Accelerating Li-ion diffusion in β-eucryptite by tuning Li–Li correlation

Author:

Niu Yinghua12,Li Wenjun12ORCID,Liu Longfei1ORCID,Nitou Modeste Venin Mendieev12,Nie Jinlan1,Mei Zongwei2,Cao Feng3,Lv Weiqiang12ORCID

Affiliation:

1. School of Physics, University of Electronic Science and Technology of China, Chengdu 611731, People's Republic of China

2. Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, People's Republic of China

3. Department of Engineering Technology, Huzhou College, Huzhou 313000, People's Republic of China

Abstract

Solid-state Li-ion batteries are emerging as promising next-generation energy storage devices, but new solid-state Li-ion conductors or electrolytes, a critical component of such devices, are highly demanded to meet the conductivity and stability requirements. In this study, one of the cost-effective and stable silicate-based solid Li-ion conductors, β-eucryptite LiAlSiO4, was studied via ab initio molecular dynamics simulations. The Si/Al ratio from 0 to 7 corresponding to x in Li1+ xAl1+ xSi1- xO4 from 1 (Li-rich) to −0.75 (Li-poor) was adjusted to investigate its impact on Li-ion diffusion. The results show that the Li-ion diffusion barrier can be greatly decreased from 0.61 eV in β-eucryptite LiAlSiO4 ( x = 0) to 0.20 eV in Li0.5Al0.5Si1.5O4 ( x = −0.5; Si/Al = 3) and 0.24 eV in Li1.25Al1.25Si0.75O4 ( x = 0.25; Si/Al = 0.6). The predicted Li-ion conductivity is 6.976 mS/cm in Li0.5Al0.5Si1.5O4 and 3.773 mS/cm in Li1.25Al1.25Si0.75O4 at 25 °C, both allowing room-temperature operation of solid-state batteries. The modulation of Li–Li correlation at these two distinctive Si/Al ratios results in significantly lower diffusion barrier and higher Li-ion conductivity than those of the parent composition. Our work facilitates the design of low-cost silicate-based Li-ion conductors with high Li conductivity.

Funder

National Natural Science Foundation of China

Sichuan-Hong Kong Collaborative Research Fund

The Foundation of YangtzeDelta Region Institute (Huzhou) of University of Electronic Science and Technology of China

Publisher

AIP Publishing

Subject

Physics and Astronomy (miscellaneous)

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Machine-learned potentials for eucryptite: A systematic comparison;Journal of Materials Research;2023-10-12

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