SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3697-8
Multi-site coupling is a promising strategy for developing highly efficient and CO-resistant hydrogen oxidation reaction (HOR) catalysts for proton exchange membrane fuel cells (PEMFCs). However, designing multifunctional synergistic schemes for single-atom sites remains a significant challenge. Herein, we propose a dual-template-confined oxophilic engineering strategy to construct well-dispersed iridium-nickel (IrNi) atomic dimers adjacent to IrNi nanoclusters on porous nitrogen-doped carbon (IrNi Dimer/NC1.8-PNC). The paired IrNi dimer features an asymmetric Ir-N3 configuration coordinated with heteroatomic Ni-N3O via an N-bridge. Remarkably, IrNi Dimer/NC1.8-PNC exhibits a ~23-fold enhancement in mass activity (4.36 A mg−1 Ir at 20 mV) and 5-fold longer stability compared to benchmarking Pt/C toward HOR, while achieving a high rated power density of 1.18 W cm−2 in PEMFC anode applications. Furthermore, IrNi Dimer/NC1.8-PNC demonstrates superior CO tolerance over monometallic Ir and Pt/C in both half-cell and full-cell devices. Combined experimental and density functional theory studies reveal that oxophilic Ni modulates the electronic environment of Ir through alloying and dimer interactions, thereby enhancing HOR activity. Importantly, the asymmetric IrNi dimer enables efficient CO* and OH* co-adsorption while facilitating CO2* desorption, synergistically mitigating CO poisoning and improving atom utilization efficiency. This work provides a design strategy and fundamental insights for multi-site synergistic catalysts in PEMFC anodes.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025031202
Manganese-rich constructed wetlands (CWs) have emerged as an effective strategy for enhanced nitrogen removal, yet current understanding of their denitrification mechanisms remains limited to speculative interpretations of microbial community structures. This study developed a novel CW-MFC system integrating manganese ore (MO) and activated carbon (AC) substrates with microbial fuel cell (MFC) technology to investigate the manganese-nitrogen coupling biochemical metabolism. It was systematically evaluated the effects of influent organic carbon concentrations on nitrogen removal performance and elucidated the mechanisms of electron transfer and their coupling with nitrogen removal pathways through multi-dimensional analyses, including functional enzymes, extracellular polymeric substances (EPS) characterization, intra-/extracellular electron transfer-related gene expression, and electron transport activity. Results showed that the synergistic integration of MO, AC, and MFC configuration significantly enhanced nitrogen removal efficiency, with ammonium removal reaching up to 5.5 times that of the control group. The functional substrates notably upregulated enzyme activities of nitrogen transformation in biofilms while stimulating nitrification and anammox processes at the anode. EPS analysis revealed that Mn2+ derived from manganese reduction was captured by EPS, thereby facilitating the manganese cycling. Concurrently, the increased abundance of electron transport chain (ETC) and extracellular electron transfer (EET) genes, coupled with increased cytochrome C (Cyt-C) concentration and activity, confirmed enhanced EET performance. It indicated that the coordinated EET network among electrodes, microorganisms, MO, and AC serves as critical electron mediators for nitrogen transformation. This study provides mechanistic insights into manganese-carbon coupled CW-MFC systems regarding nutrient removal, biogeochemical cycling, and electron transfer dynamics, advancing fundamental knowledge for the development and application of manganese-rich constructed wetland technology.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202607021
The continuous expansion of urban sewage treatment capacity has led to a sustained increase in sludge generation, making efficient treatment, disposal, and resource recovery critical in environmental engineering. Machine learning (ML) offers substantial potential for prediction and optimization in sludge treatment by extracting non-linear features from complex operational data. This review systematically examines the application of ML across typical sludge treatment processes, including dewatering, resource recovery (anaerobic digestion), and terminal disposal (incineration and landfill). The general modeling workflow is summarized across three dimensions: dataset preparation, algorithm selection, and model evaluation. A comparative analysis evaluates the applicability and limitations of support vector machines (SVM), random forests (RF), artificial neural networks (ANN), and other deep learning models. SVMs demonstrate greater stability with small-to-medium sample sizes and high-dimensional data, while RFs exhibit strong generalization and provide variable importance insights. ANNs and deep learning models excel in large-scale data and time-series or image tasks but require high data quality. Key findings from the literature include ANN achieving R²=0.99 and RMSE=0.02 in dewatering prediction, and R²=0.86 with NRMSE=0.31 in anaerobic digestion, while gradient boosting reached R²=0.90 and RMSE=0.33. Future directions emphasize multi-source data fusion, model interpretability (e.g., SHAP), and coupling ML with mechanistic models to enhance predictive accuracy and generalization, supporting intelligent and refined sludge treatment management.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202607022
