Search Results (5,427)

The development of sustainable encapsulation materials with tunable thermomechanical properties remains a critical challenge for photovoltaic reliability. Currently, the mainstream encapsulant for polycrystalline silicon solar cells is crosslinked EVA (Ethylene-Vinyl Acetate), which complicates the end-of-life recycling and reuse of modules. There is an urgent need to develop a novel encapsulant that combines excellent barrier properties with thermoplastic recyclability. Herein, we report a novel series of thermally decomposable CO₂-based thermoplastic polyurethane (PPC-TE) films engineered through the rational design of soft and hard segments. Utilizing polycarbonate diol (PPCDL) and polyether glycol (PEG) as soft segments, we systematically tailor material properties by modulating PEG-to-PPCDL ratios (5–20 wt%) and PEG molecular weights (1000–4000 g/mol). The optimized PPC-TE films exhibit excellent transmittance (>90%), adjustable glass transition temperature (T_g: 35.1 °C~11.6 °C), and remarkable mechanical adaptability (51~92 HA). The PPC-TE films exhibit water vapor permeability (WVP) as low as 14.8 g·mm·m⁻²·day⁻¹ and oxygen permeability (OP) of 4.13 cc·mm·m⁻² day⁻¹ at 15 wt% PEG content, surpassing commercial ethylene–vinyl acetate (EVA) encapsulants. Notably, these films demonstrate fully thermal decomposition above 350 °C, facilitating eco-friendly photovoltaic device recycling. Superior adhesion to glass substrates is evidenced by peel strengths up to 37 N/cm (PPC-TE2000-20) and the shrinkage rate is as low as 3%. This work contributes to improving the long-term stability of solar cells and has the potential for large-scale production. Full article

Metallurgical-grade silicon (MG-Si) is an important source material for many industrial applications, including the manufacture of alloys, solar photovoltaics, and electronics. The process to refine raw materials into MG-Si is energy-intensive, with the predominant method of submerged-arc furnaces requiring energy consumption of approximately 11–13 kWh/kg Si. Recent research has discussed promising methods for reducing the energy required for the silicon production process, including the use of molten-salt electrolysis (MSE), a technique that offers potential savings in energy consumption without requiring carbon inputs for the process. This paper presents a techno-economic study of a potential industrial-scale MSE plant for MG-Si production to evaluate the trade-offs between capital and operating costs of the system. Capital costs are sourced from recent MG-Si plants and an existing cost model developed for MSE processes that includes the size of the plant and the operating temperature among its inputs. The results show that MSE technology has the potential to be an economically cost-competitive option for MG-Si production if the technology successfully scales to industrial production and matures enough to allow for financing costs similar to that of a comparably sized submerged-arc furnace plant. Full article

Hydrogen is a clean energy carrier with broad application potential. This study focuses on improving hydrogen production efficiency in a proton exchange membrane (PEM) electrolyzer system that integrates a photovoltaic (PV) array, a battery energy storage system, and the electrolyzer. The PV array is interfaced with the electrolyzer through a buck converter using a maximum power point tracking (MPPT) algorithm to ensure maximum energy harvesting. A key contribution of this work is the integration of a battery system through a dual-active-bridge (DAB) converter. The DAB converter employs a multilayer perceptron (MLP) model to dynamically regulate the electrolyzer current and maintain optimal operating efficiency. An adaptive energy management strategy is further proposed to address solar irradiance fluctuations and enhance long-term operational stability. The MLP model is developed in Python and embedded into a PLECS simulation environment. The simulation results verify the effectiveness of the proposed control approach and efficiency optimization scheme. Throughout the simulation period, the PEM electrolyzer sustains an optimal efficiency of 69.9% under maximum PV power output. A limitation of this study is that the efficiency model is derived from the literature and does not yet consider all operational factors, indicating the need for refinement in future work. Full article

