Search Results (5,252)

Substantial advances have been made since the 1970s in reducing the environmental impacts of ammonia production. Renewable-driven electrochemical synthesis offers a promising pathway to decarbonize ammonia production. This review examines an integrated route in which hydrogen is generated photoelectrochemically under concentrated solar irradiation and subsequently used in electrochemical ammonia synthesis. Photoelectrochemical cells are fabricated by electrostatically depositing photosensitive particles onto cathodes to enhance light-driven hydrogen production. Hydrogen production rates and ammonia yield depend strongly on temperature and electrolyte composition. The synthesized hydrogen is fed into a molten salt electrochemical reactor that operates at atmospheric pressure and receives nitrogen from a dedicated supply. This combined solar–electrochemical approach can produce low-carbon ammonia with improved safety and reduced environmental impact, offering a scalable alternative to conventional processes. Full article

Numerous space missions are advancing our understanding of the origin and evolution of planetary bodies and the potential for the emergence of life throughout the Solar System and beyond. Investigations across the inner Solar System have revealed contrasting planetary environments: Venus offers insights into runaway greenhouse processes, while Mars remains a primary target for studying climate evolution, atmospheric loss, past water activity, and extinct life, with sample return missions planned in the next decade. Beyond the traditional habitable zone, attention has shifted to the icy moons of Jupiter and Saturn. Data from space missions have identified subsurface oceans and possibly active geology on moons such as Europa, Ganymede, Titan, and Enceladus, highlighting their astrobiological potential. Among others, Europa’s ocean, possibly interacting with a silicate mantle and sustained by tidal heating, Enceladus plumes and Titan’s complex organic chemistry make these worlds compelling targets. Current and upcoming missions will further explore these environments and refine our understanding of habitability. This work also emphasizes the importance of planetary protection to prevent biological contamination, particularly for sample return missions. Continued exploration, supported by international collaboration and technological innovation, will be essential to address engineering challenges and to expand our knowledge of potentially habitable environments across the Solar System. Full article

The present study investigates the application of electric resistive heating to photovoltaic (PV) panels, aimed at enabling their subsequent thermo-mechanical delamination. The key process parameters—namely current magnitude and applied voltage—required for direct electro-resistive heating are identified, and the process is experimentally demonstrated under laboratory conditions. The electric resistive heating of a composite photovoltaic panel, consisting of a solar cell layer (crystalline silicon, c-Si, with a metallic grid), a backsheet, and a glass layer, is analyzed in detail using a virtual model of a single-crystal silicon solar cell implemented as coupled electric-thermal analysis. The temperature dependence of the electrical resistance of the solar cell layer is experimentally measured, and exponential relationships are derived and subsequently incorporated into the numerical model. The virtual model results are validated, demonstrating that, for a given geometry and configuration of the conductive metallic grid (busbars and fingers), the electrical resistance of the semiconductor layer containing the p–n junction governs the temperature achieved during electro-resistive heating as a function of the applied current. Furthermore, results for the terminal current and voltage, current density in the busbars and fingers, electric field intensity, and the resulting temperature within the semiconductor layer of the single-crystal silicon solar cell are presented and analyzed. Full article

With the large-scale development of renewable energy such as wind, solar and ocean energy, the demand for energy storage is more urgent. Pumped hydro energy storage (PHES) is one of the fundamental solutions to the problem of intermittent supply of renewable energy. The large-capacity/low-head pumped hydro energy storage (LL-PHES) system with the use of tubular pump turbine is a beneficial extension of traditional PHES systems owing to large flow rate and cheaper civil structures. However, the continuous competition between the “static water pressure difference caused by gravity” and the “pressure increase caused by accelerated impeller rotation” leads to prominent instability in the start-up process of the LL-PHES system under pump conditions. An explicit coupling algorithm is proposed for analyzing the transient characteristics in the start-up process of the LL-PHES system under pump conditions. This algorithm is based on the idea of dimensional transformation, and performs 3D flow calculations and 2D rigid body dynamics equation solution in the pump domain and the flap gate domain, respectively. This algorithm avoids the problems of high computational cost and poor convergence that exist in existing fully three-dimensional coupling algorithms and ensures the efficiency of transient hydraulic characteristic calculation. A comprehensive analysis of the transient characteristics of the LL-PHES system during pump start-up process is conducted using the proposed new algorithm. The entire process of the increase in rotational speed, valve opening, flow rate, and the continuous evolution of blade surface pressure during the start-up process is quantitatively described. The amplitude and spectral characteristics of the alternating pressure on multiple blades are clarified. The evolution law of blade load during the stage of severe pressure fluctuations during the start-up process is explained. The load distribution characteristics of “high in the leading and trailing edge areas and low in the middle” in the blade stream direction is presented. The research results have a direct guiding role in improving the hydraulic design and enhancing the operational stability of LL-PHES systems. Full article

