Zeqiu Chen , Limiao Cai , Jie Guan , Zhanyang Li , Hao Wang , Yaoguang Guo , Xingtao Xu , Likun Pan
{"title":"Advanced electrode materials in capacitive deionization for efficient lithium extraction","authors":"Zeqiu Chen , Limiao Cai , Jie Guan , Zhanyang Li , Hao Wang , Yaoguang Guo , Xingtao Xu , Likun Pan","doi":"10.1016/j.actphy.2025.100089","DOIUrl":null,"url":null,"abstract":"<div><div>Efficient technologies for lithium extraction are progressively pivotal in response to the growing requirement for lithium in new energy applications. However, due to its high energy consumption and possible secondary pollution problems, traditional lithium absorption and recovery technologies, are limited in practical application and development. Capacitive deionization (CDI) demonstrates significant potential for lithium extraction with regard to efficiency, cost-effectiveness, and energy consumption. This review commences with bibliometric analysis to dissect the key research topics of lithium extraction <em>via</em> CDI, and presents a complete synopsis of recent advances in electrode materials for lithium extraction using CDI technology, along with various types of CDI systems that utilize these materials. This study elucidates in detail the main electrode materials used in CDI systems for lithium resource recovery —— aqueous lithium ion electrode materials (including LiFePO<sub>4</sub>, LiMn<sub>2</sub>O<sub>4</sub>, LiNi<sub>1/3</sub>Co<sub>1/3</sub>Mn<sub>1/3</sub>O<sub>2</sub>) and their modification materials (including carbon nanotubes, graphene, MOFs). In addition, this paper discusses the improvement of lithium extraction efficiency through different CDI systems and evaluates the capability of various advanced electrode materials in these systems. The end of the paper emphasizes the application potential of machine learning in the domain of lithium extraction <em>via</em> CDI. The study is anticipated to deliver a strong theoretical basis and practical recommendations for advancing efficient lithium extraction systems that utilize CDI.</div></div>","PeriodicalId":6964,"journal":{"name":"物理化学学报","volume":"41 8","pages":"Article 100089"},"PeriodicalIF":10.8000,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"物理化学学报","FirstCategoryId":"92","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S1000681825000451","RegionNum":2,"RegionCategory":"化学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Efficient technologies for lithium extraction are progressively pivotal in response to the growing requirement for lithium in new energy applications. However, due to its high energy consumption and possible secondary pollution problems, traditional lithium absorption and recovery technologies, are limited in practical application and development. Capacitive deionization (CDI) demonstrates significant potential for lithium extraction with regard to efficiency, cost-effectiveness, and energy consumption. This review commences with bibliometric analysis to dissect the key research topics of lithium extraction via CDI, and presents a complete synopsis of recent advances in electrode materials for lithium extraction using CDI technology, along with various types of CDI systems that utilize these materials. This study elucidates in detail the main electrode materials used in CDI systems for lithium resource recovery —— aqueous lithium ion electrode materials (including LiFePO4, LiMn2O4, LiNi1/3Co1/3Mn1/3O2) and their modification materials (including carbon nanotubes, graphene, MOFs). In addition, this paper discusses the improvement of lithium extraction efficiency through different CDI systems and evaluates the capability of various advanced electrode materials in these systems. The end of the paper emphasizes the application potential of machine learning in the domain of lithium extraction via CDI. The study is anticipated to deliver a strong theoretical basis and practical recommendations for advancing efficient lithium extraction systems that utilize CDI.