly oriented primary particles.…”
Section: Resultsmentioning
confidence: 97%
“…S3 and Table S1. LiNiO 2 undergoes a series of well-studied phase transitions upon delithation (H1 -M1 -H2 -H3), between hexagonal phases, H, which differ slightly in their lattice parameters and a monoclinic phase, M. 5,38,[40][41][42][43][44][45] Upon reaching the onset of the voltage plateau at 4.1 V vs. Li + /Li, LiNiO 2 has transitioned to the H2 phase with an expanded unit cell along the crystallographic c axis, ESI † Fig. S4 and Table S2.…”
Section: Structure Of Linio 2 Over the First Cyclementioning
confidence: 99%
“…Thirdly, constant-potential MD modeling of battery interfaces can be combined with machine learning potentials (MLPs), which would hopefully lead to fast computation as in CMD and high electronic accuracy as in AIMD at the same time. 6,7,[88][89][90][91] Currently, MLPs have already been extensively utilized in the study of bulk liquid 92,93 and solid battery materials, [94][95][96] especially to investigate their transport properties. 91,[97][98][99] Some studies have demonstrated the possibility to model interfaces with MLPs, [100][101][102][103][104][105] even though these interfaces are not held at constant potentials and may not be related to batteries.…”
Section: Conclusion and Prospectsmentioning
confidence: 99%