Search Results (6,951)

Soil organic carbon (SOC) plays a critical role in climate regulation, soil fertility, and ecosystem resilience, making its accurate spatial quantification essential for sustainable land management and greenhouse gas (GHG) reporting. However, mapping SOC in heterogeneous agroforestry systems remains challenging due to vegetation cover and landscape complexity. In this study, we develop and evaluate a hybrid bare soil modelling- Digital Soil Mapping supported by ML framework to generate high-resolution soil properties predictions in Mediterranean agroforestry systems (Extremadura, Spain). A dual modelling approach was implemented, combining (i) Bare Soil modelling using Sentinel-2 multi-temporal reflectance composites and (ii) Digital Soil Mapping (DSM) supported by environmental covariates (climate, terrain, vegetation) following the SCORPAN framework. Machine learning models, namely Quantile Regression Forests (QRF) and Extreme Gradient Boosting (XGBoost), were applied and optimised using automated hyperparameter tuning (FLAML). A total of 107 LUCAS topsoil samples and 36 complementary points from the Forest ICP Level I were used for calibration and validation, with a 70/30 train–test split. Results show that Sentinel-2-based modelling can effectively capture SOC spatial variability in bare soil conditions, while DSM improves predictions in vegetated areas. Model performance reached R² values up to 0.76 (QRF, pH) and RMSE as low as 0.03 (XGBoost, N), with uncertainty quantified using the Prediction Interval Ratio (PIR) and performance further supported by RPIQ values up to 3.15. However, prediction accuracy remains sensitive to vegetation structure and sample density. The proposed framework provides a scalable and uncertainty-aware approach for SOC mapping, supporting Tier-3 GHG inventories and emerging Monitoring, Reporting, and Verification (MRV) systems. The results highlight the importance of integrating multi-source datasets and hybrid modelling strategies for reliable SOC estimation in complex landscapes. Full article

Saltwater intrusion (SI) threatens groundwater sustainability in nearshore regions, particularly in Bangladesh, where over-extraction and sea-level rise accelerate aquifer salinization. Accurate prediction of SI dynamics is therefore critical for effective groundwater management. This study developed and evaluated several deep learning and hybrid models, including CNN, DNN, LSTM, CNN–DNN, CNN–LSTM, DNN–LSTM, and CNN–DNN–LSTM, to predict SI in a nearshore aquifer system. Predictor–response datasets were generated using the three-dimensional density-dependent flow and solute transport model FEMWATER. This study presents the first comprehensive benchmarking of standalone and hybrid CNN–DNN–LSTM models for SI prediction in a Bangladesh nearshore aquifer, supported by CRITIC–EDAS-based model ranking. Model performance was assessed using RMSE, MAE, MAD, R, IOA, a-20, NRMSE, along with CRITIC weighting and EDAS ranking. Results indicate that hybrid models integrating LSTM outperformed standalone models. The CNN–LSTM model achieved the best performance at OW1 (RMSE = 1.57 mg/L, MAE = 1.26 mg/L, R = 0.99, IOA = 0.99). The DNN–LSTM model performed best at OW2 (RMSE = 2.87 mg/L, IOA = 0.98, R = 0.97) and OW3 (RMSE = 1.95 mg/L, IOA = 0.99, R = 0.99). In contrast, the DNN model showed poor performance, while the CNN model demonstrated moderate performance and the LSTM model underperformed. Overall, the hybrid CNN–LSTM and DNN–LSTM models demonstrated superior accuracy and robustness for reliable SI prediction and sustainable groundwater management. Full article

Travel–time-based thermal tracer tomography (TTT) has emerged as a promising technique for characterizing aquifer heterogeneity. However, the influence of travel–time definitions and data density on inversion performance is not well understood. In this study, we present a controlled two-dimensional sandbox experiment designed to systematically investigate three travel–time definitions (early-time t₁₀, intermediate t₅₀, and peak-time t_peak) under data-rich (32 travel times) and data-sparse (10 travel times) conditions. The obtained hydraulic conductivity (K) fields are benchmarked against permeameter measurements and a geostatistical inversion that assimilates dense steady-state head observations. The results demonstrate that all three travel–time definitions satisfactorily reproduce the primary layered heterogeneity when abundant travel–time data are available, with t₅₀ and t_peak providing marginally better structural fidelity under data-rich conditions. However, only the early-time t₁₀ definition preserves the spatial continuity of dominant geological structures under data-sparse conditions, exhibiting superior robustness. All TTT inversions systematically underestimate the K ranges and exhibit pronounced range compression, whereas the geostatistical inversion overestimates K and introduces spurious high-value extremes. Forward thermal transport simulations reveal that TTT-derived K fields yield systematically delayed thermal breakthroughs, while the geostatistical inversion yields more accurate predictions. These findings highlight the critical interplay between travel–time diagnostics and observation density. They also underscore the necessity of jointly inverting hydraulic and thermal data to overcome the limitations of single-dataset approaches for reliable aquifer characterization and transport prediction. Full article

