Bifunctional additive phenyl vinyl sulfone for boosting cyclability of lithium metal batteries

IF 9.1 Q1 ENGINEERING, CHEMICAL Green Chemical Engineering Pub Date : 2023-03-01 DOI:10.1016/j.gce.2022.03.002
Xiaoyan Zhang , Juyan Zhang , Mengmin Jia , Linshan Peng , Nana Zhang , Suitao Qi , Lan Zhang
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引用次数: 2

Abstract

One of the bottlenecks limiting the cycling stability of high voltage lithium metal batteries (LMBs) is the lack of suitable electrolytes. Herein, phenyl vinyl sulfone (PVS) is proposed as a multifunctional additive to stabilize both cathode and anode interfaces as it can be preferentially oxidized/reduced on the electrode surfaces. The PVS derived solid electrolyte interphase films can not only reduce the transition metal dissolution on the cathode side, but also suppress the Li dendrite spread on the lithium anode side. The Li||Li symmetric battery with PVS addition delivers longer cycle life and a higher critical current density of over 3.0 mAh cm−2. The LiNi0.8Co0.1Mn0.1O2 (NCM811)||Li full cell exhibits excellent capacity retention of 80.8% or 80.0% after 400 cycles at 0.5 C or 1 C rate with the voltage range of 3.0–4.3 V. In particular, the NCM811||Li cell under constrained conditions remains operation over 150 cycles. This work offers new insights into the electrolyte formulations for the next generation of LMBs.

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提高锂金属电池循环性能的双功能添加剂苯基乙烯基砜
限制高压锂金属电池(LMB)循环稳定性的瓶颈之一是缺乏合适的电解质。本文提出苯基乙烯基砜(PVS)作为一种多功能添加剂来稳定阴极和阳极界面,因为它可以在电极表面上优先氧化/还原。PVS衍生的固体电解质界面膜不仅可以减少过渡金属在阴极侧的溶解,还可以抑制Li枝晶在锂阳极侧的扩散。添加PVS的Li||Li对称电池具有更长的循环寿命和更高的临界电流密度,超过3.0 mAh cm−2。LiNi0.8Co0.1Mn0.1O2(NCM811)||Li全电池在电压范围为3.0–4.3V的0.5C或1C速率下400次循环后表现出80.8%或80.0%的优异容量保持率。特别是,NCM811||Li电池在受限条件下保持150次循环的运行。这项工作为下一代LMB的电解质配方提供了新的见解。
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来源期刊
Green Chemical Engineering
Green Chemical Engineering Process Chemistry and Technology, Catalysis, Filtration and Separation
CiteScore
11.60
自引率
0.00%
发文量
58
审稿时长
51 days
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OFC: Outside Front Cover Outside Back Cover Outside Back Cover OFC: Outside Front Cover Integration of physical information and reaction mechanism data for surrogate prediction model and multi-objective optimization of glycolic acid production
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