SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4182-6
Substrate interactions dictate the epitaxial growth of low-dimensional nanomaterials, yet controlling these interfaces at the atomic scale precision remains a critical challenge. Blue phosphorene (blueP) with freestanding lattice constant, experimentally realized exclusively on Ag(111), provides a unique platform to explore this interplay. Here, we elucidate the structural evolution of blueP on Ag(111), revealing that neighboring islands are not isolated but linked by single-phosphorus-atom bridges. To manipulate the interfacial coupling, we introduce a tellurium interlayer, driving the formation of an interfacial AgTe buffer that effectively decouples the islands. By tuning the substrate temperature, we achieve the synthesis of magic-number blueP clusters with uniform size and geometry. The resulting isolated blueP nanostructures facilitate the emergence of higher-order topological corner states in triangular geometries. Our findings demonstrate that tailoring interfacial interactions offers a robust route for reshaping phosphorene nanostructures, establishing essential building blocks for next-generation topological quantum materials.
Journal of Environmental Engineering Technology•2026•DOI: 10.13205/j.hjgc.202605005
Water quality prediction is essential for river basin management, yet existing models often struggle with non-stationary, noisy monitoring data. This study collected water quality data from two city-level control sections in southern China from December 2020 to June 2024, including eight indicators: water temperature, turbidity, pH, conductivity, dissolved oxygen (DO), ammonia nitrogen (NH4+-N), total phosphorus (TP), and permanganate index (CODMn). To predict four key indicators (DO, NH4+-N, TP, CODMn), we developed hybrid models combining seasonal trend decomposition (STD), Bayesian hyperparameter optimization, and either random forest (RF) or XGBoost. STD smoothed and denoised the data while extracting seasonal factors; Bayesian optimization tuned model hyperparameters. Evaluation showed that the STD-Bayesian-XGBoost model achieved smaller bias errors and higher prediction accuracy than STD-Bayesian-RF. Specifically, XGBoost reduced root mean square error (RMSE) by 15-20% across all four indicators and improved the coefficient of determination (R²) to above 0.90, compared to RF's 0.85-0.88. The models were validated on southern river data, but the methodology is generalizable to other climatic and hydrological settings. This work provides a technical reference for pollution reduction and carbon management in regional watersheds.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.3724/2097-213X.2026.JFCT.0003
Phosphorus and transition metal-modified ZSM-5 zeolites were prepared via incipient wetness impregnation and characterized by XRD, N2 adsorption-desorption, and pyridine-IR spectroscopy. The effects of modified zeolites on catalytic pyrolysis of n-heptane, 3-methylhexane, and methylcyclohexane were systematically investigated, revealing the structure-activity relationship between acid properties and catalytic performance. Results indicate that phosphorus and transition metal modification can regulate the L/B acid ratio of ZSM-5, which significantly influences product distribution. An excessively high L/B acid ratio promotes hydrogen and coke formation, leading to pore blockage and reduced conversion, whereas an appropriate L/B acid ratio facilitates mild dehydrogenation, enhancing ethylene and propylene yields. Reactant conversion followed the order: n-heptane > 3-methylhexane > methylcyclohexane. Efficient conversion of 3-methylhexane and methylcyclohexane requires catalysts with high L acid amount, strong Brønsted acid amount, optimized L/B acid ratio, as well as high specific surface area and micropore specific surface area. This study provides critical insights for designing high-efficiency alkane pyrolysis catalysts.
Environmental Chemistry•2026•DOI: 10.7524/j.issn.0254-6108.2025041405
Microplastics, as a class of emerging environmental contaminants, pose global concerns due to their potential ecological and human health impacts. Accurate identification and quantification of microplastics in environmental matrices are essential for assessing their environmental fate and ecological risks. Pyrolysis-based analytical methods, which decompose macromolecules into smaller fragments followed by gas chromatographic separation and mass spectrometric detection, offer high sensitivity and accuracy, making them significant for microplastic analysis. Despite these advantages, their application remains nascent, with limited comprehensive understanding of their applicability across diverse environmental media. This review systematically compares three pyrolysis-based techniques—pyrolysis-gas chromatography-mass spectrometry (Py-GC-MS), thermogravimetry-differential scanning calorimetry (TGA-DSC), and thermal extraction-desorption gas chromatography-mass spectrometry (TED-GC-MS)—for microplastic detection in various matrices. The effectiveness of each method is evaluated in terms of sensitivity, selectivity, and matrix compatibility. Critical challenges, including lack of standardized protocols, complex sample pretreatment requirements, and limitations in quantifying mixtures, are identified. Future research directions emphasize the need for standardization, optimization of pretreatment for complex matrices, and integration with complementary techniques such as FTIR and Raman spectroscopy to enhance comprehensive microplastic characterization. This review provides a critical framework for selecting appropriate pyrolysis-based methods and highlights areas requiring further methodological development.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-026-4124-x
Aerogel fibers, featuring distinct porous architecture and fiber flexibility, have emerged as leading materials for personal thermal protection; however, complex drying processes and singular thermal insulation mechanisms limit their use in complex environments. Here, aramid nanofiber/carbon nanotube (ANF/CNT) aerogel fibers integrating passive thermal insulation and active solar heating were fabricated via wet-spinning and ambient-pressure drying (APD). The incorporation of CNT and Ca2+ generates abundant physical and chemical crosslinking points, strengthening the nanofiber network skeleton and reducing structural collapse during APD to only 8.9% shrinkage. The resulting ANF/CNT aerogel textiles exhibit low thermal conductivity of 33.8–40.4 mW/(m K) and thermal insulation capability from −196 to 400 °C. The photothermal effect of CNT enables active solar heating, effectively supplementing passive insulation and allowing survival in extremely cold environments. In real tests, the synergistic effect improved skin temperature by up to 5.9 °C, significantly higher than 1.6 °C from passive insulation alone. These ANF/CNT aerogel fibers combine flexibility, mechanical strength, and flame retardancy, demonstrating promising potential for smart, controllable personal thermal management applications.