Homojunction engineering in graphitic carbon nitride (g-C3N4) presents a viable approach to boost interface/bulk charge carrier separation for enhanced photocatalytic efficiency. Herein, an effective S-scheme homojunction (NA-DCN/NA-SCN) was constructed by compositing N-deficient g-C3N4 (NA-DCN) with S-doped g-C3N4 (NA-SCN) to mediate the interface/bulk carrier separation dynamic in g-C3N4. Structural characterizations and density functional theory (DFT) calculations confirmed the formation of S-scheme homojunction and strong interfacial electronic interactions between NA-DCN and NA-SCN. Photoluminescence and photoelectrochemical measurements demonstrated efficient directional interfacial charge transfer in NA-DCN/NA-SCN, with electrons migrating from NA-DCN and holes from NA-SCN, consistent with the S-scheme mechanism. Meanwhile, bulk separation of photogenerated carriers in NA-DCN and NA-SCN components of NA-DCN/NA-SCN was effectively promoted by N-defect engineering and S doping, respectively. Furthermore, NA-DCN/NA-SCN held the formation of mesoporous nanosheets with a high specific surface area (118.3 m2 g−1), facilitating interface/bulk carrier transfer as well as providing abundant surface-active sites. As a result, the optimized NA-DCN/NA-SCN with S-scheme homojunction exhibited exceptional photocatalytic H2-evolution activity (7607.4 μmol h−1 g−1) with a high apparent quantum yield (9.3% at 420 nm), and an excellent photocatalytic CO2-reduction performance for CO and CH4 production with CH4 electron conversion selectivity of 95.8% in pure water. This study highlights a feasible doping-coupled interfacial homojunction design strategy for regulating photogenerated carrier interface/bulk kinetics in g-C3N4, while offering promising guidelines for developing efficient g-C3N4-based photocatalytic systems.

The development of frameworks for large-scale energy construction zoning, considering geographical potential, is pivotal for achieving carbon neutrality goals. Existing wind power development environmental impact studies have predominantly focused on microscale analyses, lacking the incorporation of eco-environment factors into wind power development planning at a macro level. This study presented a hybrid evidence-integrated explainable AI framework for eco-suitability zoning prediction, combining a rule-based indicator prior with documented real-world anchors and four nonlinear learners. It introduced SHapley additive explanations and feature importance analysis to quantitatively elucidate the relationships between eco-environment factors and wind power suitability zoning and served as a link between mitigation and adaptation measures in the context of global climate change. Twenty suitability indicators were incorporated, and a quantitative evaluation method generated twenty raster data layers as inputs for subsequent models. Prediction accuracies for suitability zoning reached 93.36%, 96.50%, 97.17%, and 97.46% for XGBoost, LightGBM, CatBoost, and CNN, respectively. Notably, CatBoost demonstrated consistent performance across all scores, while CNN excelled in accurately identifying very unsuitable and very suitable zones. The explainable artificial intelligence revealed that desertification sensitivity and road proximity are the most influential factors in suitability zoning, whereas socio-economic indicators exert minimal impact. Applying the framework to the Qinghai-Tibetan Plateau revealed that 71.4% of the area is very unsuitable for wind farm construction, but the total geographical potential remains substantial at 1.90 × 106 GWh. These findings suggest prioritizing the development of the 11.32% highly suitable areas, following detailed micro-site assessments, to meet nationally determined contributions targets and effectively mitigate global warming.

The photocatalytic efficiency of graphitic carbon nitride (g-C3N4) is constrained by its inherent limitations of inefficient charge carrier transfer and a scarcity of active surface sites. To surmount these challenges, we herein report a rational design of g-C3N4 nanosheets featuring synchronous cyano-defects and (S, Na, B) co-doping (CNS-NaB). This synergistic modification enhances charge separation and provides abundant catalytic centers. Through comprehensive structural characterizations and density functional theory (DFT) calculations, we elucidate the distinct roles of each dopant in generating a synergistic effect. Specifically, Na & horbar;N coordination facilitates interlayer charges transfer, B & horbar;N bond optimizes the electronic properties, and S & horbar;C bond serves as active sites. Meanwhile, the cyano-defects work as electron acceptors and charge-transfer mediators. These synergy interactions spatially separate the LUMO and HOMO in CNS-NaB, inhibit the charge recombination, and reduce the exciton binding energy to 51.50 meV. As a result, CNS-NaB exhibits a CO2-to-CO conversion rate of 779.2 mu mol h(-1) g(-1), and enhanced H-2 evolution rate of 3637.9 mu mol h(-1) g(-1), with an apparent quantum yield of 15.6% at 432 nm. This study proves that element co-doping coupled with structural defect offers a good strategy to optimize the electronic properties, and the photocatalytic activity of g-C3N4 for solar-to-chemical conversion.