Hydrogen-compatible elastomeric seals are critical for the reliability and safety of high-pressure hydrogen infrastructure. However, hydrogen exposure can alter the mechanical response and surface condition of elastomeric materials through coupled transport–mechanical interactions. This study presents a comparative experimental and data-driven investigation of the pressure-dependent degradation behavior of ethylene propylene diene monomer (EPDM), nitrile butadiene rubber (NBR), and fluorocarbon elastomer (FKM) O-ring seals following 192 h exposure to hydrogen pressures ranging from 800 to 7000 psi at room temperature. Tensile testing was performed directly on complete O-ring geometries, and descriptor-based analysis was used to quantify peak-response behavior, energy absorption, stiffness evolution, and normalized deformation characteristics. Multivariate statistical methods, principal component analysis (PCA), clustering analysis, and Random Forest regression were applied to identify material-specific degradation patterns. NBR maintained the highest overall load-bearing capability and stiffness-related response across the investigated pressure range, whereas EPDM exhibited more compliant and non-monotonic deformation behavior. FKM showed the strongest pressure sensitivity, with substantial increases in force- and stiffness-related descriptors at elevated hydrogen pressures. Optical image analysis revealed pronounced increases in defect density and defect area fraction for NBR, while FKM exhibited comparatively stable surface-state behavior. PCA and clustering analyses identified distinct material-dependent degradation trajectories, and Random Forest regression achieved an R² value of 0.888 for energy-absorption prediction. The results demonstrate that hydrogen-induced degradation emerges through coupled interactions among stiffness evolution, deformation progression, energy absorption, and surface-state changes, providing a comparative framework for assessing elastomer performance in hydrogen environments. Full article

Although garnet-type Li₇La₃Zr₂O₁₂ (LLZO) solid electrolytes are promising candidates for high-energy-density all-solid-state batteries, their practical applications are limited by high-voltage oxidation instability and interfacial degradation. To address these limitations, Al-doped LLZO (Al-LLZO) solid electrolytes were synthesized via a conventional solid-state reaction method, and the effects of PE-ALD-derived Al₂O₃ nanocoatings on electrochemical properties and interfacial stability were investigated. Al₂O₃ nanocoatings with different structures (5 and 10 nm single-side, and 5 nm double-side) were deposited on Al-LLZO pellets using plasma-enhanced atomic layer deposition. The Al₂O₃ coating reduced electronic conductivity by approximately one order of magnitude while maintaining similar ionic conductivity. Linear sweep voltammetry revealed that initial oxidation onset voltage increased from ~4.2 V (bare Al-LLZO) to ~5.0 V (5 nm-coated samples), while the 10 nm-coated sample exhibited the most delayed anodic current response (~5.2 V). The 5 nm double-side coated sample showed the best Li plating/stripping stability with a critical current density of 1.10 mA/cm² and stable long-term galvanostatic cycling behavior over 200 h at 0.05 mA/cm². Thus, ALD-based Al₂O₃ interfacial engineering can simultaneously improve the high-voltage oxidation and Li metal interfacial stabilities of garnet-type Al-LLZO solid electrolytes for practical all-solid-state batteries. Full article

As a Chinese national key protected medicinal fern naturally occurring in forest understories, Cibotium barometz faces severe threats of wild population degradation, while standardized large-scale artificial breeding technology for conservation purposes remains immature. To establish an efficient spore-based conservation propagation system for this endangered forest fern, this study quantified the independent and interactive effects of spore storage temperature, storage duration and sowing density on spore germination, gametophyte growth and sporophyte seedling establishment. Spores were preserved under four gradient temperature treatments with sequential sampling at multiple storage durations, followed by sowing trials with a series of density gradients; germination rate, seedling establishment rate and gametophyte–sporophyte conversion rate were dynamically recorded and statistically analyzed. The results demonstrated that appropriately extended storage significantly shortened the germination phase and simultaneously elevated both spore germination and sporophyte seedling formation rates. Among all temperature treatments, storage at −4 °C achieved the maximum germination and seedling establishment capacity, whereas ultra-low-temperature cryopreservation at −196 °C greatly promoted gametophyte–sporophyte conversion rate. The optimal sowing density balancing growth space and survival rate was determined to be 30 spores per cm². The complete dynamic developmental traits covering the full spore propagation life cycle of C. barometz were systematically summarized in this work. Our findings supply reliable technical parameters to standardize spore breeding protocols, and offer critical support for ex situ conservation, wild forest population restoration and sustainable resource utilization of C. barometz. Full article

