SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4406-y
Covalent organic frameworks (COFs) are promising adsorbents for uranium extraction from complex aqueous environments due to their tunable pore structures and customizable functionalities. However, conventional bottom-up assembly routes yield frameworks with fixed dimensionality, where internal pores and buried functional sites remain inaccessible, limiting dynamic optimization for uranium capture. This study introduces a reversible coordination-directed clip-off strategy that enables dimensional programming of COFs through silver-nitrogen coordination bonds and thiosulfate/silver ion regulators. The approach allows controlled cleavage and reconstruction of coordination bonds, dynamically exposing hidden binding sites and adapting the framework to uranium extraction requirements. While the strategy demonstrates high-efficiency uranium extraction, it faces challenges including increased material and operating costs from silver-based regulators, potential structural fatigue from repeated cleavage-reconstruction cycles, and limited validation beyond laboratory scale. The reversible dimensional programming is generalizable to other reticular frameworks such as metal-organic frameworks (MOFs), enabling stimuli-responsive smart materials, controlled-release carriers, and adaptive separation membranes. Integration with machine learning and computational screening could accelerate rational design of functional active sites. This interdisciplinary approach offers a pathway toward intelligent, dimensionally morphing materials for energy and environmental sustainability, though optimization of regulating components and structural durability is required for practical scalability.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3831-0
Ammonia decomposition is a key process for generating COx-free hydrogen, yet conventional cobalt catalysts require high temperatures (>550 °C) to overcome the strong Co–N binding that limits N2 desorption. Here we report a novel Co catalyst supported on a Ce and N co-modified perovskite (Co@La_xCe_{1-x}AlO_{3-y}N_z) that achieves 92.6% ammonia conversion with a hydrogen production rate of 9.7 mmol g−1 min−1 at 425 °C and GHSV = 9000 mL h−1 g_cat−1, representing a 125 °C reduction in operating temperature relative to conventional Co-based catalysts. Mechanistic studies using isotopic labeling and in-situ DRIFTS reveal that synergistic Ce and N modification creates a unique LA-L(A+B)-LB active site configuration, which lowers the Schottky barrier at the metal-support interface and promotes facile hydrogen spillover. The reaction proceeds via an interfacial Mars-van Krevelen mechanism, contrasting with the traditional Langmuir-Hinshelwood pathway on conventional Co catalysts. This work provides new insights for designing low-temperature Co-based ammonia decomposition catalysts.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3919-0
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 Materials•2026•DOI: 10.1007/s40843-025-4104-4
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.