Engineering cellulose-based hybrid materials for radionuclide sequestration: coordination mechanisms, challenges, and machine learning perspectives

Key Points

• Radionuclide sequestration efficiency is influenced by cellulose-based hybrid materials, indicating significant potential for environmental applications.
• Machine learning techniques can enhance the design of these materials by predicting their performance in radionuclide sequestering scenarios.
• Coordination mechanisms play a crucial role in how effectively these hybrid materials interact with radionuclides in various environments.
• This work emphasizes the importance of developing reliable models for improving material efficiency and safety in waste management.