Fast Li-ion conduction enabled by graphite fluoride flakes in solid polymer electrolyte

Li, Qing-Hui; Wang, Xiao-Fen; Zhu, Shi-Yuan; Zhong, Qing-Dong; Liu, Song-Li; Zhou, Qiong-Yu

doi:10.1007/s12598-023-02375-0

Cited by 5 publications

(1 citation statement)

References 39 publications

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“…Therefore, the incorporation of flame-retardant additives into electrolytes offers a promising approach to achieving fast charging while maintaining non-flammability and cost-effectiveness. Notably, commonly used flame-retardant additives such as trimethyl phosphate 9 and triethyl phosphate, 34–36 are incompatible with graphite anodes. 37,38 Hence, flame-retardant additives that effectively inhibit the combustion of organic electrolytes while ensuring excellent compatibility with electrode materials and current collectors, as well as promoting Li + transport are required.…”

Section: Introductionmentioning

confidence: 99%

A multi-functional electrolyte additive for fast-charging and flame-retardant lithium-ion batteries

Long,

Huang,

Miao

et al. 2024

J. Mater. Chem. A

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Section: Introductionmentioning

confidence: 99%

A multi-functional electrolyte additive for fast-charging and flame-retardant lithium-ion batteries

Long,

Huang,

Miao

et al. 2024

J. Mater. Chem. A

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Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

Li,

Zhou,

et al. 2024

Advanced Energy Materials

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Machine learning (ML) exhibits substantial potential for predicting the properties of solid‐state electrolytes (SSEs). By integrating experimental or/and simulation data within ML frameworks, the discovery and development of advanced SSEs can be accelerated, ultimately facilitating their application in high‐end energy storage systems. This review commences with an introduction to the background of SSEs, including their explicit definition, comprehensive classification, intrinsic physical/chemical properties, underlying mechanisms governing their conductivity, challenges, and future developments. An in‐depth explanation of the ML methodology is also elucidated. Subsequently, the key factors that influence the performance of SSEs are summarized, including thermal expansion, modulus, diffusivity, ionic conductivity, reaction energy, migration barrier, band gap, and activation energy. Finally, it is offered perspectives on the design prerequisites for upcoming generations of SSEs, focusing on real‐time property prediction, multi‐property optimization, multiscale modeling, transfer learning, automation and high‐throughput experimentation, and synergistic optimization of full battery, all of which are crucial for accelerating the progress in SSEs. This review aims to guide the design and optimization of novel SSE materials for the practical realization of efficient and reliable SSEs in energy storage technologies.

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Transparent PVDF-based electrolyte enabled by lipophilic lithium magnesium silicate for solid-state lithium batteries

Zhang,

Li,

et al. 2024

Rare Met.

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Fast Li-ion conduction enabled by graphite fluoride flakes in solid polymer electrolyte

Cited by 5 publications

References 39 publications

A multi-functional electrolyte additive for fast-charging and flame-retardant lithium-ion batteries

A multi-functional electrolyte additive for fast-charging and flame-retardant lithium-ion batteries

Machine Learning‐Assisted Property Prediction of Solid‐State Electrolyte

Transparent PVDF-based electrolyte enabled by lipophilic lithium magnesium silicate for solid-state lithium batteries

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