Anaerobic sludge digestion is the core process for achieving energy recovery and sludge reduction in wastewater treatment plants. However, its complex biological reaction mechanisms and multivariable coupling characteristics pose persistent challenges to process optimization and stable control. Traditional mechanistic models, while theoretically clear, suffer from parameter calibration difficulties and insufficient adaptability under dynamic and nonlinear conditions. Machine learning (ML) has gained attention for its powerful data modeling capabilities. This review systematically examines ML applications in sludge anaerobic digestion, focusing on biogas production prediction, process monitoring and early warning, and process parameter optimization. For gas production, hybrid models and deep learning achieve high-precision methane yield predictions. Soft-sensing models using easy-to-measure parameters enable real-time estimation of volatile fatty acids and total ammonia nitrogen. At the optimization level, coupling surrogate models with optimization algorithms provides dynamic regulation strategies for co-digestion ratios and pretreatment conditions. Interpretable methods address the 'black-box' issue, enhancing engineering acceptability. Deep integration of these methods with dynamic optimization supports an intelligent decision-making framework. However, translation from laboratory to engineering faces constraints including data quality, model generalization, and implementation. This paper provides an analytical framework combining predictive capability with engineering reliability for sludge treatment optimization.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3825-x
Polymer-based dielectric materials with high energy density and thermal stability are critical for modern electric/electronic industries. Polyimide (PI) based materials are promising due to their high temperature resistance and chemical inertness, yet their inherently low dielectric constant and limited charge-discharge energy density restrict applications in film capacitors. While incorporating ferroelectric or conductive fillers can enhance dielectric performance, batch-to-batch inconsistency and physical deterioration remain problematic. This study focuses on molecular structure design and modulation, preparing hyperbranched polyimides with different dianhydride monomers and branching degrees. The effects of chain packing density with polar groups on dielectric and energy storage performances were systematically investigated via experimentation and molecular simulation. Results demonstrate a significant correlation between monomers' electrical distribution and packing density in polymer systems. Molecular simulation further elucidated the underlying mechanism. This work establishes a foundation for designing polymer-based dielectric materials with high dielectric and energy storage performances at the molecular level.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-4037-1
Nasopharyngeal carcinoma (NPC) poses a therapeutic challenge due to its anatomical complexity and the limitations of conventional treatments in achieving precise targeting and sufficient efficacy. Here, we report a multifunctional platform based on heat-triggered electrospray self-healing porous poly(lactic-co-glycolic acid) (PLGA) microspheres encapsulating indocyanine green (ICG), sequentially coated with a tannic acid-Fe3+ (TAF) metal-phenolic network and fibronectin (FN) for targeted photothermal/chemodynamic combination therapy. The resulting functional microspheres (PI-TAF@FN) exhibit an average size of 1.9 μm, excellent colloidal stability, heat-induced self-healing performance, and a high photothermal conversion efficiency of 51.4%. These microspheres specifically target NPC cells via FN-mediated integrin recognition, enabling ICG/TAF-mediated photothermal therapy under 808-nm laser irradiation and TAF-mediated chemodynamic therapy, leading to enhanced cancer cell apoptosis in vitro. In a mouse NPC model, the combined photothermo-chemodynamic therapy achieved effective tumor treatment with minimal systemic toxicity. Furthermore, the dual TAF and ICG components allow multimode FN-targeted T1-weighted magnetic resonance/fluorescence/thermal imaging for precision NPC management. This electrospray self-healing porous microsphere platform offers a unique theranostic strategy that can integrate diverse therapeutic and diagnostic components for precision oncology.