The diversity of mating systems in fish is unparalleled among vertebrates. This variability is shaped by a long evolutionary history associated with differences in selection pressures and plasticity within species. However, there is also significant intraspecific variability within species, often related to differences in environments among populations. Herein, I explore how habitat features (temperature, oxygen, turbidity and vegetation) and availability of mates or mating resources (nest sites, population density, sex ratio and alternative mating strategies) can affect the distribution of reproductive success in a population. The literature reviewed here indicates that differences in the intensity of sexual selection and variation in mating-system structure can be directly related to differences in breeding habitat. The way in which habitat affects mating-system structure is complex, with both abiotic and biotic factors interacting to influence different aspects of breeding behavior and success. Thus far, our understanding of variation in mating systems in fishes is based on very well-studied species, and more exploration is needed to provide an overview of habitat and mating-system structure. This is critical as we face human-induced changes in breeding habitats that can alter mating systems and potentially affect variation and viability of fish populations. Full article

Surface erosion of loess slopes in arid and semi-arid regions of China remains a critical geotechnical issue, requiring green and low-carbon stabilization techniques. This study investigated the effectiveness of enzyme-induced carbonate precipitation (EICP) for the surface spraying reinforcement of Q3 loess collected from a high-fill engineering site at Yan’an University. Single-factor tests, response surface methodology (RSM), surface strength tests, CT-based three-dimensional pore reconstruction, and scanning electron microscopy (SEM) were conducted to evaluate the effects of cementation solution concentration and spraying dosage. The cementation solution was prepared by mixing analytical-grade urea and anhydrous calcium chloride at a 1:1 molar ratio, and the specimens were compacted to a dry density of 1.4 g/cm³. The results showed that surface strength first increased and then decreased with increasing cementation solution concentration and spraying dosage. Spraying dosage had a more pronounced influence than cementation solution concentration; excessive spraying above 9 L/m² reduced surface strength because of the high water sensitivity of loess. Five replicate tests at the central point were conducted to evaluate experimental error. The optimal parameters were 1.5 mol/L for cementation solution concentration and 9 L/m² for spraying dosage. CT and SEM results showed that CaCO₃ precipitation filled large pores and cemented soil particles, reducing total porosity from 6.7% to approximately 4.0%. These findings indicate that EICP improves loess surface strength mainly through pore filling and particle cementation, providing guidance for the ecological protection of loess slopes. Full article

Al-Cu-Fe quasicrystalline coatings were deposited on AISI 321 stainless steel substrates by high-velocity oxy-fuel (HVOF) spraying at oxygen pressures of 3.0, 3.5, and 4.0 bar. The influence of oxygen pressure on the phase composition, microstructure, porosity, corrosion behavior, thermal stability, and microhardness of the coatings was investigated using X-ray diffraction (XRD), scanning electron microscopy coupled with energy-dispersive spectroscopy (SEM/EDS), ImageJ porosity analysis, electrochemical corrosion testing in 3.5 wt.% NaCl solution, simultaneous thermal analysis (TGA/DSC), and microhardness measurements. XRD analysis revealed the formation of quasicrystalline-related intermetallic phases together with Al, Fe₃Al₁₃, FeAl, Fe₃O₄, CuFe₂O₄, Cu₂O, and CuO phases. The coating deposited at 3.5 bar exhibited the lowest porosity (5.37%), the most homogeneous microstructure, and the largest residual coating thickness after corrosion testing. SEM and EDS analyses indicated that corrosion preferentially initiated at pores, splat boundaries, and phase interfaces, while the coating produced at 3.5 bar demonstrated the most stable surface condition after exposure to a 3.5 wt.% NaCl solution. Thermal analysis showed that all coatings remained stable up to 900 °C. Sample (a) exhibited the lowest mass loss and the highest thermal stability, whereas sample (b) demonstrated the most favorable combination of structural integrity, phase ordering, coating density, corrosion-related performance, and thermal stability. Microhardness values of the coatings ranged from 754 to 778 HV, significantly exceeding that of the AISI 321 substrate. The results demonstrate that oxygen pressure is a critical parameter controlling the microstructure and functional properties of HVOF-sprayed Al-Cu-Fe coatings, with 3.5 bar providing the most balanced set of properties. Full article

