SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4419-1
Proton exchange membrane fuel cells (PEMFCs) fed with reformate hydrogen suffer severe anode poisoning by trace CO, necessitating high CO electrooxidation potentials that degrade performance and durability. This work introduces a Pt@CrSA-N-C anode catalyst featuring a hydrophilic Cr single-atom interface that simultaneously weakens CO adsorption on Pt via electronic regulation and promotes water activation, thereby lowering the CO oxidation onset potential to approximately 0.13 V vs. RHE. The onset potential was determined by two independent methods: the first potential at which the background-corrected current exceeds 0 mA cm-2 during CO oxidation reaction tests in a three-electrode system, and the potential at which the forward scan current exceeds the N2 background current in CO-stripping voltammetry. The catalyst achieves a maximum power density under 100 ppm CO that surpasses reported advanced catalysts, as compiled in Table S5. Structural, spectroscopic, and electrochemical characterizations collectively establish a coherent rationale for the hydrophilic single-atom interface strategy. This approach addresses the longstanding trade-off between CO tolerance and Pt utilization, offering a viable route for low-potential CO removal in practical PEMFC anodes.
The Chinese Journal of Process Engineering•2026•DOI: 10.12034/j.issn.1009-606X.225213
Gas-liquid stirred tanks are widely used in oxidation, hydrogenation, and other chemical processes, where the gas dispersion state directly affects production efficiency. This study systematically investigated the effects of impeller type, impeller installation height, and rotational speed on gas-liquid dispersion in a stirred tank equipped with a porous tube sparger. Two typical impellers, a wide hydrofoil (WH) and a half-elliptical disk turbine (HEDT), were tested at various installation heights (L/D ratios) and gassing rates. The critical rotational speed for complete gas dispersion, agitation power consumption, and overall gas holdup were measured. Results showed that for both impellers, the critical Froude number (Fr) decreased significantly with increasing gas flow number (FlG). Under the same gassing rate, the HEDT impeller generally required a higher critical Fr and greater agitation power for complete dispersion compared to the WH impeller. Relative power demand (RPD) decreased as FlG increased, with a more pronounced decline at higher L/D ratios. At different impeller positions, the RPD of the HEDT impeller was higher than that of the WH impeller, indicating that the HEDT impeller's power was less affected by gas. Notably, the impeller installation height significantly influenced gas holdup and power consumption. When L/D = 0.75, higher gas holdup and lower power consumption were observed. This work provides crucial theoretical and data support for optimizing the design of gas-liquid stirred tanks with gas sparging, offering clear engineering value for enhancing mass transfer efficiency and energy-saving operation in chemical processes.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202607014
Reducing ammonia emissions and recovering lost nitrogen are critical for enhancing nitrogen content in compost. Biological trickling filters, as end-of-pipe odor control, retain ammonia nitrogen in effluent, offering a reuse pathway. However, the impact of nitrogen-rich wastewater reuse within the optimal C/N range (20.0:1–30.0:1) remains unclear. This study composted biogas residue, sawdust, food waste, and mushroom residue, setting initial C/N as the control variable. Four groups were established: low C/N with nitrogen-rich wastewater (LRN), low C/N with deionized water (LRW), high C/N with nitrogen-rich wastewater (HRN), and high C/N with deionized water (HRW). Simulated wastewater (2000 mg/L NH4+-N and 2000 mg/L NO2−-N) was recycled. Results showed no inhibition of final maturity; pH (8.17–8.48) and seed germination index (GI) (90.85%–122.96%) met organic fertilizer standards. HRN reduced cumulative total greenhouse gases, N2O, and NH3 by 20.32%–30.35%, 0.67%–53.38%, and 52.14%–62.15% compared to LRN and LRW. Although HRN emissions were slightly higher than HRW (total GHGs +4.56%, NH3 +4.99%), HRN final nitrogen content (4691.27 mg/kg) exceeded HRW (4514.96 mg/kg), attributed to sufficient carbon enhancing microbial assimilation. Conversely, low C/N with nitrogen-rich wastewater increased NH3 and N2O emissions (LRN vs LRW: +26.43% and +112.99%) due to carbon limitation. Thus, high initial C/N with nitrogen-rich wastewater reuse effectively reduces gaseous nitrogen loss and greenhouse gas emissions while maintaining compost maturity.
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.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4146-8
The conventional trial-and-error approach for the research and development (R&D) of high-performance super-hydrophilic coatings presents long-standing challenges, including data scarcity, unclear structure-activity relationships, and lack of design guidelines. In this study, an innovative multi-model framework that synergistically integrates a specialized polymer large language model (PolyLLM) and a polymer machine learning model (PolyML) for intelligent R&D of super-hydrophilic coating systems is designed and demonstrated. During the hydrophilic polymer research phase, the multi-model leverages the domain-specific insights and chemical tool capabilities of the fine-tuned PolyLLM to systematically screen 852 hydrophilic monomers and generate 3880 hypothetical polymer architectures. By utilizing the PolyML, the multi-model enables precise performance predictions and quantitative evaluations of feature importance to facilitate efficient high-throughput screening of optimal structures from the generated polymer database. Experimental validation confirms that the multi-model achieves a mean absolute error (MAE) below 10% across various polymer property prediction tasks compared to experimental data. This approach leads to the rapid discovery of hydrophilic polymers with exceptional anti-swelling and wear-resistance. In developing super-hydrophilic coatings, the multi-model effectively combines PolyLLM's guidance for large-scale synthesis with PolyML's capabilities for precise formulation optimization and curing parameter adjustments. The effectiveness of multi-model is demonstrated by the accelerated development of high-performance anti-fogging coatings for swim goggles and optical films with outstanding water- and wear-resistance that significantly outperform leading commercial products. This multi-model offers a flexible approach for advanced polymer coatings, leading to a seamless connection between laboratory research and industrial development.