Acta Energiae Solaris Sinica•2026•DOI: 10.19912/j.0254-0096.tynxb.202608_9730
Existing ultra-short-term wind power forecasting methods exhibit limited performance due to insufficient extraction of fluctuation information and inadequate analysis of evolution patterns. This paper proposes an ultra-short-term wind power forecasting method based on fluctuation continuation scenario identification. First, the coupling mechanism of wind power fluctuations under multiple turbulence processes is investigated, and historical wind power dynamics are decoupled into a combination of nonlinear and linear fluctuation components. A fluctuation continuation concept is introduced, and the future continuation scale of wind power fluctuations is derived from nonlinear and linear decoupling parameters, thereby classifying fluctuation continuation scenarios. A sparse neural network (SNN) oriented to high-dimensional sparse features is constructed to identify historical fluctuation continuation scenarios, and ultra-short-term power forecasting is conducted separately for each scenario. Validation using measured wind speed and power data from three wind farms shows that, compared with baseline models, the proposed model improves root mean square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) by at least 1.46%, 2.44%, and 14.67%, respectively, demonstrating superior accuracy and stability. The method addresses the limitations of signal decomposition techniques that lack physical interpretability and are sensitive to hyperparameters, and overcomes the high-dimensional sparsity challenges faced by traditional scenario identification models.
Acta Energiae Solaris Sinica•2026•DOI: 10.19912/j.0254-0096.tynxb.202608_9731
Fault diagnosis of wind turbine planetary gearboxes is severely constrained by the scarcity of high-quality fault data, as gearboxes operate predominantly in healthy states and automatic shutdowns prevent fault progression. This paper proposes a data-model jointly driven diagnosis method to address low diagnostic accuracy under limited fault samples. A high-fidelity lumped-parameter dynamic model of the planetary gearbox is constructed to generate pseudo-fault data, supplementing the training set. A domain-shared residual network feature extractor incorporating convolutional block attention modules extracts key physical features from both pseudo and measured data. Local maximum mean discrepancy aligns feature distributions at the fault-category level between pseudo and real fault data. A Kolmogorov-Arnold network module enhances the model's capacity to learn complex data relationships, enabling classification and identification of different fault types. Validation on a wind turbine planetary gearbox fault diagnosis test rig demonstrates that the proposed method achieves superior diagnostic performance under fault sample scarcity compared to classical methods. The framework offers an effective solution for known fault types, though identification of unknown and atypical faults remains a challenge for future work via open-set domain generalization.
Power Automation Equipment•2026•DOI: 10.16081/j.epae.20251130012
Parameter identification for electromagnetic transient (EMT) models of grid-connected photovoltaic (PV) systems suffers from weak identifiability of controller parameters when environmental, electrical, and controller parameters are optimized simultaneously. This paper proposes a two-stage identification framework that partitions parameters by physical meaning into an environmental/electrical set and a controller set. For the environmental/electrical set, a Sobol global sensitivity analysis based on variance decomposition screens key parameters. For the controller set, a dynamic response feature clustering method combined with an unsupervised screening strategy using an inter-cluster mean difference index reduces the parameter space. Differentiated fitness functions are constructed for each stage, and an improved quantum dung beetle optimization (IQDBO) algorithm incorporating quantum angle encoding and a stagnation perturbation mechanism performs the identification sequentially. Case studies demonstrate that the proposed method compresses the search space and improves controller parameter identifiability. Compared with particle swarm optimization (PSO) and grey wolf optimizer (GWO), the IQDBO-based method achieves superior identification accuracy and convergence stability. Environmental and electrical parameter identification errors remain below 1%, while controller parameter errors remain below 3%. The framework addresses the weak identifiability bottleneck in unified optimization and provides a practical pathway for EMT model calibration in PV grid-connected systems. Future work will extend the method to multiple operating conditions and noisy field data, and develop accelerated computation strategies.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3596-2