Nanostructured catalysts have become a core component of energy conversion in electrocatalysis and photocatalysis; however, successfully translating their performance from laboratory scale to industrial applications remains a long-standing challenge. This paper provides a critical assessment of the field, systematically tracing the entire development trajectory from catalyst design to practical application. We focus on five major classes of catalysts—monometallic catalysts, bimetallic/multimetallic alloy catalysts, metal compound catalysts, carbon-based composite catalysts, and single-atom catalysts—and explore synthetic strategies for achieving precise structural control, including hydrothermal/solvothermal methods, electrodeposition, template-assisted and MOF-derived syntheses, high-temperature pyrolysis, and post-treatment defect engineering. This paper delves into the mechanisms and performance descriptors governing the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), urea oxidation, photocatalytic water splitting, and CO₂ reduction. Based on the above analysis, this paper lays the mechanistic foundation for five core strategies to improve catalyst performance: morphology control, elemental doping, heterostructure and interface engineering, defect and vacancy engineering, and support modification. Furthermore, this paper provides an in-depth evaluation of the applications of these catalysts in water splitting, CO₂ valorization, fuel cells, metal–air batteries, and energy-saving electrolysis, with a particular focus on earth-abundant alternatives to precious metals. We argue that in many well-studied reactions, intrinsic activity may no longer be the primary bottleneck restricting their development; instead, the core challenge now lies in maintaining excellent catalytic performance under harsh and industrially relevant conditions, especially under high-current densities, impurity-containing feed systems, and long-term operating conditions. In response to this shift in research focus, this paper clearly identifies the key obstacles hindering the industrial application of catalysts and proposes practical directions for future research. Full article

Water Distribution System (WDS) pipe failures are one of the most critical issues in WDS management. In order to identify them, a machine learning approach was applied to eight years of geolocated data on pipe failures to establish priorities for WDS rehabilitation. District-level characteristics, such as network length, pressures, materials, population density, and temperature, were combined with a specific failure rate to account for differences in network size. A cost-sensitive classification approach minimized false negatives, ensuring that high-risk areas were correctly flagged. Among all models analyzed the best performance was achieved by Naive Bayes, which reliably predicted priority districts for proactive maintenance, supporting pipeline renewal strategies. Full article

Radiation use efficiency (RUE) is closely associated with cotton biomass and yield, yet the synergistic regulation of phenotypic structure and physiological potential remains unclear. A field experiment (2024–2025) in Anyang, China, utilized three independent trials: six sowing dates (from 12 April to 12 May at 6-day intervals, S1–S6), six planting densities (1.5, 3.3, 5.1, 6.9, 8.7, and 10.5 × 10⁴ plants·ha⁻¹, D1–D6), and ten cultivars with distinct architectures (V1–V10). Feature importance and structural relationships were quantified via random forest (RF) and partial least squares structural equation modeling (PLS-SEM). Results indicated that delaying sowing reduced true leaf number (TLN) and plant height (PH), with the April 24 sowing (S3) optimizing leaf area index (LAI, 2.57) and light interception rate (iPAR, 0.61). Increasing density significantly enhanced population-level LAI, above-ground biomass, and RUE, despite a progressive decline in TLN. Among cultivars, CCRI 60 (V6) exhibited superior structural traits (PH: 72.94 cm; iPAR: 0.61), while CCRI 113 (V8) exhibited the highest maximum carboxylation rate (V_cmax, 88.9 μmol·m⁻²·s⁻¹) and RUE (4.88 g·MJ⁻¹). Across the comprehensive dataset (integrating the density, sowing date, and cultivar trials), iPAR exhibited the highest relative importance (42.01%) for RUE variation, while associated structural traits (PH, LAI, TLN) yielded a cumulative relative importance of 41.69%. RUE was strongly associated with biomass accumulation (path coefficient > 0.97), which subsequently optimized yield components. Conversely, within the cultivar-comparison subset, the relative importance of iPAR decreased to 17.95%, while V_cmax rose significantly to 19.20%. PLS-SEM indicated that canopy structure exerted a significant negative association with photosynthetic potential (V_cmax, J_max) within this cultivar subset (path coefficient ≈ −0.51), whereas enhanced physiological potential was positively associated with resource allocation to yield components (path coefficient ≈ 0.57). Consequently, mitigating the inherent trade-off between canopy structure and leaf photosynthetic capacity is critical for further improving RUE and cotton yield under similar production environments. Full article