Porous, crystalline gamma-Al₂O₃ coatings with a thickness of (6 ± 0.5) μm and a uniform distribution of rare earth (RE) dopants are synthesized by plasma electrolytic oxidation of aluminum at a current density of 150 mA/cm² in a boric acid and borax (BB) solution containing added RE oxide particles (Ho₂O₃, Tb₄O₇, and Pr₆O₁₁) at concentrations of 1, 2, and 4 g/L. The concentration of RE oxide particles in the BB solution determines the amount of RE elements incorporated into the coatings but does not significantly affect their surface morphology, crystal structure, or light absorption properties. The coatings exhibit high absorption in the middle/near-ultraviolet region, characteristic of Al₂O₃. Typical 4f-4f transitions of Ho³⁺, Tb³⁺, and Pr³⁺ are observed in the photoluminescence spectra. Photocatalytic evaluations using methyl orange degradation under simulated solar irradiation show that RE doping significantly enhances photocatalytic efficiency. Peak degradation efficiencies are achieved at a concentration of 4 g/L for all RE oxides. After 8 h of irradiation, maximum degradation reaches 88%, 92%, and 85% with pseudo-first-order rate constants (k_app) of about 0.274 h⁻¹, 0.339 h⁻¹, and 0.232 h⁻¹ for coatings synthesized in BB with 4 g/L Ho₂O₃, Tb₄O₇, or Pr₆O₁₁, respectively. In comparison, the pristine Al₂O₃ coating achieves only about 50% degradation (k_app ≈ 0.087 h⁻¹). Photoluminescence indicates that RE³⁺ ions serve as effective charge-carrier traps, suppressing electron–hole pair recombination. RE-doped Al₂O₃ coatings demonstrate exceptional structural stability and reusability over six cycles, highlighting their potential for sustainable wastewater remediation. Full article

Nongovernmental organizations (NGOs) play an important role in the interface between business, government and society, including serving as a link between diverse stakeholders, amplifying public visibility, and serving as a watch dog. This research seeks to understand the involvement and experience of environmental NGO (ENGO) staff members in the environmental planning of utility-scale wind and solar projects. We conducted 19 one-hour interviews with individuals representing 13 ENGOs which were located in or had projects within North Dakota, South Dakota, Nebraska, Kansas, Iowa, Oklahoma, and Texas. We found that, overall, engagement with the renewable energy industry was mixed, with some organizations being very involved and others having limited to no engagement. Participants also shared positive as well as more challenging engagement experiences they have had. Overall, ENGOs see a number of potential opportunities to engage more in renewable energy planning, particularly in collaboration with renewable energy developers, to move renewable energy deployment forward while balancing land use and environmental concerns. Full article

In this paper we present a hybrid convolutional neural network–hidden Markov model framework for detecting solar flare events of intensity greater than or equal to M1.0 from very low frequency signals via their induced sudden ionospheric disturbances. The convolutional neural network processes fixed-length windows of raw very low frequency signals and their temporal derivatives to produce probabilistic flare estimates, which serve as emission probabilities for a two-state hidden Markov model. Viterbi decoding enforces temporal consistency, suppressing spurious fluctuations and yielding physically plausible event sequences. The approach is specifically designed to detect the onset-to-peak interval of flare events and, with further development, could operate in real time for early flare warning. The model was trained and evaluated on very low frequency data from the DHO38 transmitter in Germany to a receiver near Birr, Ireland. Sample-level evaluation achieved a balanced accuracy of 0.819 and a Matthews correlation coefficient of 0.529, while event-level detection reached a peak F1-score of 0.558 for moderate-to-strong flares of intensity greater than or equal to C6.0. These results demonstrate automated, physically consistent detection of solar flares based on sudden ionospheric disturbances, indicating the potential of the proposed approach, when combined across multiple receivers, to act as a low-cost complement to satellite-based monitoring. Full article

