One-dimensional electrospinning nanomaterials toward capacitive deionization: Fundamentals, development, and perspectives

Yang, Zhiqian; Gao, Ming; Liang, Wencui; Ao, Tianqi; Chen, Wenqing

doi:10.1016/j.desal.2023.117010

Cited by 10 publications

(1 citation statement)

References 201 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional approaches to electrode optimization often involve extensive experimental trials, which can be timeconsuming, resource-intensive, and limited in scope. Integrating Artificial Intelligence (AI) [21][22][23][24][25][26][27][28] and machine learning [27][28][29][30][31][32][33][34] techniques in many technologies, especially in this contrast provides a powerful and efficient means to explore the vast design space of CDI electrodes, predict optimal configurations, and accelerate the development of high-performance materials [35][36][37][38][39][40][41][42][43][44][45][46][47][48]. This research uses AI-based modeling methodologies to investigate and optimize CDI electrode's key features systematically.…”

Section: Introductionmentioning

confidence: 99%

Optimization of capacitive deionization electrode features and materials using artificial intelligence-based modeling

Khalil,

AlShabi,

Abdelrazek

et al. 2024

Energy Harvesting and Storage: Materials, Devices, and Applications XIV

View full text Add to dashboard Cite

Capacitive deionization (CDI) is an emerging technique for removing dissolved, charged species from aqueous solutions. It has been previously applied to brackish water and seawater desalination, wastewater remediation, and water softening. The CDI unit cell comprises two parallel electrode sheets separated by a non-conductive spacer (nylon cloth, 100 mm thick) and fixed with a rubber gasket. The electrodes are typically carbon, and the feed water flows between or through the two charged electrodes. The porous electrode pair is accused of an applied voltage difference (called the cell or charging voltage). Optimizing the CDI electrode features is essential for scaling up the technique to an industrial scale. The effect of the water flow rate and the applied voltage are key factors that affect the efficiency of the CDI units. This research used Artificial Intelligence (AI) as a smart-based modeling tool to optimize and predict the highest efficiency concerning the electrode and process parameters. The results showed that a carbon-based structure with super-electrochemical and mechanical properties could revolutionize CDI technology.

show abstract