A solar-assisted thermochemical cycle to store concentrated solar energy in solid elemental sulfur via the reversible interconversion of sulfuric acid and sulfur is being developed within the SULPHURREAL project. This process enables long-term, transportable energy storage through internal recycling of sulfur oxides. A central objective is to integrate SO₂ capture and conversion in separation-friendly steps that support closed-loop operation with minimal additives and limited downstream purification, while remaining compatible with industrial sulfuric acid and sulfur feedstocks. The method presented in this paper can also be feasible for SO₂ removal from fossil fuels and industrial emissions. With this purpose, indirect SO₂ conversion via bisulfite disproportionation was investigated using elemental sulfur as a heterogeneous auto-catalyst. Batch tests were performed in a pressurized, Teflon-lined autoclave with concentrated bisulfite solutions (3 M) at 140–180 °C for 3 h. Sodium bisulfite showed no conversions at 140–160 °C, whereas sulfur auto-catalysis was observed at T ≥ 170 °C. Ammonium bisulfite was also evaluated as a separable SO₂-capture intermediate; due to thermal instability, operation was limited to 170 °C, where sulfur formation remained detectable. For loop closure, NH₃ and H₂SO₄ must be recovered from the produced sulfate. This was addressed by reacting (NH₄)₂SO₄ with metal oxides in a tubular furnace at 500 °C. The evolved NH₃ was trapped in acid and quantified by ion chromatography. Near-quantitative NH₃ recovery (≈92–98%) was achieved with MgO and ZnO, and the corresponding metal sulfates were identified by XRD. These results support integrated process development and motivate kinetic and mass-balance studies toward continuous operation and scale-up. Full article

The rapid expansion of solar photovoltaic (PV) deployment has led to increasing concerns regarding end-of-life module management and the sustainability of material supply chains, where waste volumes are projected to reach 3.3–5.6 million tons by 2045. This study evaluates the environmental and economic impact of advanced photovoltaic recycling in Germany, focusing on high-value material recovery from crystalline silicon modules. A Full Recovery of End-of-Life Photovoltaics (FRELP) pathway is developed, integrating light-pulse delamination and molten salt etching, and a comparative life cycle assessment and economic assessment framework is applied. The results indicate that advanced recycling achieves high recovery rates for silicon, silver, aluminum, copper and low-iron glass, yielding around €1174.88 per ton of panels recycled. Economic analysis shows that manufacturing PV modules from recycled materials reduces costs by approximately 60–77% compared to virgin material production, mainly due to avoided energy-intensive upstream processes. From an environmental perspective, the recycling-based pathway yields net benefits across impact categories, as avoided impacts from primary material extraction outweigh additional burdens associated with recycling. Overall, PV recycling in Europe is shown to be environmentally and economically favorable; however, technological maturity and policy constraints remain key barriers to large-scale implementation and a holistic overall recycling process, indicating the need for targeted policy support. Full article

