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Prof. Zhongshan Chen

School of Chemistry and Chemical Engineering, Harbin Institute of Technology

Research Publications & English Decoded Briefs

Showing 2 publications
SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-3919-0

From Bench to Buoy: Challenges in Seawater Uranium Extraction

Nuclear energy is critical for sustainable economic development and achieving carbon neutrality. With only about 6.14 million tons of terrestrial uranium, sufficient for ~70 years of global nuclear power plant operation, the recovery of uranium from seawater and spent fuel is essential for long-term nuclear fuel supply. The ocean contains approximately 4.5 billion tons of uranium, which could sustain nuclear power for ~2000 years if efficiently extracted. However, seawater uranium extraction faces significant challenges due to the extremely low uranium concentration (~3.3 ppb), high concentrations of competing ions, natural organic matter, and marine biofouling. This perspective reviews representative laboratory advances, including sulfonated covalent organic frameworks (S-COF) achieving a sorption capacity of 31.5 mg/(g·day) with high selectivity, amidoxime-based organic cages with a capacity of 11.97 mg/g over 30 days, and a micro-redox reactor strategy that continuously regenerates binding sites. Electrochemical methods have also shown promise for converting soluble U(VI) to insoluble U(IV) oxides. Despite these advances, the transition from laboratory powders to durable marine materials remains problematic. Key gaps include the need for antibacterial properties, mechanical stability under wave action, cost competitiveness with terrestrial mining, and environmental safety of nanomaterials. Artificial intelligence (AI) is proposed to accelerate the design of high-performance, stable materials. This perspective emphasizes the necessity for interdisciplinary research to bridge the gap between bench-scale innovations and practical ocean deployment.

SCIENCE CHINA Materials2026DOI: 10.1007/s40843-025-4104-4

COFAP: A Universal Framework for COFs Adsorption Prediction through Designed Multi-Modal Extraction and Cross-Modal Synergy

Covalent organic frameworks (COFs) are promising adsorbents for gas adsorption and separation, yet identifying optimal structures among their vast design space requires efficient high-throughput screening. Conventional machine-learning predictors rely heavily on specific gas-related features, which are time-consuming and limit scalability, leading to inefficiency and labor-intensive processes. Here, we propose COFAP, a universal COFs adsorption prediction framework that extracts multi-modal structural and chemical features via deep learning and fuses these complementary features through a cross-modal attention mechanism. Without relying on explicit gas-specific thermodynamic descriptors, COFAP achieves state-of-the-art prediction performance on the hypoCOFs dataset under the conditions investigated, outperforming existing approaches. Based on COFAP, we found that high-performing COFs for gas separation concentrate within a narrow range of pore size and surface area. A weight-adjustable prioritization scheme is also developed to enable flexible, application-specific ranking of candidate COFs. Superior efficiency and accuracy render COFAP directly deployable in crystalline porous materials.