Layered two-dimensional heterostructures are attractive as anodes for alkali metal ion batteries, especially the large sized Na/K-ion batteries (sodium/potassium ion batteries (SIBs/PIBs)), but suffer from poor high-rate capability and cyclability. In this work, a novel hierarchical architecture of interlayerexpanded MoSe2 hollow nanospheres featuring carbon monolayer intercalation embedded in Ti3 C2 Tx MXene nanosheets (IE-MoSe2 /C@Ti3 C2 Tx ) is engineered as a durable anode for hosting Na+ /K+ . The IEMoSe2 /C@Ti3 C2 Tx architectures have numerous structural merits for enhanced reaction kinetics and cycling stability, including: (i) the sandwiched IE-MoSe2 /C structure, consisting of interoverlapped MoSe2 and carbon monolayers, can significantly widen the interlayer spacing (1.04 nm) to reduce Na+ /K+ diffusion barriers and improve the internal electronic conductivity; (ii) the sandwiched IE-MoSe2 /C structure provide interlayer-confined phase separation for fast and reversible conversion reaction; and (iii) the IEMoSe2 /C nanospheres with hollow interiors can accommodate huge volume change, while the conductive MXene sheets serve as a robust framework. Consequently, the IE-MoSe2 /C@Ti3 C2 Tx architecture possesses excellent rate capability (205.6 mAh g-1 at 20 A g-1 ) and cycling stability (142.2 mAh g-1 at 20 A g-1 for the 10 0 0 0th cycle) as anodes for SIBs. Moreover, an intercalation coupled with the conversion mechanism of reversible Na+ storage in IE-MoSe2 /C@Ti3 C2 Tx is revealed through in-situ and ex-situ characterizations. For PIBs, a reversible capacity of 231.1 mA h g-1 is maintained after 1400 cycles at 1 A g-1 . Significantly, the Na3 V2 (PO4 )3 ||IE-MoSe2 /C@Ti3 C2 Tx full cell is demonstrated and exhibits excellent cycling performance, yielding a long cycling life of 800 cycles at 2 C with negligible capacity loss, confirming the great potential for practical applications. (c) 2026 Published by Elsevier Ltd on behalf of The editorial office of Journal of Materials Science & Technology.
Accurate prediction of the three-dimensional (3D) morphology of jet-grouted columns is critical for ensuring foundation stability and seepage control. However, traditional empirical and artificial intelligence-based models predominantly focus on average diameter estimation, failing to capture depth-dependent morphological variations in stratified soft soils where jet energy attenuation is highly nonlinear. To bridge this gap, this study establishes a quantitative mathematical framework coupling submerged free turbulent jet theory with site-specific soil failure criteria. By integrating critical stratigraphic variables, including fine-grained content, mean particle diameter, and undrained shear strength, a robust 3D reconstruction algorithm and user-centric intelligent software were developed. Field investigations at the Wenzhou Tingtian project in a coastal soft soil region preliminarily validated the model’s predictive accuracy. The results demonstrate that the proposed method can estimate the 3D morphology complex column geometries and accurately capture localized “steep drops” in diameter at stratigraphic interfaces, showing a generally consistent with in-situ measured benchmarks. This research provides a scientific decision-making platform for design optimization and real-time quality control in ground improvement engineering using jet grouting.
Urban parks are important biodiversity hotspots as well as recreational spaces in cities. It is desirable for urban residents to derive pleasure from parks that also embody biodiversity values due to their ecological and health benefits. Despite the evidence that urban residents are happier and healthier when they have access to green spaces of good quality, it remains unclear how residents prioritise biodiversity when considering the joint effects of other green space attributes, and how these priorities differ from those of landscape/ecology professionals. We used Adaptive Choice-Based Conjoint (ACBC) analysis to examine how biodiversity was prioritised in trade-offs with other important park attributes among residents and professionals. The ACBC study was conducted in Hangzhou (N = 187) and Shanghai (N = 194), two cities in eastern China. Utility values and average importances were estimated using Hierarchical Bayes (HB). While both residents and professionals preferred higher biodiversity levels, biodiversity improvements from poor to moderate yielded greater utility gains than from moderate to rich. Residents prioritised facilities over biodiversity when making trade-offs, whereas professionals prioritised biodiversity as the most important park attribute and valued it significantly more than residents. In both groups, preferences for higher biodiversity were associated with preferences for fewer facilities, denser tree cover, and low-maintenance, wild-looking landscapes. Urban park planning may prioritise biodiversity enhancement in biodiversity-poor sites over further enhancement in biodiversity-moderate sites to optimise resource allocation. Professionals should account for differences between their own preferences and those of residents, while recognising the importance of managing biodiversity-amenity trade-offs and supporting the coexistence of both objectives.
Blind super-resolution (SR) of remote sensing images is highly challenging due to the complex and spatially variant degradations caused by diverse imaging conditions, atmospheric effects, and sensor characteristics. Most existing methods assume uniform degradation across the entire image or rely on global degradation representations, which fail to capture the intrinsic spatial heterogeneity in real-world remote sensing scenarios. To address this issue, a novel spatial degradation field modeling framework is proposed, in which spatially varying degradations are explicitly characterized at the patch level. Specifically, a spatially adaptive degradation estimation network (SADEN) is designed to estimate patch-wise degradation representations, which are further organized into a spatial degradation field to describe the degradation distribution across the image. Based on this representation, an adaptive degradation adjustment module (ADAM) is introduced to dynamically modulate feature responses conditioned on the estimated degradation. The module incorporates both spatial modulation (SM) and channel modulation (CM) mechanisms, enabling the network to adaptively refine spatial structures and channel-wise feature responses under complex degradation patterns. Extensive experiments across multiple benchmark remote sensing datasets demonstrate that the proposed method effectively captures spatially varying degradation patterns and consistently outperforms state-of-the-art blind super-resolution approaches in both quantitative performance and visual quality. Moreover, its effectiveness is further validated through evaluations on real-world scenarios as well as downstream semantic segmentation tasks.
Bio-oil production from biomass pyrolysis is an important pathway for developing green alternative fuels and supporting the low-carbon energy transition. However, bio-oil production is strongly affected by process parameters such as feedstock blending ratio and condensation temperature, which involve complex trade-offs among environmental impact, product yield, target compound yield, and production cost. Therefore, a methodological framework capable of balancing these indicators is needed to comprehensively evaluate the overall sustainability performance of different process schemes. In this study, the co-pyrolysis of cotton straw and polyethylene coupled with selective condensation was selected as the case process. The ReCiPe 2016 Midpoint (H) method was used to quantify the environmental impacts under different feedstock mass ratios (1:0, 1:2, 1:1, and 2:1) and condensation temperatures (303–343 K). Subsequently, the combined weighting-TOPSIS method was applied to integrate four key indicators, namely GWP, bio-oil yield, phenol yield in bio-oil, and production cost. The results showed that the 1:2 mass ratio at 303 K resulted in the lowest GWP value of 8.57 kg CO2 eq, which was 39.18% lower than that of cotton straw pyrolysis alone at 343 K. This indicates that appropriate polyethylene incorporation under a lower condensation temperature can reduce the climate-change burden of bio-oil production. Within the multi-objective evaluation framework, all scenarios were assessed under different indicator-priority settings. The scenario with a mass ratio of 2:1 at 303 K was identified as the optimal scheme, as it consistently ranked among the top positions and showed stable uncertainty distributions across several key environmental indicators. The proposed framework provides practical guidance for the optimization of biomass pyrolysis and the valorization of bio-oil, and offers support for advancing the green industrialization of agricultural waste resource utilization.