High-entropy alloys (HEAs) have shown great promise in the CO2 reduction reaction (CO2RR) due to their tunable composition and unique physical and chemical properties. However, the role of HEAs in CO2RR and the underlying reaction mechanism remain underexplored, particularly through in situ techniques. In this work, we investigate the mechanism of CO2 reduction on AuAgCuPdPt HEAs using in situ Raman spectroscopy and attenuated total reflectance Fourier-transform infrared (ATR-FTIR) spectroscopy to reveal key intermediates and reaction pathways. Our results demonstrate that within the potential window of −0.2 to −0.7 V vs. reversible hydrogen electrode (RHE), the AuAgCuPdPt HEAs efficiently reduce CO2 to CO, achieving a Faradaic efficiency (FE) for CO greater than 90%, with a peak FE of 96.5% at −0.3 V vs. RHE. The CO2− intermediate was observed at low potentials, revealing the reaction pathway in the CO2 reduction process. Additionally, in situ ATR-FTIR results suggest that the introduction of an appropriate amount of Pt metal not only promotes water dissociation to generate protonic hydrogen, but also facilitates the desorption of *CO intermediates. The kinetic isotope effect of hydrogen-deuterium (H-D) confirms that water dissociation acts as a key proton donor in CO2RR. Furthermore, the catalyst of AuAgCuPdPt HEAs was applied as cathodes in a Zn-CO2 battery, achieving 90.23% FE for CO and a power density of 3.474 mW cm−2. This study provides new insights into the mechanistic understanding of CO2 reduction and underscores the importance of in situ spectroscopic techniques for advancing the design of efficient electrocatalysts for CO2 conversion.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3789-x
Wearable sensors have attracted significant attention due to their superior sensitivity, safety, and adaptability compared with conventional detection technologies. However, developing sustainable sensing materials that combine excellent performance with environmental friendliness remains a significant challenge. In this study, Juncus effusus (JE), a natural fiber featuring a unique internal three-dimensional (3D) network structure, was employed as the substrate. Conductive polyaniline was loaded onto the JE structure to impart electrical conductivity, and Ecoflex encapsulation provided high elasticity. Based on this approach, a JE-based resistive flexible sensor (PHE-JE) was successfully fabricated. The PHE-JE sensor exhibits high stability under various strain conditions, along with excellent flexibility and durability. Moreover, benefiting from its complex 3D structure and synergistic material interactions, the PHE-JE sensor enables accurate detection of diverse motion types, showing promising potential for future wearable sensing applications.
SCIENCE CHINA Materials•2026•DOI: 10.1007/s40843-025-3773-7
Kesterite Cu2ZnSn(S,Se)4 (CZTSSe) solar cells suffer from significant open-circuit voltage (VOC) deficits due to severe interfacial and bulk recombination, restricting their power conversion efficiency (PCE) far below the Shockley-Queisser limit. This work proposes a low-temperature annealing strategy during ITO sputtering (SA) to synergistically address these challenges. The temperature applied during ITO sputtering not only improves the crystallinity, carrier concentration, and optical transmittance of the ITO layer but also promotes the diffusion of In from ITO into both CdS and CZTSSe layers. Consequently, lattice matching at the CZTSSe/CdS interface is optimized, enabling epitaxial growth. And a favorable ITO/In:CdS/In&Cd:CZTSSe structure with optimal band alignment is obtained. As a result, a champion device with a PCE of 14.29% was achieved. The SA-treating also enabled the CZTSSe solar cells to achieve the highest VOC reported to date, exceeding 590 mV. This underscores the essential role of SA processing in optimizing interface engineering and suppressing defects, thus promoting the development of low-cost, high-performance kesterite photovoltaics.
