Decoupling functional group effects on uranium adsorption in UiO-66 through density functional theory and neural...

Area of Science:

• Materials Science
• Nuclear Chemistry
• Environmental Engineering

Background:

• Seawater uranium offers a sustainable nuclear energy resource.
• Terrestrial uranium reserves are diminishing, increasing reliance on seawater extraction.
• High-performance adsorbents are crucial for efficient and selective uranium recovery.

Purpose of the Study:

• To synthesize and evaluate functionalized UiO-66 materials for U(VI) adsorption from seawater.
• To investigate the impact of various functional groups on adsorption capacity and selectivity.
• To elucidate the adsorption mechanism using spectroscopic and computational methods.

Main Methods:

• Synthesis of pristine and functionalized UiO-66 (2X-UiO-66, X = H, OH, NH2, SH, AO).
• Uranium adsorption experiments and selectivity tests in the presence of competing ions.
• Characterization using X-ray photoelectron spectroscopy (XPS) and density functional theory (DFT) calculations.
• Machine learning modeling (Stacking ensemble and SHAP analysis) for adsorption prediction and feature importance.

Main Results:

• 2AO-UiO-66 demonstrated the highest U(VI) adsorption capacity (344.5 mg/g) and selectivity.
• Amidoxime functionalization enabled N,O dual-site synergistic chelation for enhanced U(VI) binding.
• Machine learning models accurately predicted U(VI) adsorption capacity, with contact time and pore size as key factors.

Conclusions:

• Amidoxime-functionalized UiO-66 is a highly effective adsorbent for seawater uranium extraction.
• The study provides a framework combining experimental, computational, and machine learning approaches.
• Findings guide the rational design of advanced adsorbents for sustainable nuclear fuel resources.