Atmospheric water harvesting (AWH) offers a promising solution to freshwater scarcity, yet conventional materials often suffer from poor multi-performance synergy. Here, we develop a hygroscopic gel (PAPNM) by polymerizing polyoxometalate (P5MoW29), acrylic acid (AA), and N-isopropylacrylamide (PNIPAM). Molecular dynamics and DFT simulations confirmed that water adsorption is enhanced by strong osmotic pressure and multiple hydrogen bonding interactions. It achieves a water uptake 21 times higher than pure PAA at 25% RH. And the material exhibits an adsorption capacity of 148 mg/g at 30% RH. Under low solar irradiation (0.6 kW m-2), the gel attains 65.3% water desorption without additional energy input. After 10 adsorption-desorption cycles, PAPNM still retains an adsorption capacity of 895 mg/g. This strategy thus presents a key strategy to overcome the multi-performance bottleneck in AWH materials, offering a scalable, energy-efficient solution for practical AWH technologies.

Snow cover in the Tibetan Plateau's (TP) interior basins is thin and ablates quickly, with a mean snow depth of 31.5 mm on snow days and a mean continuous snow days (CSD: new snow to complete ablation) of 2.6 days from in situ observations. To model this ephemeral snow in the TP, we applied a land surface model derived by optimized atmospheric forcing data. Simulation results show that snow depth was well simulated after using the observation-constrained air temperature and precipitation, with relative bias reducing from-39.1% to 1.1%. However, the best simulation still failed to capture most of the CSD events within 3 days and so significantly underestimated their proportion in all events. For example, the proportion of 1-day CSD events was observed at 58.5% while simulated at only ;20%. Furthermore, model simulations overestimated the mean CSD by approximately one time due to the predominance of overestimated short CSD (SCSD: <= 7 days), although medium CSD (MCSD: 8-31 days) and long CSD (LCSD: >31 days) were underestimated by 2.6 and 17 days, respectively. The LCSD underestimation was probably driven by excessive snowmelt due to overestimated maximum ground temperature (Tg), whereas MCSD/SCSD uncertainties stemmed from both overestimated snow depth and underestimated maximum Tg. Additionally, reducing snow-depth thresholds in model physical schemes can improve the underestimated maximum Tg during SCSD from-5.1 degrees to-2.1 degrees C, subsequently reducing mean SCSD overestimation from 2.6 to 1.0 days across 88 TP sites. These findings reveal the distribution patterns and model physical performances of snow duration on the TP, providing a reference for future snow model development.

The photocatalytic performance of graphitic carbon nitride (g-C3N4) is intrinsically limited by poor light absorption and rapid charge recombination due to the strong localization of photoexcited carriers within heptazine units. Herein, a dual-modification strategy involving synergistic carbon self-doping and controlled nitrogen defects is proposed to delocalize charge confinement within the heptazine units of g-C3N4. The resulting carbon self-doped, nitrogen-deficient ultrathin g-C3N4 nanosheets (CNuu-640) exhibit an extended light absorption edge up to 700 nm attributed to activated n -* it* electronic transitions, high specific surface area (150.5 m2 g- 1), and a hierarchical porous structure. Experimental and theoretical analyses reveal that nitrogen defects function as electron acceptors and catalytic active sites, while self-doped carbon atoms mediate charge separation and transfer properties by spatially decoupling the HOMO and LUMO sites. This synergistic effect breaks the charge confinement in heptazine units of CNuu-640, promoting directional carrier migration across the it-conjugated framework with prolonged carriers lifetime (t2 = 458.79 ps) and reduced exciton binding energy (48.37 meV) as supported by femtosecond transient absorption spectroscopy (fs-TAS) and temperature-dependent PL analyses. Consequently, CNuu-640 demonstrates an exceptional photocatalytic H2 evolution rate of 12,469 mu mol h- 1 g- 1 with an apparent quantum yield (AQY) of 27.8 % at 400 nm, and a remarkable CO2-to-CO conversion rate of 1918.5 mu mol h- 1 g- 1 with a CO selectivity of 88.7 %. This work demonstrates that simultaneous electronic structure and morphology optimization of g-C3N4 through synergistic carbon doping and nitrogen defects is an effective strategy for addressing its physicochemical properties and photocatalytic performance.