While rapid digital transformation has significantly optimized sectors such as finance and e-commerce, maintenance management in industrial environments has historically received lower levels of technological and capital investment. This lag creates critical gaps in operational efficiency and asset longevity, particularly within renewable energy infrastructures where sustainability and resilience are paramount. Addressing this technological disparity is essential for minimizing ecological footprints and maximizing the viability of net-zero systems. This paper introduces an advanced multi-platform digital solution designed to optimize the operation and maintenance of renewable energy systems and smart infrastructures. The platform addresses traditional management gaps by implementing standardized protocols that integrate real-time remote monitoring, sensor networks, and cloud-based data acquisition. By centralizing historical and real-time data from solar, wind, and hybrid grids, it facilitates advanced analytics, such as predictive modeling of component degradation. Real-world validation across photovoltaic plants and wind farms demonstrates significant impacts: a 30% reduction in unplanned outages and a 20% to 25% decrease in operational and maintenance costs. The results confirm that digitalizing maintenance processes is a strategic pillar for the energy transition, aligning industrial performance with global low-carbon pathways. Full article

Limited access to clean water and reliable electricity infrastructure remains a major challenge in many remote regions of Indonesia, particularly for building-scale domestic use. Conventional water treatment systems are often constrained by high operational costs and dependence on grid power, highlighting the need for sustainable and autonomous infrastructure solutions. This study presents the design, development, and performance evaluation of an integrated solar-powered clean water treatment system for smart building applications in remote areas using a Research and Development (R&D) approach. The proposed system combines off-grid polycrystalline photovoltaic panels with a multi-stage water treatment process consisting of a floss (mud) filter, activated carbon filter, water hyacinth cellulose bio-filter, ultraviolet (UV) sterilization unit, storage tank, and an IoT-based real-time water quality monitoring system. System performance was evaluated through microbiological, physical, and chemical water quality testing, with monitoring conducted via Wi-Fi-enabled sensors connected to the Blynk platform. The results demonstrate substantial improvements in treated water quality. Escherichia coli and total coliform bacteria were eliminated (100% reduction). Total dissolved solids (TDSs) decreased from 450 mg/L to 218 mg/L (51.6%), and dissolved manganese was reduced from 30 mg/L to 0.01 mg/L (99.97%), while nitrate levels decreased by 50%. Water pH and temperature remained stable and within regulatory limits. All treated water parameters complied with national clean water standards for hygiene and sanitation. The system operated independently using solar energy and achieved a clean water production capacity of 1000–1500 L/day. These findings indicate that the proposed system is a feasible, cost-effective, and sustainable civil engineering solution for clean water infrastructure in remote building environments. Full article

Water is an indispensable resource for the survival of all living organisms on Earth. However, many coastal villages continue to face challenges in accessing potable water, particularly during extended droughts. This comprehensive study evaluates the implementation and performance of a solar desalination system that employs photovoltaic (PV) panels and a parabolic solar concentrator to meet clean water demand in a drought-prone area of Indonesia. The system harnesses both solar-generated electricity and thermal energy to power an advanced desalination apparatus, effectively converting seawater into safe drinking water. Over a rigorous 4-month testing period, the device maintained an average steam outlet temperature of 105.9 °C, enabling a direct single-stage evaporation and condensation desalination process. Under optimal sunlight conditions, the system produced 1500 mL of purified water every 30 min, resulting in a total daily output of approximately 12 L (1500 mL × 8 cycles over 4 h). Laboratory analysis revealed a decrease in pH from 8.0 in raw seawater to 6.8 in treated water after post-treatment pH adjustment, meeting established safety standards for human consumption. Electrical conductivity measurements fell from 40–50 mS/cm to 480–500 µS/cm, confirming substantial salt removal. These results demonstrate the system’s capacity to generate potable water using sustainable energy sources and support circular economy principles by repurposing renewable resources for water desalination in water-scarce environments. Full article