The weak-field, quasi-static regime of gravity is commonly described by the Newton–Poisson equation as an effective response law. We construct this response within a cost-first discrete variational framework. The Recognition Composition Law (RCL) uniquely selects a reciprocal closure cost within the restricted quadratic symmetric composition class; together with the discrete ledger axioms AX1–AX5 (including conservation) and standard DEC refinement, the Newton–Poisson baseline is then recovered in the instantaneous-closure limit. Conditional on Assumption AS1 (scale-free latency) and Assumption AS2 (causal frequency–wavenumber ansatz), allowing finite equilibration introduces fractional memory into the response, yielding a scale-free modification of the source–potential relation characterized by a power-law kernel $w_{\ker }\left(k\right)=1+C{(k_0/k)}^{\alpha }$ in Fourier space. The kernel exponent $\alpha =\frac{1}{2}(1-{\phi }^{-1})\approx 0.191$, where $\phi =(1+\sqrt{5})/2$, is derived from self-similarity of the discrete ledger closure; the amplitude $C={\phi }^{-2}\approx 0.382$ is identified as a hypothesis from a three-channel factorization argument. We evaluate this quasi-static kernel-motivated response against SPARC galaxy rotation curves under a strict global-only protocol (fixed $M/L=1$, no per-galaxy tuning, conservative ${\sigma }_{\mathrm{tot}}$), using a controlled multiplicative surrogate for the full nonlocal disk operator implied by the kernel. In this deliberately over-constrained setting, the surrogate interface achieves $\mathrm{median}({\chi }^2/N)=3.06$ over 147 galaxies (2933 points), outperforming a strict global-only NFW benchmark and remaining less efficient than MOND under identical constraints. The analysis is restricted to the non-relativistic, quasi-static sector and should be read as a falsifier-oriented galactic-regime consistency check of the scaling window, not as a relativistic completion or a claim of Solar System viability without additional UV regularization/screening. Full article

Designing sustainable pedestrian infrastructure in hyper-arid cultural landscapes requires balancing visitor experience, heritage protection, and environmental constraints. This study develops a statistically grounded model for planning sustainable walking trails in Al-Ula, Saudi Arabia, using multi-spectral remote sensing data integrated with expert-based evaluation. A GIS-based Multi-Criteria Decision-Making (MCDM) framework was applied to assess topographic slope, vegetation cover (NDVI), built-up density (NDBI), Land Surface Temperature (LST), and solar exposure. Indicator weights were validated through a three-round Delphi survey involving fifteen experts. The results indicate strong consensus among experts, identifying LST (21%) and slope (20%) as the most influential determinants of trail suitability in desert environments. These findings highlight the critical role of thermal stress in shaping safe and sustainable pedestrian mobility in hot climates. The optimized 44.5 km trail network, classified into three difficulty levels, improves energetic efficiency by reducing caloric expenditure by 24% compared to conventional routing. In addition, the proposed network has the potential to reduce carbon emissions associated with heritage-related travel by approximately 75% through modal shift from vehicles to walking. The framework provides a practical decision-support tool for planners seeking to develop low-carbon, climate-responsive tourism infrastructure aligned with the objectives of Saudi Arabia’s Vision 2030. Full article

