Selective Ion Separation by Capacitive Deionization: A Comprehensive Review
Abstract
:1. Introduction
2. Desalination Method
2.1. Pressure-Driven Desalination
2.2. Thermal Desalination
2.3. Electrokinetic Desalination
3. CDI Evolution and Working Principles
3.1. Evolution of CDI
3.2. Working Principles of CDI
3.2.1. Electro-Absorption Principle
3.2.2. Advanced Characterization Techniques for Understanding Electrosorption
3.2.3. Faraday’s Principle of Ion Removal
3.2.4. Advanced Characterization Techniques for Understanding Faradaic Storage
4. Performance Indicators and Electrode Materials
4.1. Performance Indicators
4.2. Electrode Materials
4.2.1. Capacitor Materials
4.2.2. Pseudo-Capacitor Materials
5. Ion-Selective Membrane
5.1. Cation-Exchange Membrane
5.2. Anion-Exchange Membrane
5.3. Ion-Exchange Resin Coating
6. Problems Faced and Solutions
6.1. Water Oxidation
6.2. Dissolved Oxygen Reduction
6.3. Electrode Fouling
7. Outlook
- Material discovery and design: The discovery of new materials can be accelerated through machine learning algorithms. For example, training models to predict the properties of different material combinations, such as capacitance values, conductivity, stability, etc., can help researchers find new materials with excellent properties. Machine learning can also be used to optimize the microstructure of materials, such as the pore distribution of porous carbon materials, to achieve higher specific capacitance.
- Manufacturing optimization: In the manufacturing process of capacitors, machine learning can be used to optimize process parameters such as temperature, pressure, reaction time, etc., to improve product quality and consistency. Three-dimensional printing technology combined with machine learning can enable a customized design of the internal structure to make them more suitable for specific application scenarios.
- Performance prediction and modeling: Based on historical experimental data, the machine learning model can predict the performance of capacitors under different conditions, such as temperature changes, the charge/discharge rate, and other factors on their life and performance. It can also help engineers to consider the impact of these factors at the design stage.
- Fault detection and diagnosis: Machine learning can be used to monitor the working status of capacitors, provide early warning of potential failures, extend service life, and reduce maintenance costs.
- Intelligent control of capacitors: In some application scenarios, such as power systems or battery management systems for electric vehicles, machine learning algorithms can be used to optimize the charging and discharging strategies of capacitors for optimal energy management. For example, researchers at Georgia Tech used supercomputers and machine learning techniques to accelerate the analysis of electronic materials, which helped them find ways to improve capacitors faster. Meanwhile, research has shown that a combination of 3D printing technology and machine learning can produce carbon micro-lattices with customized properties that can be used as energy storage elements for supercapacitors. These applications demonstrate the potential of machine learning to enhance the development, production, and application of capacitors, helping to advance capacitor technology.
Author Contributions
Funding
Conflicts of Interest
References
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Xu, F.; Yuan, L.; Zhao, R.; Qin, B.; Zhang, F.; Ren, L.; Yang, H.; Yuan, M. Selective Ion Separation by Capacitive Deionization: A Comprehensive Review. Materials 2025, 18, 1107. https://doi.org/10.3390/ma18051107
Xu F, Yuan L, Zhao R, Qin B, Zhang F, Ren L, Yang H, Yuan M. Selective Ion Separation by Capacitive Deionization: A Comprehensive Review. Materials. 2025; 18(5):1107. https://doi.org/10.3390/ma18051107
Chicago/Turabian StyleXu, Fanyi, Ling Yuan, Rui Zhao, Bing Qin, Feng Zhang, Liming Ren, Hailun Yang, and Menglei Yuan. 2025. "Selective Ion Separation by Capacitive Deionization: A Comprehensive Review" Materials 18, no. 5: 1107. https://doi.org/10.3390/ma18051107
APA StyleXu, F., Yuan, L., Zhao, R., Qin, B., Zhang, F., Ren, L., Yang, H., & Yuan, M. (2025). Selective Ion Separation by Capacitive Deionization: A Comprehensive Review. Materials, 18(5), 1107. https://doi.org/10.3390/ma18051107