Compressed air energy storage (CAES) technology plays a crucial role in mitigating the volatility and intermittency of wind and photovoltaic (PV) power generation, thereby enhancing energy efficiency and system stability. This study proposes a novel load-oriented hybrid system integrating wind, PV and CAES, while investigating its capacity optimization and scheduling strategies. A multi-objective optimization model is developed to balance power curtailment, load power deficiency, and system investment costs, ensuring economic efficiency and operational reliability. The model incorporates wind and PV generation variability, the charging and discharging characteristics, power constraints and storage capacity of the CAES system. The weight coefficients for power curtailment rates, load power deficiency rates, and system investment costs are set to 0.25, 0.40, and 0.35, respectively. Using seasonal data from a region in China, the optimization results show different capacity needs for each season. Analyze these seasonal capacities to support the final configuration plan. The installed wind power capacity in winter (1853 MW) slightly exceeds that in summer (1834 MW), while PV capacity in winter (761 MW) is significantly lower than in summer (933 MW). The CAES power capacity in winter (305 MW) exceeds that in summer (218 MW), while the storage duration is 2.4 h in winter and 2.6 h in summer. The optimized system effectively utilizes the complementary characteristics of wind and solar power generation, reducing power curtailment and shortages, and lowering investment costs. This study provides an effective solution for integrating high wind and PV power shares into the grid.

The Qinghai-Tibet Plateau’s Gobi region is rich in solar energy resources, offering tremendous potential for utilization. Considering the fragile and highly sensitive ecosystem of this area, an evaluation method has been proposed to assess the coordinated relationship between photovoltaic generation systems and the ecological environment. A three-level evaluative index system including 23 specific factors for collaborative development is constructed. The weight of each evaluation factor is calculated through the analytic hierarchy process, in which the effect of each index factor on the coordinated development is preliminarily analyzed. The factors having the greatest and the least influence can be obtained. This work provides a comprehensive and systematic methodology exploration in utilizing the solar energy resource collaborated with the ecological environment in the Qinghai-Tibet Plateau area.

We study large eddy simulation (LES) in the form of vorticity transport equations (VTE), employing six subgrid-scale (SGS) models, including the dynamic Smagorinsky model, dynamic mixed model, velocity gradient model, scale similarity model, approximate deconvolution model, and dynamic iterative approximate deconvolution (DIAD) model. In the a priori study, the correlation coefficient of SGS vorticity stress given by DIAD is significantly higher than those of other structural models, and the relative error of DIAD is the lowest. In the a posteriori validation, structural models outperform functional models in predicting energy spectra, enstrophy spectra, and probability density functions (PDFs) of vorticity, strain-rate tensor, SGS enstrophy flux, and enstrophy production term. These results confirm the feasibility of VTE-based LES. They also indicate that the classic modeling approaches for the SGS terms in filtered Navier–Stokes equations (NSE) are also applicable to the SGS counterparts in filtered VTE.

We apply the direct deconvolution model (DDM) and discrete direct deconvolution model (D3M) to large-eddy simulation (LES) of compressible homogeneous isotropic turbulence, using Gaussian and Helmholtz filters. An information-preserving method is proposed. By performing a filtering operation, we extract the sub-filter scale (SFS) components of the physical quantity reconstructed by deconvolution models and add them to the original resolved large-scale field. The combined quantity is used to reconstruct SFS stress and SFS heat flux; thus, it exactly preserves the information of large scales. In a priori studies, the performance of D3M is significantly enhanced by the information-preserving scheme, with the relative error reduced by nearly 20%. The correlation coefficients of all deconvolution models are higher than 0.9. In the a posteriori validation, the DDM and D3M outperform the traditional models including the velocity gradient model, dynamic Smagorinsky model, and dynamic mixed model in the prediction of various statistical properties, including spectra of velocity and probability density functions of the normal and shear components of the normalized strain-rate tensor and SFS stress. The direct deconvolution approach for closing the LES equations requires no additional assumption, offering a significant potential for LES of compressible turbulence.