This study proposes a novel simulation-driven intelligent framework for the performance and reliability assessment of renewable energy-integrated pavement systems by unifying coupled multiphysics finite element modeling, structured dataset generation, and graph-based artificial intelligence within a single computational paradigm. The proposed pavement is formulated as a seven-layer multifunctional infrastructure system comprising the asphalt surface, intermediate binder, base layer, thermoelectric energy layer, piezoelectric insert zone, subbase, and subgrade soil, thereby enabling simultaneous consideration of structural load transfer, thermal gradient-driven energy harvesting, moisture-sensitive support behavior, and reliability-oriented performance interpretation. A three-dimensional thermo-hydro-mechanical Abaqus model was developed to simulate the concurrent effects of moving wheel load, solar heat flux, rainfall infiltration, and internal moisture diffusion, and it was subsequently used to construct an AI-ready dataset containing 6000 simulation cases and 68 variables spanning geometric, material, environmental, traffic, uncertainty, structural, thermal, hydraulic, renewable-energy, and probabilistic reliability descriptors. To preserve the physical hierarchy of the layered pavement within the learning process, a Layer-Coupled Reliability Graph Operator Network (LaRGO-Net) was proposed, in which pavement layers are represented as interacting graph nodes linked through adaptive interlayer coupling and optimized through multi-task, physics-aware, and coupling-consistent learning. Experimental evaluation across nine progressive configurations demonstrated a monotonic improvement from baseline dense and graph-convolution models to the full LaRGO-Net formulation. The final model achieved the best overall performance with mean RMSE = 0.040, mean MAE = 0.028, mean $R^2=0.994$, and reliability prediction accuracy characterized by F1 = 99.21 and AUC = 99.53. These results confirm that the proposed framework provides a highly accurate, physically interpretable, and reliability-aware surrogate for next-generation pavement systems capable of simultaneously supporting structural serviceability, renewable-energy functionality, and intelligent decision-making. Full article

Accurate forest height reference points are essential for large-scale forest canopy mapping and carbon stock estimation. Currently, spaceborne Light Detection and Ranging (LiDAR) systems, primarily GEDI and ICESat-2, serve as the main data sources for acquiring global forest height reference points. To ensure data quality, conventional processing often relies on strict physical parameter filtering, such as retaining only nighttime and strong (full power) beam observations, which considerably reduces the available data density. Moreover, gross errors caused by signal attenuation or solar background noise often remain, limiting the accuracy of subsequent spatial modeling. To address the trade-off between measurement accuracy and data density, this study proposes a physically constrained outlier filtering strategy for spaceborne LiDAR retrievals, assisted by a priori continuous canopy height model (CHM) products. Aiming to maximize data retention, this method introduces a morphologically consistent global continuous CHM (such as the 10 m Pauls CHM) as a prior spatial envelope. By calculating the local height difference distribution and applying a 1$\sigma$ adaptive truncation, outliers are effectively removed. Comparative validations in the Genhe (coniferous forest, China) and HARV (mixed broadleaf forest, USA) study areas indicate that: (1) traditional filtering results in a data loss of over 80% while yielding limited accuracy; (2) after relaxing the initial filtering conditions, the proposed strategy reduces the overall root mean square error (RMSE) of GEDI and ICESat-2 retrievals by 12.6% to 36.0%; (3) owing to the effective removal of gross errors, the conventionally discarded daytime and weak (or coverage) beam data achieve substantially reduced error levels, sometimes even lower than those of traditional nighttime strong beam observations. Consequently, the spatial density of high-quality reference points is increased by 1.5 to 4.4 times. This study demonstrates the application value of low signal-to-noise ratio (SNR) spaceborne observations and provides a practical approach for obtaining high-quality, high-density control points for large-scale forest structure mapping. Full article

Solar-driven interfacial water evaporation has been recognized as an effective measure to address freshwater scarcity. Photothermal materials lie at the core of this process and have been extensively studied. However, conventional carbon-based materials typically suffer from high thermal emissivity, leading to significant heat loss. Here, we report a tungsten carbide/carbon composite polyvinyl alcohol hydrogel evaporator (PWC) for solar-driven interfacial seawater evaporation. Specifically, a tungsten carbide/carbon (WC/C) composite was synthesized via a straightforward one-step molten salt coating method and exhibited a remarkable photothermal conversion efficiency of 67.1%, attributed to the plasmon resonance absorption effect of WC nanoparticles. When incorporated into a polyvinyl alcohol (PVA) hydrogel via a physical-chemical dual-crosslinking strategy, the resulting PWC evaporator achieved a high evaporation rate of 2.99 kg m⁻² h⁻¹ and a conversion efficiency of 90.9% in a 5 wt% NaCl solution under 1 kW m⁻² illumination. In addition, the evaporator can purify seawater and effectively remove a variety of organic dyes. This study provides a viable strategy for a sustainable freshwater supply. Full article