The building façade serves as the primary interface between the built environment and external climate, marking the transition from static regulation to dynamic response in climate-adaptive design. While existing research predominantly addresses periodic climatic elements such as temperature and solar radiation, the highly stochastic wind environment and its potential for internal acoustic problems remain systematically unexplored. This study investigates the acoustic modulation mechanism of building façades under dynamic wind conditions through a simulation-based methodology. The primary aim is to demonstrate the use of active control to mitigate the influence of fluctuating wind on the internal acoustic environment of buildings with open windows or semi-open boundaries, focusing on the coupling between stochastic wind fields and architectural acoustics in humid subtropical climates. We propose a wind-responsive adaptive acoustic façade system employing fractal geometry and configurable delay strategies, and develop a high-fidelity simulation framework to quantify how façade geometry and activation logic regulate acoustic parameters under varying wind conditions (1–8 m/s). Results indicate that: (1) support vector regression-based mapping of wind speed to delay strategies maintains key sound-field parameters (Lateral Fraction (LF), Speech Clarity (C₅₀), and Early Decay Time to Reverberation Time ratio (EDT/RT₃₀)) within 10% fluctuation across wind regimes; (2) fractal configurations achieve balanced wide-band (125 Hz–8 kHz) performance, with SPL fluctuation <3 dB, spectral tilt (+0.3 dB), and reverberation time slope <0.3; (3) configurational switching between column (high LF) and row (high C₅₀) arrangements enables dynamic trade-off between spatial impression and speech clarity. This work establishes an integrated framework coupling wind dynamics, façade morphology, and acoustic modulation to regulate objective indoor acoustic parameters. Based on the simulated omnidirectional point-source model, the results show that key acoustic indicators remain stable across varying wind conditions, providing a theoretical and quantifiable basis for climate-responsive acoustic envelope design. Future work will include empirical prototype testing and listening tests to determine whether these simulated acoustic parameters translate into improved comfort and well-being for occupants. Full article

This study presents a comprehensive flavour profile analysis of 12 Morchella samples (5 cultivated and 7 wild species) collected from diverse regions across China. The contents of free amino acids and volatile organic compounds were determined using UHPLC-QE-HRMS and HS-SPME-GC-MS. Flavour contribution was assessed by calculating taste activity values (TAVs) and relative odor activity values (rOAVs), and the influence of environmental factors on flavour compound accumulation was further explored. The findings indicated that cultivated Morchella exhibited pronounced fruity, floral, sweet, and mushroom-like notes (e.g., 1-octen-3-one, beta-damascone, and 1-(2-aminophenyl)ethanone), rendering them suitable for fresh consumption. In contrast, wild Morchella exhibited higher levels of herbaceous and smoky aroma compounds (e.g., (E,Z)-2,6-nonadienal, benzenemethanethiol, and non-8-enal), suggesting potential for premium product development. Correlation analysis revealed metabolic associations between taste-active amino acids and key volatile organic compounds via intermediates of the lipoxygenase pathway and the tricarboxylic acid cycle. Furthermore, environmental parameters including elevation, annual precipitation, and solar radiation were found to significantly influence the accumulation of flavour-related metabolites. These findings provide insights into the chemical basis underlying the flavour diversity of Morchella and offer a theoretical foundation for species identification, flavour-directed breeding, and differentiated product development. Full article

This study empirically isolates the marginal CO₂ abatement efficiency of wind and solar power within the Greek electricity system, utilizing hourly dispatch data from August 2012 to December 2018—a period characterizing the grid’s “pre-saturation” technical potential. By employing an econometric framework to capture ex-post displacement dynamics, we identify a statistically significant but highly heterogeneous abatement impact across renewable technologies. Our analysis reveals that wind power consistently achieves higher carbon savings per MWh than solar photovoltaics, primarily by driving deeper displacement of carbon-intensive thermal baseload. Conversely, solar generation exhibits a stronger propensity to displace zero-carbon hydroelectric output and net imports, thereby dampening its domestic abatement efficiency. Furthermore, we demonstrate that the marginal emissions avoided are non-linear, fluctuating significantly with system load, interconnection flows, and renewable penetration levels. These findings establish an “unconstrained efficiency” benchmark for the Greek grid, providing the necessary counterfactual to evaluate the diminishing returns and curtailment penalties characterizing the high-penetration era of renewables. Full article