With the rapid integration of renewable energy sources, such as wind and solar, multiple types of energy storage technologies have been widely used to improve renewable energy generation and promote the development of sustainable energy systems. Energy storage can provide fast response and regulation capabilities, but multiple types of energy storage involve different energy conversion relationships. How to fully utilize the advantages of multiple energy storage and coordinate the multi-energy complementarity of multiple energy storage is the key to maintaining a stable operation of the power system. To this end, this paper proposes a robust optimization method for large-scale wind–solar storage systems considering hybrid storage multi-energy synergy. Firstly, the robust operation model of large-scale wind–solar storage systems considering hybrid energy storage is built. Secondly, the column constraint generation (CCG) algorithm is adopted to transform the original problem into a two-stage master problem and sub-problem for solving to obtain the optimal strategy of system operation with robustness. Finally, the validity of the proposed method is verified through case tests. The results show that the proposed method can effectively coordinate the multi-energy complementary and coordinated operation of multiple hybrid energy storage, and the obtained operation strategy of large-scale wind–solar storage systems can well balance the economy and robustness of the system.

Solar photovoltaic (PV) is one of the most environmental-friendly and promising resources for achieving carbon peak and neutrality targets. Despite their ecological fragility, China’s vast desert regions have become the most promising areas for PV plant development due to their extensive land area and relatively low utilization value. Artificial ecological measures in the PV plants can reduce the environmental damage caused by the construction activity and promote the ecological condition of fragile desert ecosystems, therefore yield both ecological and economic benefits. However, the understanding of the current status and ecological benefits of this approach in existing desert PV plants is limited. Here we surveyed 40 PV plants in northern China’s deserts to identify the ecological construction modes and their influencing factors. We quantified the ecosystem service value (ESV) provided by these PV plants using remote sensing data and estimated the potential for ESV enhancement. Our results show that PV plant construction in desert regions can significantly improve the ecosystem, even with natural restoration measures (M1) alone, resulting in a 74% increase in average fractional vegetation cover (FVC) during the growing season, although the maximum average FVC of only about 10%. The integrated mode M4, which combined artificial vegetation planting M2 and sand control measures M3, further enhance the average growing season FVC to 14.53%. Currently, 22.5% of plants lack ecological measures, 40% employ only a single measure, but 92% of new plants since 2017 have adopted at least one ecological construction mode. The main influencing factors include surface type, policy support, water resources, ecological construction costs, and scientific management guidance. If artificial ecological construction were incorporated, a significant ESV could be achieved in these PV plants, reaching $8.9 million (a 7.7-fold increase) if assuming a targeted 50% vegetation coverage. This study provides evidence for evaluating the ecological benefit and planning of large-scale PV farms in deserts.

Modified MIL-101(Al)-NH2 (MIL-101(Al)-NH2-net) is used as a crosslinking agent and acrylic acid (AA) as a monomer, the MIL-101(Al)-NH2-net grafted polyacrylic acid (MIL-101(Al)-NH2-net-g-PAA (MAP)) composite water-absorbent material is synthesized using the free radical polymerization method to study the application of composite absorbent materials in water collection. The structure and morphology of the composite water-absorbent material are characterized using Fourier transform infrared spectrometry (FTIR), scanning electron microscope (SEM), transmission electron microscope (TEM), Brunauer Emmett Teller (BET), and thermogravimetric analysis (TG), and the contents of ammonium persulfate (APS), MIL-101(Al)-NH2-net, and neutralization degree of AA are optimized. Under optimal conditions, the water absorption ratios of the composite materials in distilled water, tap water, and 0.9% NaCl solution are 744, 169.5, and 85.5 g g(-1). In addition, the properties of collecting water vapor are investigated. At 25 degrees C, 50% Relative Humidity (RH), 70% RH, and 90% RH, the MAP water vapor adsorption capacity is 0.0312, 0.5760, and 1.6856 g g(-1), respectively. The water vapor adsorption of MIL-101(Al)-NH2 and MIL-101(Al)-NH2 self-polymerization products are 0.025 and 0.1278 g g(-1), respectively. The result showed that autopolymerization can improve the water vapor adsorption performance.

