Recent research advances of self-discharge in supercapacitors: Mechanisms and suppressing strategies
Authors:
- Kunlun LiuKLKunlun Liu
- Chang YuCYChang Yu
- Wei GuoWGWei Guo
- Lin NiLNLin Ni
- Jinhe YuJYJinhe Yu
- Yuanyang XieYXYuanyang Xie
- Zhao WangZWZhao Wang
- Yongwen RenYRYongwen Ren
- Jieshan QiuJQJieshan Qiu
- 1 July 2021
- journal article
- research article
- Published by Elsevier in Journal of Energy Chemistry
- Vol. 58, 94-109
Abstract
No abstract availableKeywords
Funding Information
- National Key Research and Development Program of China (2016YFB0101201)
- Fundamental Research Funds for the Central Universities (DUT19LAB20)
- Dalian University of Technology
- National Natural Science Foundation of China (51872035)
- National Natural Science Foundation of China (XLYC1807002)
This publication has 121 references indexed in Scilit:
- The effect of the HClO4 oxidization of petroleum coke on the properties of the resulting activated carbon for use in supercapacitorsCarbon, 2013
- Supercapacitor modified with methylene blue as redox active electrolyteElectrochimica Acta, 2012
- 3D carbon based nanostructures for advanced supercapacitorsEnergy & Environmental Science, 2012
- Using eggshell membrane as a separator in supercapacitorJournal of Power Sources, 2012
- A review of electrode materials for electrochemical supercapacitorsChemical Society Reviews, 2012
- Examination of Water Electrolysis and Oxygen Reduction As Self-Discharge Mechanisms for Carbon-Based, Aqueous Electrolyte Electrochemical CapacitorsThe Journal of Physical Chemistry C, 2011
- Detailed analysis of the self-discharge of supercapacitorsJournal of Power Sources, 2011
- Pore Shape Affects Spontaneous Charge Redistribution in Small PoresThe Journal of Physical Chemistry C, 2010
- Electrochemical Performance of Nickel Hydroxide/Activated Carbon Supercapacitors Using a Modified Polyvinyl Alcohol Based Alkaline Polymer ElectrolyteChinese Journal of Chemical Engineering, 2009
- Artificial neural network simulator for supercapacitor performance predictionComputational Materials Science, 2007