A practical guide for separator selection, characterization, and electrochemical evaluation for supercapacitor application

分离器(采油) 超级电容器 表征(材料科学) 阳极 储能 阴极 计算机科学 材料科学 工艺工程 电容 纳米技术 电气工程 功率(物理) 工程类 化学 电极 热力学 物理化学 物理 量子力学
作者
Yaroslav Zhigalenok,Saken Abdimomyn,Kaiyrgali Zhumadil,Maxim Lepikhin,Alena A. Starodubtseva,Marzhan Kiyatova,Netanel Shpigel,Fyodor Malchik
出处
期刊:Applied physics reviews [American Institute of Physics]
卷期号:11 (3) 被引量:6
标识
DOI:10.1063/5.0202782
摘要

Supercapacitors are widely acknowledged as crucial devices for storing and converting electrical energy, alongside batteries and fuel cells. Their ability to rapidly charge and discharge, typically within seconds or even milliseconds, makes them ideal for high-power applications. This feature provides significant advantages for electric vehicles, such as regenerative braking and hill-climbing, where quick energy transfer is essential. To optimize the power performance of supercapacitor cells, it is essential to focus not only on the active material but also on the inactive components, including binders, conductive agents, and separators. The latter functions as an electronic insulating barrier between the cathode and the anode while facilitating optimal ionic transport across the cell. Therefore, particularly in high-power devices, selecting suitable separators is crucial to ensure fast charging kinetics and minimal cell resistance. Despite significant progress in developing high-power electrode materials, relatively few studies have been dedicated to membranes and their impact on the cell's electrochemical behavior. Herein, we provide a practical guide for choosing appropriate membranes for high-power supercapacitor applications. A comprehensive description of the main characterization methods for reliable evaluation of separators, alongside practical experimental examples, is given below. A special discussion is devoted to the evaluation of membrane impedance by various analytical approaches.
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
不想查文献完成签到,获得积分10
刚刚
健忘的老姆完成签到,获得积分10
2秒前
乐乐应助Doctor-C采纳,获得10
2秒前
2秒前
丁丁丁发布了新的文献求助30
2秒前
3秒前
3秒前
凌波丽发布了新的文献求助10
4秒前
夕荀发布了新的文献求助20
5秒前
6秒前
雷浩发布了新的文献求助10
7秒前
7秒前
hyyy发布了新的文献求助10
7秒前
Orange应助Zcy31098采纳,获得10
7秒前
小金完成签到,获得积分10
7秒前
从容问薇完成签到,获得积分10
7秒前
一只羊完成签到 ,获得积分10
7秒前
8秒前
任娜发布了新的文献求助10
8秒前
充电宝应助冯玉石采纳,获得10
8秒前
9秒前
灵波完成签到,获得积分20
9秒前
花无双完成签到,获得积分0
9秒前
舟舟临渊发布了新的文献求助15
9秒前
凝光发布了新的文献求助10
9秒前
9秒前
一只柠檬完成签到,获得积分20
9秒前
10秒前
10秒前
万惜文完成签到,获得积分10
10秒前
wwww发布了新的文献求助10
10秒前
乐乐应助今晚吃什么采纳,获得30
10秒前
年轻的咖啡豆完成签到,获得积分20
11秒前
个性归尘应助徐徐采纳,获得10
11秒前
snowy_owl发布了新的文献求助10
12秒前
加菲丰丰完成签到,获得积分0
12秒前
程住气完成签到 ,获得积分10
12秒前
徐佳佳完成签到,获得积分10
13秒前
丁丁丁完成签到,获得积分10
13秒前
研友_851KE8发布了新的文献求助10
13秒前
高分求助中
Thinking Small and Large 500
Algorithmic Mathematics in Machine Learning 500
Getting Published in SSCI Journals: 200+ Questions and Answers for Absolute Beginners 300
Deciphering Earth's History: the Practice of Stratigraphy 200
New Syntheses with Carbon Monoxide 200
Quanterion Automated Databook NPRD-2023 200
Interpretability and Explainability in AI Using Python 200
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 3834829
求助须知:如何正确求助?哪些是违规求助? 3377355
关于积分的说明 10497842
捐赠科研通 3096774
什么是DOI,文献DOI怎么找? 1705187
邀请新用户注册赠送积分活动 820484
科研通“疑难数据库(出版商)”最低求助积分说明 772090