Desert areas offer rich solar resources and low land use costs, ideal for large-scale new energy development. However, desert ecosystems are fragile, and large-scale photovoltaic (PV) power facilities pose ecological risks. Current assessments of PV plant sites in deserts lack consideration of wind-sand hazards and ecological impacts. In this study, we have developed a new large-scale photovoltaic (PV) site selection model that integrates the analytic hierarchy process with geographic information system technology, and applies it to the desert regions of China. The results show that the potential for large-scale PV power plants in China's deserts is significant, with 69.4 % of the region assessed as medium or higher. The most suitable area is 12.7 × 104 km2 (7.6 % of the overall study area), mainly centered in the Tibetan Plateau's Qaidam Basin Desert and the deserts of northern China, characterized by favorable solar resources, climate, and terrain. Across all regions, gravel deserts are recognized as more suitable for the construction of large-scale PV power projects than sandy deserts. Considering varying PV installation density scenarios with an installed capacity potential of 36.4-84.9 TW and system costs ranging from 10.0 to 33.5 trillion USD, the study estimates an annual solar power generation potential of 47-110 PWh which is 1.7-3.9 times the global electricity demand. Carbon emissions could be reduced by 26.8-62.6 gigatons annually, offsetting 73-170 % of global emissions. Covering just 4.8-11.5 % of China's desert area (8 × 104-19.4 × 104 km2) would meet the projected 2025 electricity needs of the country. This study lays the groundwork for spatial planning and benefit assessment of large-scale PV projects in desert regions, and reduces conflicts between PV plant construction and local ecosystem.

Abstract As the scale of the wind farm becomes bigger and the wind turbines are increasingly clustered, a large wind farm wake effect occurs. In this work, the initial distance where the large wind farm wake effect model starts to take effect is investigated analytically. The large wind farm wake effect is evaluated by constructing a large-scale wind farm with a regular arrangement of wind turbines similar to Horns Rev. The essential variables that influence the wind deficit trend of the large wind farm wake effect model are explored numerically. The wake deficit becomes more obvious as the surface roughness gets lower, the apparent turbine spacing gets smaller, or the thrust coefficient decreases. The wake deficits between the large wind farm wake effect model and the wake superposition of the modified Park model are compared. The transition distance, where the dominator of the wind deficit switches from the modified Park model to the large wind farm wake effect model, moves upstream along the incoming wind for the rougher surface or the lower thrust coefficient. In addition, for the similar resultant turbine spacing, the slightly staggered wind farm layout does not show an obvious influence on the wake deficit obtained by the large wind farm wake effect model.

Real-time Direct Normal Irradiance (DNI) prediction is crucial for reliable and economic operation of Concentrated photothermal Solar Power (CSP) system in arid desert areas. However, the stochastic characteristics of short-term multidimensional meteorological time series make intra-hour DNI prediction a challenging task. In this study, we have proposed a deep learning model called TLD, which is combined with topological features captured by Topology Data Analysis (TDA) and temporal features captured by LSTM to address this challenge. Experimental results demonstrated that TLD outperformed the five latest models (Ridge, RF, C_GRU, BiLSTM, and GBRT) on seven solar radiation datasets in arid desert areas. Further analysis revealed that the proportion of cloudy days is a key factor affecting the model's performance. To enhance the forecast ability of TLD, we developed a physics-informed hybrid model named TLDP based on TLD and a smart persistence model, which fully combines the DNI prediction ability of TLD under cloudy conditions and that of the smart persistence model under sunny conditions. Experimental results of eight datasets collected from real-world solar photothermal power stations indicated that TLDP outperformed existing models, which may lay a foundation for more economical and stable operation of CSP plants in arid desert areas.