Recent Advances in First Principles Computational Research of Cathode Materials for Lithium-Ion Batteries

锂(药物) 阴极 材料科学 纳米技术 离子 工程物理 化学 物理化学 有机化学 生物 物理 内分泌学
作者
Ying Shirley Meng,M. Elena Arroyo-de Dompablo
出处
期刊:Accounts of Chemical Research [American Chemical Society]
卷期号:46 (5): 1171-1180 被引量:145
标识
DOI:10.1021/ar2002396
摘要

To meet the increasing demands of energy storage, particularly for transportation applications such as plug-in hybrid electric vehicles, researchers will need to develop improved lithium-ion battery electrode materials that exhibit high energy density, high power, better safety, and longer cycle life. The acceleration of materials discovery, synthesis, and optimization will benefit from the combination of both experimental and computational methods. First principles (ab Initio) computational methods have been widely used in materials science and can play an important role in accelerating the development and optimization of new energy storage materials. These methods can prescreen previously unknown compounds and can explain complex phenomena observed with these compounds. Intercalation compounds, where Li(+) ions insert into the host structure without causing significant rearrangement of the original structure, have served as the workhorse for lithium ion rechargeable battery electrodes. Intercalation compounds will also facilitate the development of new battery chemistries such as sodium-ion batteries. During the electrochemical discharge reaction process, the intercalating species travel from the negative to the positive electrode, driving the transition metal ion in the positive electrode to a lower oxidation state, which delivers useful current. Many materials properties change as a function of the intercalating species concentrations (at different state of charge). Therefore, researchers will need to understand and control these dynamic changes to optimize the electrochemical performance of the cell. In this Account, we focus on first-principles computational investigations toward understanding, controlling, and improving the intrinsic properties of five well known high energy density Li intercalation electrode materials: layered oxides (LiMO2), spinel oxides (LiM2O4), olivine phosphates (LiMPO4), silicates-Li2MSiO4, and the tavorite-LiM(XO4)F (M = 3d transition metal elements). For these five classes of materials, we describe the crystal structures, the redox potentials, the ion mobilities, the possible phase transformation mechanisms, and structural stability changes, and the relevance of these properties to the development of high-energy, high-power, low-cost electrochemical systems. These results demonstrate the importance of computational tools in real-world materials development, to optimize or minimize experimental synthesis and testing, and to predict a material's performance under diverse conditions.
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
刚刚
刚刚
共享精神应助Zosty采纳,获得10
刚刚
猪米妮发布了新的文献求助10
1秒前
香蕉觅云应助zhangxl123采纳,获得10
1秒前
酷波er应助13333采纳,获得10
1秒前
zza应助小太阳采纳,获得10
2秒前
2秒前
守护发布了新的文献求助10
2秒前
张牧之完成签到 ,获得积分10
3秒前
多情的寻真完成签到,获得积分10
3秒前
3秒前
15940203654完成签到 ,获得积分10
3秒前
Xc完成签到,获得积分10
4秒前
LaLune发布了新的文献求助10
4秒前
传奇3应助第七个星球采纳,获得10
4秒前
4秒前
4秒前
123发布了新的文献求助10
4秒前
哈哈完成签到 ,获得积分10
4秒前
无极微光应助vidgers采纳,获得20
5秒前
二氧化碳发布了新的文献求助20
5秒前
5秒前
5秒前
6秒前
等待的小白菜完成签到,获得积分20
6秒前
111发布了新的文献求助50
7秒前
7秒前
ding应助昭昭如愿采纳,获得10
7秒前
orixero应助科研通管家采纳,获得10
7秒前
Orange应助爱听歌的蘑菇采纳,获得10
7秒前
Momomo应助科研通管家采纳,获得10
7秒前
量子星尘发布了新的文献求助10
7秒前
NexusExplorer应助科研通管家采纳,获得10
7秒前
慕青应助科研通管家采纳,获得10
8秒前
科研通AI6应助科研通管家采纳,获得10
8秒前
小蘑菇应助科研通管家采纳,获得10
8秒前
guozizi应助科研通管家采纳,获得20
8秒前
mumiaona完成签到,获得积分10
8秒前
orixero应助科研通管家采纳,获得10
8秒前
高分求助中
2025-2031全球及中国金刚石触媒粉行业研究及十五五规划分析报告 12000
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
The Cambridge History of China: Volume 4, Sui and T'ang China, 589–906 AD, Part Two 1000
The Composition and Relative Chronology of Dynasties 16 and 17 in Egypt 1000
Russian Foreign Policy: Change and Continuity 800
Real World Research, 5th Edition 800
Qualitative Data Analysis with NVivo By Jenine Beekhuyzen, Pat Bazeley · 2024 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 计算机科学 有机化学 物理 生物化学 纳米技术 复合材料 内科学 化学工程 人工智能 催化作用 遗传学 数学 基因 量子力学 物理化学
热门帖子
关注 科研通微信公众号,转发送积分 5699679
求助须知:如何正确求助?哪些是违规求助? 5132628
关于积分的说明 15227678
捐赠科研通 4854695
什么是DOI,文献DOI怎么找? 2604865
邀请新用户注册赠送积分活动 1556246
关于科研通互助平台的介绍 1514444