Developing cost-effective alternatives to platinum-based catalysts remains paramount for commercializing anion exchange membrane fuel cells (AEMFCs). We report a metal-free nitrogen-doped carbon catalyst derived from a rationally designed polyurea–polyimine copolymer that outperforms commercial 20 wt% Pt/C in superior relative durability and methanol tolerance. Strategic integration of polyurea’s pore-forming capability with polyimine’s thermal stability enabled the synthesis of a catalyst (NC-1000N) featuring ultrahigh surface area (1276.5 m² g⁻¹), optimal nitrogen speciation (20.5% pyridinic-N, 45.3% graphitic-N), and enhanced graphitization, which improves the electrical conductivity of catalysts. NC-1000N exhibited exceptional oxygen reduction performance with an onset potential of 0.96 V, almost four-electron selectivity (n = 3.87), a medium Tafel slope (105 mV dec⁻¹), and minimal charge transfer resistance (46.74 Ω). When evaluated in single-cell AEMFCs, NC-1000N delivered a peak power density of 372.1 mW cm⁻², which is 26% higher than Pt/C at equivalent loading, while demonstrating superior stability (94.8% retention after 7 h) and complete methanol tolerance. Systematic pyrolysis temperature optimization (800–1000 °C) revealed critical structure–property relationships governing catalyst evolution from disordered precursor to highly graphitic, nitrogen-enriched carbon with precisely engineered active sites. This work establishes polymer-derived carbons and provides design principles for scalable synthesis of high-performance metal-free electrocatalysts for sustainable energy conversion technologies. Full article

Oil spills represent a critical environmental challenge. The wastewater treatment with porous sorbents presents the advantage of higher uptake and recyclability. In this work, highly porous and low-density three-dimensional reduced graphene oxide aerogels were obtained by hydrothermal reduction followed by lyophilization. The porosity and reduction degree of the aerogels were controlled by the addition of reducing species, namely ethylenediamine, and hydrothermal conditions. The aerogels were characterized using scanning electron microscopy, Raman spectroscopy, and energy-dispersive X-ray analysis. The sorption measurements were performed with vegetable oils, namely canola and olive oil, at varying operating temperatures. The morphological analysis revealed a well-defined porosity gradient along the aerogel length, along with a functionalization gradient. The sorption performance is highly dependent on their combined action. The maximum gravimetric absorption capacity was about 122 g g⁻¹ at room temperature, increasing to 156 g g⁻¹ at 60 °C, with the absorption rate increasing from about 1 g g⁻¹ s⁻¹ to 15 g g⁻¹ s⁻¹ within 10 s. These results demonstrate that anisotropic gradient aerogels could be obtained by simple tailoring of the synthesis conditions, and such aerogels could benefit the sorption of oils with higher viscosities in terms of rate, pore filling and retention. Full article

This paper studies the continuation problem for the three-dimensional compressible Navier–Stokes–Fourier system inside an admissible thermo-acoustic regime under periodic boundary conditions. The analysis considers strong solutions in the Sobolev class $H^s\left(\mathrm{\Omega }\right)$, $s>$ 5/2, with positive density and temperature and strict subsonic evolution. Using dyadic Fourier–triadic decomposition together with localized Littlewood–Paley analysis, the nonlinear transfer structure is decomposed into perturbative interaction classes and coherent same-scale High–High interactions. Within the present framework, coherent same-scale High–High persistence is identified as the only currently identified potentially nonperturbative concentration mechanism. A transport–acoustic alternative structure is then derived connecting persistent transport concentration with nonvanishing pressure response. The resulting pressure response is decomposed into thermodynamic and acoustic branches. The transonic acoustic branch is shown to be incompatible with the strict subsonic admissible class. The remaining interaction structure is controlled through entropy-driven thermodynamic dissipation and localized thermo-acoustic regularization. The exclusion of dynamically sustained critical thermo-acoustic concentration yields a localized ε-regularity framework combining thermodynamic dissipation, Campanato decay, and interior parabolic regularization. The resulting estimates provide localized Lipschitz control sufficient for the continuation of admissible strong solutions within the same thermo-acoustic class. The framework further remains compatible with weak–strong stability and irreversible long-time thermodynamic relaxation through the relative entropy structure and free-energy dissipation. Full article