Activating PIK3CA mutations occur in approximately 40% of hormone receptor-positive (HR+)/HER2-negative breast cancers and represent a major driver of endocrine resistance. The PI3Kα-selective inhibitor alpelisib, in combination with fulvestrant, significantly improves progression-free survival in patients with PIK3CA-mutant disease, as demonstrated in the SOLAR-1 trial. However, this therapeutic strategy is frequently complicated by treatment-induced hyperglycemia, a metabolic disturbance that promotes oxidative stress, mitochondrial dysfunction, and inflammatory signaling, thereby increasing cardiovascular vulnerability. Sodium–glucose cotransporter-2 (SGLT2) inhibitors have emerged as cardiometabolic modulators with benefits extending beyond glucose lowering. In this study, we used a human cardiomyocyte in vitro model designed to recapitulate the hyperglycemic metabolic milieu observed in breast cancer patients receiving PI3Kα-targeted therapy, to investigate whether the SGLT2 inhibitor dapagliflozin directly protects cardiomyocytes from alpelisib- and fulvestrant-induced injury. Human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) were cultured under hyperglycemic conditions (25 mM glucose) to mimic the metabolic environment associated with PI3Kα inhibitor-induced dysglycemia. Cells were exposed to alpelisib (100 nM) and fulvestrant (100 nM), alone or in combination, in the absence or presence of dapagliflozin (1 μM). Cardiomyocyte viability was assessed using the MTS assay, mitochondrial function by TMRM-based mitochondrial membrane potential (ΔΨm) measurements, and apoptosis by caspase-3 quantification. Cardiomyocyte injury was evaluated by release of cardiac troponin I and heart-type fatty acid binding protein (H-FABP). Lipid peroxidation markers (MDA and 4-HNE) were measured to assess oxidative membrane damage. Intracellular inflammasome-related signaling (NLRP3 and MyD88) and secreted inflammatory mediators (IL-1β, IL-18, IL-6, TNF-α, and CCL2) were quantified by ELISA. Exposure to alpelisib, particularly in combination with fulvestrant, significantly reduced cardiomyocyte viability, induced mitochondrial depolarization, and increased caspase-3-mediated apoptotic signaling. These alterations were accompanied by elevated lipid peroxidation (MDA and 4-HNE) and increased release of cardiac injury biomarkers (troponin I and H-FABP). Alpelisib-based treatments also activated inflammasome-related signaling, as indicated by increased intracellular NLRP3 and MyD88 levels and enhanced secretion of pro-inflammatory mediators (IL-1β, IL-18, IL-6, TNF-α, and CCL2). Co-treatment with dapagliflozin significantly attenuated these alterations, preserving mitochondrial membrane potential, reducing apoptotic signaling, limiting oxidative membrane damage, and suppressing inflammatory cytokine release. This study provides evidence that alpelisib-based therapy under hyperglycemic conditions is associated with oxidative, mitochondrial, and inflammatory stress responses in human cardiomyocytes, recapitulating key features of cardiometabolic stress relevant to PI3Kα-targeted therapy. Importantly, dapagliflozin markedly attenuated these alterations, supporting a potential cardioprotective role that may extend beyond glycemic control. These findings provide a mechanistic rationale for further investigation of SGLT2 inhibition as a cardiometabolic protective strategy in patients receiving PI3Kα inhibitor-based cancer therapy. Full article

