Efficient conversion of nitrogen oxides (NOx) in oxygen-rich exhaust remains a major challenge for selective catalytic reduction (SCR) with C3H6 and CO, especially at low temperature (<200 degrees C). In this study, in-situ coprecipitation method was used to support Cu-Mn-Ce on layered double oxide (LDO), which was then anchored on reduced graphene oxide (rGO) and further doped with Co, Ni, or Al, to prepare the nanocomposite catalysts, Cu-Mn-Ce-LDO/rGO/M. The C3H6/CO-SCR reactivity of the catalysts was evaluated by a fixed bed microreactor. Results proved that the nanocomposite catalysts have two functions: capture and storage NO via strong adsorption on oxygen-vacancy-rich coordinatively unsaturated sites (CUS) at lower temperature and reduce NO to N-2 via C3H6/CO-SCR at higher temperature. The capacity for NO capture-storage was between 159.35-58.81 mu mol. g(-1). The NO conversion and N-2 selectivity was proven to be as high as 92.5 % and 93.7 % at 250 degrees C for Co-doped catalyst, i.e., CuMnCe-LDO/rGO/Co, during the C3H6/CO-SCR reaction. The catalysts showed a strong resistance to SO2, above 90 % of NO conversion and 94 % of N-2 selectivity in the presence of 1000 ppm of SO2. Detailed characterizations illustrated that finely dispersed oxide nanodomains were anchored to rGO surface with abundant defects, which enhanced NO adsorption and reduction. In-situ DRIFTS showed that bidentate nitrates were the dominant intermediates, alongside bridging and monodentate nitrates were also formed. A mechanism combined the C3H6-driven Langmuir-Hinshelwood mechanism below 200 degrees C and Mars-van-Krevelen mechanism above 250 degrees C for CO oxidation was proposed.
In the emerging field of urban digital twins (UDTs), advancing intelligent road inspection (IRI) vehicles with automatic road crack detection systems is essential for maintaining civil infrastructure. Over the past decade, deep learning-based road crack detection methods have been developed to detect cracks more efficiently, accurately, and objectively, with the goal of replacing manual visual inspection. Nonetheless, comprehensive comparative analyses of state-of-the-art (SoTA) deep learning techniques, especially data-fusion and label-efficient algorithms for this task, remain limited. This paper thoroughly reviews the SoTA deep learning-based algorithms, including (1) supervised, (2) unsupervised, (3) semi-supervised, and (4) weakly-supervised methods developed for road crack detection. In addition, we establish UDTIRI-Crack, a curated and standardized benchmark suite comprising 2500 images from seven public annotated datasets, and develop an online benchmark platform to provide standardized, reproducible, and extensible evaluation of algorithms. Comprehensive experiments are conducted to compare the detection performance, computational efficiency, and generalizability of public SoTA deep learning-based algorithms for road crack detection. The feasibility of foundation models and vision-language foundation models for road crack detection is further explored. Finally, the existing challenges and future development trends of deep learning-based road crack detection algorithms are discussed. We believe this review, together with the released dataset and platform, can serve as practical guidance for developing intelligent road detection vehicles with the next-generation road condition assessment systems. The benchmark UDTIRI-Crack is available at https://udtiri.com/submission/.
Developing solar evaporators that simultaneously achieve high evaporation efficiency and robust operational stability is a central research objective in seawater desalination. Fibrous aerogels are promising interfacial evaporation platforms due to their interconnected porous network that enables thermal localization and efficient water transport. However, mostly fiber aerogels typically employ a planar configuration design, which limits moisture release and hinders airflow coupling. While a three-dimensional (3D) structure could theoretically overcome these limitations, its mechanical performance is often insufficient to ensure structural stability. Herein, a mechanically robust fibrous aerogel is developed based on nano-microfiber multiscale networks, featuring high flexibility, fatigue resistance, and interfacial stability. Leveraging its excellent deformability, a lantern-shaped 3D interfacial solar evaporator is constructed. The open 3D architecture effectively enhances coupling between the evaporation interface and ambient airflow, thereby strengthening convective vapor transport and suppressing vapor accumulation near the interface. As a result, the evaporator achieves evaporation rates of 2.59 kg m-2 h-1 under natural evaporation and 7.11 kg m-2 h-1 under forced airflow, while maintaining excellent long-term salt tolerance under high salinity. Furthermore, efficient vapor transport and condensation recovery enable an outdoor daily freshwater yield of 27.2 L m-2 d-1. This work provides a viable material and structural design strategy for high-performance and stable solar desalination systems. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
The advancement of lithium-ion batteries toward higher energy densities depends critically on the development of anode materials that offer both exceptional capacity and structural durability. Transition metal hydroxides like Co(OH)2 are promising but suffer from low conductivity and structural instability. While the α-phase is often prioritized for its larger interlayer spacing, this work demonstrates that the less-explored β-phase can be engineered for superior performance through interface control. We report a facile one-step electrodeposition strategy to fabricate petal-like N-modified β-Co(OH)2 nanosheets directly on carbon cloth (CC) using 2-methylimidazole (2-MI) as a dual-function additive. The 2-MI acts as a morphological director, enabling a hierarchical porous structure, and as a nitrogen source, enhancing electronic conductivity and Li+ interaction. The optimized CC@β-Co(OH)2-8 anode exhibits an exceptional reversible capacity of 2525.4 mAh g−1 at 0.5 A g−1 and outstanding rate capability (1146.9 mAh g−1 at 2 A g−1). It also demonstrates remarkable long-term stability, retaining 1014.5 mAh g−1 after 500 cycles at 1 A g−1. Kinetic analysis reveals a dominant pseudocapacitive charge storage mechanism (71.3%), accounting for the excellent rate performance. This interface engineering strategy provides a valuable pathway for developing high-performance metal hydroxide-based anodes.
Introduction Submicron particulate matter (PM1) exposure in urban rail transit systems is a public health concern, but evidence for metro commuters remains limited. We assessed spatiotemporal variation in exposure on Shanghai Metro Line 9, focusing on tidal commuting, defined as directional passenger-flow imbalance. Methods We collected 53 p.m.1 exposure datasets during winter (6–14 January 2025) and summer (21–25 July 2025) across outdoor, concourse, platform, aboveground cabin, and underground-cabin microenvironments. A microenvironmental model integrated PM1 concentration, exposure time, inhalation rate, and pulmonary deposition fraction to estimate trip-specific exposure and lung-deposited doses. Results Platforms were the main exposure hotspot, averaging 63.4 μg/m3, while cabin concentrations were 47–85% of those on adjacent platforms. Mean outdoor and cabin concentrations were 101.9 ± 59.9 μg/m3 and 66.5 ± 31.5 μg/m3 in winter, compared with 40.0 ± 22.1 μg/m3 and 34.6 ± 13.4 μg/m3 in summer. Aboveground cabins had a higher outdoor infiltration coefficient than underground cabins. Conversely, the model-estimated system-generated component was 6.5-fold greater underground than aboveground. Mean PM1 in cabin was 21% higher in the peak-flow direction, indicating an association between passenger-flow imbalance and cabin exposure. One morning peak and one evening peak commute produced an estimated lung deposited dose of 42.3 μg. Stations contributed 56% of this dose despite accounting for only 27% of commuting time. Conclusion Commuter exposure reflected ambient infiltration, internal sources, spatial configuration, season, and passenger-flow conditions. Platform air purification, underground ventilation, and peak hour cabin filtration should be prioritized to reduce lung deposition of PM1.
