Strategies for formulation optimization of composite positive electrodes for lithium ion batteries based on layered oxide, spinel, and olivine-type active materials

材料科学 复合数 电极 复合材料 锂(药物) 尖晶石 化学工程 冶金 化学 医学 物理化学 工程类 内分泌学
作者
Anna Weichert,Vinzenz Göken,Olga Fromm,Thomas Beuse,Martin Winter,Markus Börner
出处
期刊:Journal of Power Sources [Elsevier BV]
卷期号:551: 232179-232179 被引量:11
标识
DOI:10.1016/j.jpowsour.2022.232179
摘要

Electrode processing and performance strongly depend on the active material. Maximizing the active material content of positive composite electrodes enables low cost and high energy density. However, this maximization cannot reach 100%, as composite electrodes additionally consist of binder to provide mechanical integrity and conductive additive to enhance electronic conductivity, which in combination create a flexible porous microstructure for appropriate electron and lithium transport. In this study, the influence of three positive active material classes, layered oxide LiNi0.6Mn0.2Co0.2O2, spinel-type LiMn2O4 and olivine-type carbon-coated LiFePO4, were investigated regarding the optimum amount of polyvinylidene difluoride as binder and carbon black as conductive additive to achieve high mechanical stability as well as high electronic and ionic conductivity within composite electrodes. Formulation optimization was conducted and compared to a reference electrode formulation with regard to physical, mechanical, electronic and electrochemical properties. In a first step, the binder amount was optimized for each active material class by varying the ratio of binder content to surface area of the solid electrode components. In a second step, the critical conductive additive content was determined. Overall, this strategy allows to decipher material class dependent optimized electrode formulations for high energy density composite electrodes with maximized active material content.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
2秒前
2秒前
健壮保温杯完成签到,获得积分10
3秒前
michellewu完成签到 ,获得积分10
6秒前
wwwwppp发布了新的文献求助10
6秒前
Nostalgia完成签到,获得积分10
6秒前
宝贝蛋完成签到,获得积分10
6秒前
瓜子完成签到,获得积分10
9秒前
9秒前
英俊的铭应助科研通管家采纳,获得10
10秒前
CipherSage应助科研通管家采纳,获得10
10秒前
Estrella应助科研通管家采纳,获得10
10秒前
汉堡包应助科研通管家采纳,获得10
10秒前
深情安青应助科研通管家采纳,获得10
10秒前
星辰大海应助科研通管家采纳,获得10
10秒前
科研通AI2S应助科研通管家采纳,获得10
11秒前
共享精神应助科研通管家采纳,获得10
11秒前
11秒前
传奇3应助科研通管家采纳,获得10
11秒前
Lucas应助共产主义接班人采纳,获得10
11秒前
标致的初之完成签到,获得积分10
13秒前
13秒前
15秒前
wangminting完成签到 ,获得积分10
16秒前
Nimeide完成签到,获得积分10
17秒前
小青虫发布了新的文献求助10
17秒前
18秒前
NexusExplorer应助liuyuh采纳,获得10
19秒前
MoMo发布了新的文献求助10
20秒前
wangminting关注了科研通微信公众号
20秒前
dddyrrrrr完成签到 ,获得积分10
21秒前
SciGPT应助allucky采纳,获得10
21秒前
Owen应助zyj采纳,获得10
23秒前
24秒前
25秒前
汪格森发布了新的文献求助10
27秒前
CipherSage应助QQ采纳,获得10
28秒前
好好发布了新的文献求助10
28秒前
28秒前
30秒前
高分求助中
All the Birds of the World 4000
Production Logging: Theoretical and Interpretive Elements 3000
Les Mantodea de Guyane Insecta, Polyneoptera 2000
Machine Learning Methods in Geoscience 1000
Resilience of a Nation: A History of the Military in Rwanda 888
Musculoskeletal Pain - Market Insight, Epidemiology And Market Forecast - 2034 666
Crystal Nonlinear Optics: with SNLO examples (Second Edition) 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 物理 生物化学 纳米技术 计算机科学 化学工程 内科学 复合材料 物理化学 电极 遗传学 量子力学 基因 冶金 催化作用
热门帖子
关注 科研通微信公众号,转发送积分 3735903
求助须知:如何正确求助?哪些是违规求助? 3279592
关于积分的说明 10016324
捐赠科研通 2996292
什么是DOI,文献DOI怎么找? 1644012
邀请新用户注册赠送积分活动 781709
科研通“疑难数据库(出版商)”最低求助积分说明 749425