Acta Phys. -Chim. Sin. ›› 2025, Vol. 41 ›› Issue (8): 100089.doi: 10.1016/j.actphy.2025.100089
Special Issue: Electrochemical Separation and Recycling
• REVIEW • Previous Articles Next Articles
Zeqiu Chen1, Limiao Cai1, Jie Guan1, Zhanyang Li1, Hao Wang2, Yaoguang Guo1,*(
), Xingtao Xu3,*(
), Likun Pan2,4,*(
)
Received:2025-01-17
Revised:2025-04-03
Accepted:2025-04-07
Published:2025-06-07
Contact:
Email: ygguo@sspu.edu.cn (Yaoguang Guo)lkpan@phy.ecnu.edu.cn (Likun Pan)xingtao.xu@zjou.edu.cn (Xingtao Xu)
Supported by:Zeqiu Chen, Limiao Cai, Jie Guan, Zhanyang Li, Hao Wang, Yaoguang Guo, Xingtao Xu, Likun Pan. Advanced electrode materials in capacitive deionization for efficient lithium extraction[J]. Acta Phys. -Chim. Sin. 2025, 41(8), 100089. doi: 10.1016/j.actphy.2025.100089
Table 1
The range of values of the indicator."
| Indicator | Range of values |
| TOP N | No concrete limitations |
| Q | Q ranges from 0 to 1 Q > 0.3 indicates a distinct and cohesive network community structure |
| S | S ranges from 0 to 1 S > 0.5 suggests reasonable clustering validity S > 0.7 demonstrates high reliability |
Fig 2
(a) Ionic and hydration radius derived from alkali and alkaline earth metal elements [71]. Copyright 2020, Cell Press. (b) Principal electrode materials for aqueous lithium-ion batteries [72]. Copyright 2010, Springer Nature. (c) LiFePO4, LiMn2O4, LiNi1/3Co1/3Mn1/3O2 structure diagram (left to right)."
Table 2
Different capacitive deionization systems for lithium extraction."
| Methods | Cathode | Anode | Adsorption capacity | Separation factor | Selectivity factor | Energy consumption | Ref. |
| MCDI | LMO/LAO | Active carbon | 900 μmol·g−1 | 0.67 kWh∙m−3 | [ | ||
| LMO | Active carbon | 0.35 μmol·g−1 | 23.3 Wh∙g−1 | [ | |||
| Active carbon | Active carbon | SLi/Mg = 2.95 | 0.0018 kWh∙mol−1 | [ | |||
| HCDI | LMO/GO | Active carbon | 720.2 μmol·g−1 | [ | |||
| GO/La-LMO | Active carbon | 1.33 mmol·g−1 | [ | ||||
| CNTs-LMO | Active carbon | 11.11 mg·g−1 | 3.6 Wh∙mol−1 | [ | |||
| λ-MnO2 | Active carbon | 18.1 mg·g−1 | 0.99 Wh∙mol−1 | [ | |||
| rGO/LMO | Active carbon | 4.34 mmol·g−1 | [ | ||||
| LMTO | Active carbon (AEM) | 28.6 mg·g−1 | 1.54–12.10 Wh∙mol−1 | [ | |||
| FCDI | Nanoporous Active carbon (CEM) | Nanoporous Active carbon (AEM) | [ | ||||
| (CEM) | (AEM) | SLi/Na = 141 ± 5.85 SLi/K = 46 ± 1.46 | 16.70 ± 1.63 kWh∙kg−1 | [ | |||
| RCDI | Li1-xMn2O4 | LiMn2O4 | 18 mg·g−1 | FMg/Li from 147.8 to 0.37 | SLi/Na = 5.94 SLi/K = 9.16 SLi/Mg = 42.95 SLi/Al = 73.93 | [ | |
| LiMn2O4 | Li1-xMn2O4 | 34.69 mg·g−1 | 6.76 Wh∙mol−1 | [ | |||
| FePO4 | LiFePO4 | 32 mg·g−1 | FMg/Li from 48.4 to 0.5 | [ | |||
| NaFePO4 | FePO4 | SLi/Na = 18000 | [ |
Fig 4
(a) Schematic illustration of desalination via CDI module. (b) Selective recovery process of lithium ions in MCDI system [109]. Copyright 2025, Elsevier. (c) Experimental setup and reaction mechanism of the MSCDI system [110]. (d) Selection coefficients and the removal of Li+ and Mg2+ in tests with various CDI systems [110]. (e) Performance of MSCDI modules in separation with varying Mg/Li feed ratios [110]. Copyright 2019, Elsevier."
Fig 5
(a) Preparation of three-dimensional graphene oxide/lanthanum-doped lithium manganese oxide (3D GO/La-LMO) composites 112. (b) Lithium adsorption capacity and cycling stability of GO/La-LMO in CDI batteries 112. Copyright 2024, Elsevier. (c) Schematic diagram of rGO/LMO electrode materials and cycling stability 114. Copyright 2024, Elsevier. (d) Separation factor of Li+/Mg2+ in the HCDI system 115. Copyright 2022, Elsevier."
Fig 7
(a) Novel lithium-ion battery configuration was developed based on RCDI, comprising 'LiMn2O4 (anode)|supporting electrolyte|anionic membrane|brine|Li1−xMn2O4 (cathode, 0 < x < 1)' [118]. (b) Lithium recovery in RCDI system [118]. (c) Capacity stability after 100 cycles [118]. Copyright 2021, Elsevier."
Fig 8
(a) Feature selection for the MCDI long-term cycle time prediction model [144]. (b) Feature selection to optimize the operating parameters of the MCDI model [144]. Copyright 2022, Elsevier. (c) Basic algorithmic principles of different machine learning models, including output and predictor variables [146]. Copyright 2024, American Chemical Society."
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