Currently, there is a trend in the energy sector towards the application of edge computing techniques to facilitate active monitoring of distribution networks. The adoption of this technique is crucial for applications involving distributed monitoring systems that require real-time data processing with low latency. An edge computing environment ensures an adequate response to two time-level response requirements. One for events that could trigger a serious problem in the distribution network, and a less demanding one for the management of energy. This article justifies and analyzes an architecture specifically designed to provide an adequate response to the two levels of time demand that set the procedure followed for the monitoring, storage and local diagnosis of several photovoltaic plants located on the same distribution network, with the aim of studying their joint production. One of the main contributions is related to the expansion of the capabilities of Phasor Measurement Units (PMUs) to monitor solar radiation or energy production perimeters by sector. The second major contribution is to guarantee the quality of the measurements and low latency in communications, using as a reference the IEEE C37.118-2011 synchrophasor standard in cooperation with the Time Sensitive Networking (TSN) synchronization protocol that guarantees simultaneity in distributed measurements. In short, a procedure is sought that allows a real-time response with the use of computing techniques very close to the origin of the measurements, guaranteeing exhaustive control from the moment the capture begins until the parameters are stored in a time series database. Full article

Satellite-based vegetation monitoring has evolved from mapping vegetation canopy properties at single points in time toward analysing time-resolved dynamics of vegetation traits and process-related variables. Retrieved traits and solar-induced chlorophyll fluorescence (SIF) are inherently defined by sensor-specific spatial resolution, temporal integration, and spectral response. Modifying these characteristics alters the retrieval problem itself: under nonlinear retrievals and heterogeneous landscapes, aggregation and retrieval are generally non-commutative, and error components scale differently with resolution. Consequently, increasing spatial, spectral, or temporal detail does not guarantee improved ecological accuracy; a phenomenon we term the resolution–accuracy paradox. These interacting processes define the effective scale of vegetation products, which may differ from nominal sensor resolution and governs the interpretation of retrieved vegetation traits. When products with differing resolutions or compositing strategies are combined, scale effects can induce systematic artefacts in spatial patterns and derived dynamic indicators that cannot be resolved through improved per-pixel accuracy alone. This review establishes a scale-aware conceptual framework that treats scale as an explicit diagnostic dimension linking observation characteristics, retrieval formulations, trait definitions, and aggregation operators. We analyse how scale interactions influence spatial patterns, temporal dynamics, disturbance signals, and multiresolution data fusion, and derive diagnostic principles, best-practice guidelines, and research priorities for the scale-consistent interpretation of vegetation trait dynamics and SIF-constrained productivity and stress indicators across spatial and temporal scales. Framed in the context of upcoming hyperspectral missions such as CHIME and FLEX, which increase spectral information content, robust interpretation of vegetation products requires scale-consistent analysis and uncertainty-aware processing. For practitioners, this implies that vegetation products should be interpreted, validated, and compared at their effective scale rather than assuming that a finer spatial, spectral, or temporal resolution necessarily yields more reliable ecological information. Full article

Solar filaments (and their limb counterparts, prominences) are critical tracers of the Sun’s magnetic topology and key precursors to coronal mass ejections (CMEs). Precise identification and continuous tracking of these features are essential for understanding solar eruptive mechanisms and improving space weather forecasting. This systematic review evaluates the evolution of automated detection methodologies, addressing the challenge of processing the exponentially growing volume of high-resolution solar observations. We identify deep learning architectures, particularly U-Net variants and Mask R-CNN, as the most promising current paradigms. Compared to traditional image processing, these data-driven models demonstrate superior robustness against noise and variable observing conditions, achieving high-precision segmentation (>90% accuracy) with sub-second inference speeds. This leap in computational efficiency and accuracy directly facilitates real-time operational monitoring and enables large-scale statistical analysis of filament evolution across solar cycles. We conclude that future breakthroughs lie in developing physics-informed AI and standardized benchmarks to bridge the gap between pixel-level segmentation and physical interpretation, ultimately creating detection systems that are both operationally reliable and scientifically meaningful. Full article