Chinese Journal of Environmental Engineering•2026•DOI: 10.12030/j.cjee.202506021
To elucidate the seasonal variation in cadmium (Cd) accumulation in ratoon rice and its relationship with root surface iron plaque, this study compared Cd concentrations in brown rice and the characteristics of iron plaque components (amorphous Fe, Am-Fe; crystalline Fe, Cry-Fe) between the main and ratoon crops of six rice varieties under different stubble heights. A field experiment was conducted in a Cd-contaminated paddy in Liuyang, Hunan (soil total Cd: 0.56 ± 0.06 mg·kg⁻¹). Ratoon crop treatments included low stubble (20 cm) and high stubble (60 cm). Brown rice Cd concentrations varied by variety, season, and stubble height. Low stubble generally increased brown rice Cd in the ratoon crop compared to high stubble; high stubble reduced Cd in most varieties relative to the main crop. Health risk assessment indicated that low stubble in the ratoon crop posed higher non-carcinogenic risk than the main crop and high stubble, while carcinogenic risks exceeded acceptable levels across all treatments. Iron plaque Am-Fe and Cry-Fe concentrations in the ratoon crop were generally lower than in the main crop, with Am-Fe consistently exceeding Cry-Fe. In the main crop, total Fe, Am-Fe, and Cry-Fe on root surfaces were significantly negatively correlated with brown rice Cd (P < 0.05), but correlations were not significant in the ratoon crop. High stubble reduced Cd accumulation and non-carcinogenic risk in most varieties, yet carcinogenic risk remained. Iron plaque significantly impeded Cd uptake in the main crop but its effect weakened in the ratoon crop. Selecting low-Cd-accumulating varieties and optimizing stubble height are key strategies for safe ratoon rice production in Cd-contaminated areas.
Journal of Fuel Chemistry and Technology•2026•DOI: 10.1016/S1872-5813(26)60653-6
Carbon deposition caused by mass transfer limitations is a key challenge for traditional microporous ZSM-5 zeolites in coal tar catalytic cracking. To address this, benzene was used as a model compound. Parent ZSM-5 (NL-ZSM-5) was modified with tetraethylammonium hydroxide (TEAOH) to prepare hierarchical ZSM-5 zeolites with different mesopore sizes. Characterization (XRD, FT-IR, BET, TEM) confirmed successful mesopore introduction via selective desilication while retaining the MFI structure. At TEAOH concentration of 0.4 mol/L (ZSM-5-C), total pore volume increased from 0.24 to 0.43 cm3/g, and Brønsted acid amount increased from 0.28 to 0.67 mmol/g, with improved acid site accessibility. Catalytic experiments and carbon deposition analysis showed that hierarchical pore structure inhibits coking via a synergistic effect of diffusion enhancement and adsorption-site regulation. The coke amount of ZSM-5-C was 4.0%, only one-third of that of NL-ZSM-5 (11.9%). Molecular dynamics simulations confirmed that the diffusion coefficient of benzene in a 3.0 nm mesopore model is an order of magnitude higher than in a 2.0 nm model. Adsorption capacity decreases with increasing mesopore size, shortening residence time. Increasing temperature enhances diffusion but exponentially intensifies surface condensation reactions (Arrhenius effect), which dominates coke formation; hierarchical pores mitigate this negative effect. This research provides a theoretical basis for designing high-efficiency, coke-resistant catalysts for coal tar conversion.
SCIENCE CHINA Materials•2025•DOI: 10.1007/s40843-025-3430-0
Electrocatalytic nitrate reduction to ammonia (NO3RR) offers a sustainable alternative to the Haber-Bosch process but is limited by insufficient atomic hydrogen (H*) supply and sluggish hydrogenation of oxynitride intermediates. This study constructs a NiCo-LDH/Cu2O heterojunction catalyst via a facile hydrothermal method, leveraging the strong nitrate adsorption of Cu2O and interfacial coupling with NiCo layered double hydroxides. By tuning the Ni/Co ratio, proton absorption behavior is modulated, achieving an ammonia yield of 0.382 mmol h−1 cm−2 and a Faraday efficiency of 80.4% at −0.3 V vs. RHE. Experimental results demonstrate that interfacial coupling induces optimal electronic effects, enhancing adsorption and activation of reaction intermediates, optimizing the reaction pathway, and suppressing the competing hydrogen evolution reaction. The NiCo-LDH/Cu2O system prevents nitrite accumulation and addresses the poor conductivity of LDH and low electron transfer rates. This work provides a feasible strategy for designing efficient, cost-effective NO3RR catalysts for sustainable ammonia synthesis.