Opuntia ficus-indica (L.) Mill., also named Cactus pear, is a crop widespread in many countries with Mediterranean and subtropical climates, where it represents a valuable source of food. However, in southern Europe, this fruit market is limited to a few months, from summer to autumn. The possibility to extend the ripening period of fruit is represented by the special pruning of the first bloom flush and consequent new development of late flowers and fruits. Extending the cultivation period would allow farmers to maximize the crop’s potential, thereby extending the Cactus pear market season throughout much of the year. In this study, conducted in southern Sardinia (Italy), progressive pruning was applied with the aim of evaluating the fruit characteristics in relation to this type of cultivation, also considering the weather conditions during the experimental period. Morphological traits and physicochemical compositions of fruit picked in four harvests during two sampling seasons from August 2022 to March 2023, and from August 2023 to March 2024 were compared. According to principal component analysis (PCA), most of the observed characters showed significant differences among harvest periods but also between the two seasons of cultivation (year of cultivation: r = 0.722 on PC1), suggesting that the meteorological trend strongly modulated fruit traits. Some fruit qualities were partially lost during the winter months, such as juice acidity and total soluble solids (TSS). October was the month with the highest TSS levels (13.5 ± 0.25), followed by August, January and March. On the other hand, juiciness and fresh weight remained unchanged or even improved in fruit harvested out-of-season. As observed in the redundancy analysis (RDA) a contribution of 54% due to weather variability emerged. In Particular, TSS levels, pH and juice dry matter were associated with high temperatures, solar radiation, and wind intensity. Wind speed was also moderately linked with betalain content. Moreover, high relative humidity was associated with lower pH values, higher water content, and higher fruit fresh weight. A significant difference was found between the two years in betalains content (80.0 ± 3.7 µg·mL⁻¹ in 2022–2023 and 28.2 ± 2.5 µg·mL⁻¹ in 2023–2024). The breakdown in the 2023–2024 season was likely due to the strong heat wave of July 2023 (up to 47 °C), which caused their partial degradation. In light of seasonal variability, this work provides some useful insights for future management of Cactus pear, also considering the possibility of usefully extending the period of cultivation and harvesting. Full article

Bhutan’s hydropower-reliant electricity supply faces seasonal imbalances, with a winter deficit prompting costly imports from India at tariffs of up to $0.09/kWh. Despite the estimated solar potential of 12 GW, PV deployment remains limited. This study presents a demand-driven techno-economic assessment of a 150.8 kWp rooftop PV system for the Ministry of Infrastructure and Transport using high-resolution hourly load data and PVsyst simulation. Three operational configurations are evaluated: self-consumption without export, self-consumption with export, and a battery energy storage system (BESS) introduced to mitigate curtailed energy. The system is expected to generate 252 MWh annually, achieving self-sufficiency and Self-Consumption Ratios of around 60%. Without export, the performance ratio (PR) is reduced to 51% due to significant curtailment, resulting in a negative Net Present Value (NPV) of −$33,687.5 and a Levelized Cost of Electricity (LCOE) of $0.0682/kWh. Enabling export raises the PR to 85.62%, improving the NPV to $27,965.42, the Internal Rate of Return (IRR) to 8.07%, and the LCOE to $0.0405/kWh. A 200 kWh BESS, sized based on surplus energy and nighttime demand, increases self-consumption and self-sufficiency to 75% and 73%, respectively. However, the LCOE rises to $0.0841/kWh, limiting economic viability under current tariff structures. The results reveal a structural mismatch between prosumer-level economics and system-level benefits, underscoring a need for improved compensation and targeted policy support in Bhutan and similar hydropower-dependent systems. Full article

This study addresses the automatic classification of cells in electroluminescent panel images to detect photovoltaic cell defects. The images used in the study were obtained from a solar panel production line. An original dataset consisting of 37,538 cell images with eight defect classes (Cell-Interconnection, Electrically Insulated Cell Parts, Finger Defect, Material, Microcrack, Multi-Defect, Normal, Visual) was prepared by applying RLSA-based automated cell segmentation enhanced with morphological processing to the photovoltaic panel images. A novel CNN–Transformer model with a self-attention mechanism, called PVELNet, is proposed for classifying defect types. Experimental studies were conducted with 16 deep learning models to compare the proposed model. F1-Score, Precision, Recall, and Accuracy evaluation metrics were used in the experimental study. Furthermore, the Confusion Matrix results obtained from the 16 deep learning models and the proposed PVELNet model are presented. The results were obtained using a relatively balanced dataset prepared for this study. PVELNet achieved 95.71% accuracy, outperforming other models. With 1.79 million parameters and a memory requirement of 46.1 MB, the PVELNet model is relatively lightweight. As a result, it demonstrates the potential to control processes on actual solar panel production lines. Full article