Search Results (1,286)

High-resolution CO₂ fossil fuel emission data are critical for developing targeted mitigation policies. As a key approach for estimating spatial distributions of CO₂ emissions, top–down methods typically rely upon spatial proxies to disaggregate administrative-level emission to finer spatial scales. However, conventional linear regression models may fail to capture complex non-linear relationships between proxies and emissions. Furthermore, methods relying on nighttime light data are mostly inadequate in representing emissions for both industrial and rural zones. To address these limitations, this study developed a multiple proxy framework integrating nighttime light, points of interest (POIs), population, road networks, and impervious surface area data. Seven machine learning algorithms—Extra-Trees, Random Forest, XGBoost, CatBoost, Gradient Boosting Decision Trees, LightGBM, and Support Vector Regression—were comprehensively incorporated to estimate high-resolution CO₂ fossil fuel emissions. Comprehensive evaluation revealed that the multiple proxy Extra-Trees model significantly outperformed the single-proxy nighttime light linear regression model at the county scale, achieving R² = 0.96 (RMSE = 0.52 MtCO₂) in cross-validation and R² = 0.92 (RMSE = 0.54 MtCO₂) on the independent test set. Feature importance analysis identified brightness of nighttime light (40.70%) and heavy industrial density (21.11%) as the most critical spatial proxies. The proposed approach also showed strong spatial consistency with the Multi-resolution Emission Inventory for China, exhibiting correlation coefficients of 0.82–0.84. This study demonstrates that integrating local multiple proxy data with machine learning corrects spatial biases inherent in traditional top–down approaches, establishing a transferable framework for high-resolution emissions mapping. Full article

Traditional energy harvesting systems, such as photovoltaics and wind power, often rely on external environmental conditions and are typically associated with contact-based vibration wear and bulky structures. This study introduces light-fueled self-vibration to propose a self-harvesting system, consisting of liquid crystal elastomer fibers, two resistors, and two piezoelectric cantilever beams arranged symmetrically. Based on the photothermal temperature evolution, we derive the governing equations of the liquid crystal elastomer fiber–piezoelectric beam system. Two distinct states, namely a self-harvesting state and a static state, are revealed through numerical simulations. The self-oscillation results from light-induced cyclic contraction of the liquid crystal elastomer fibers, driving beam bending, stress generation in the piezoelectric layer, and voltage output. Additionally, the effects of various system parameters on amplitude, frequency, voltage, and power are analyzed in detail. Unlike traditional vibration energy harvesters, this light-fueled self-harvesting system features a compact structure, flexible installation, and ensures continuous and stable energy output. Furthermore, by coupling the light-responsive LCE fibers with piezoelectric transduction, the system provides a non-contact actuation mechanism that enhances durability and broadens potential application scenarios. Full article

The ongoing energy transition and decarbonization efforts have prompted the development of Hybrid Renewable Energy Systems (HRES) capable of integrating multiple generation and storage technologies to enhance energy autonomy. Among the available options, hydrogen has emerged as a versatile energy carrier, yet most studies have focused either on stationary applications or on mobility, seldom addressing their integration withing a single framework. In particular, the potential of Metal Hydride (MH) tanks remains largely underexplored in the context of sector coupling, where the same storage unit can simultaneously sustain household demand and provide in-house refueling for light-duty fuel-cell vehicles. This study presents the design and analysis of a residential-scale HRES that combines photovoltaic generation, a PEM electrolyzer, a lithium-ion battery and MH storage intended for direct integration with a fuel-cell electric microcar. A fully dynamic numerical model was developed to evaluate system interactions and quantify the conditions under which low-pressure MH tanks can be effectively integrated into HRES, with particular attention to thermal management and seasonal variability. Two simulation campaigns were carried out to provide both component-level and system-level insights. The first focused on thermal management during hydrogen absorption in the MH tank, comparing passive and active cooling strategies. Forced convection reduced absorption time by 44% compared to natural convection, while avoiding the additional energy demand associated with thermostatic baths. The second campaign assessed seasonal operation: even under winter irradiance conditions, the system ensured continuous household supply and enabled full recharge of two MH tanks every six days, in line with the hydrogen requirements of the light vehicle daily commuting profile. Battery support further reduced grid reliance, achieving a Grid Dependency Factor as low as 28.8% and enhancing system autonomy during cold periods. Full article

Photocatalytic conversion of CO₂ to hydrocarbon fuels offers a promising pathway for sustainable renewable energy production. In this study, a ZnO/ZnFe₂O₄ composite featuring a Type-II heterojunction was synthesized through a facile one-step hydrothermal approach, significantly enhancing visible-light-driven CO₂ reduction activity. The optimized catalyst exhibits CH₄ and CO production rates that are 3.3 and 4.9 times higher, respectively, than those of pristine ZnFe₂O₄ over 6 h. This significant enhancement in photocatalytic performance is attributed to the Type-II band alignment, which not only broadens light absorption but also greatly promotes efficient charge separation. It is corroborated by a series of experimental evidence: a two-fold enhancement in photocurrent response, a 15.1% reduction in PL intensity, decreased electrochemical impedance, and an extended charge carrier lifetime. Furthermore, in situ FTIR spectroscopy confirms that the heterojunction facilitates the formation of key intermediates (specifically *COOH and HCOO⁻). This study highlights the importance of precise interface design based on a Type-II heterojunction in heterostructured composite catalysts and provides mechanistic insights for developing highly efficient CO₂ photoreduction systems. Full article

The production of green hydrogen by microalgae is a promising strategy to convert energy of sun light into a carbon-free fuel. Many problems must be solved before large-scale industrial applications. One solution is to find a microalgal species that is easy to grow, easy to manipulate, and that can produce hydrogen open-air, thus in the presence of oxygen, for periods of time as long as possible. In this work we investigate by means of predictive computational models, the [FeFe] hydrogenase enzyme of Nannochloropsis salina, a promising microcalga already used to produce high-value products in salt water. Catalysis of water reduction to hydrogen by [FeFe] hydrogenase occurs in a peculiar iron-sulfur cluster (H-cluster) contained into a conserved H-domain, well represented by the known structure of the single-domain enzyme in Chlamydomonas reinhardtii (457 residues). By combining advanced deep-learning and molecular simulation methods we propose for N. salina a two-domain enzyme architecture hosting five iron-sulfur clusters. The enzyme organization is allowed by the protein size of 708 residues and by its sequence rich in cysteine and histidine residues mostly binding Fe atoms. The structure of an extended F-domain, containing four auxiliary iron-sulfur clusters and interacting with both the reductant ferredoxin and the H-domain, is thus predicted for the first time for microalgal [FeFe] hydrogenase. The structural study is the first step towards further studies of the microalga as a microorganism producing pure hydrogen gas. Full article

Trees are vital to both environmental health and human well-being. They purify the air we breathe, support biodiversity by providing habitats for wildlife, prevent soil erosion to maintain fertile land, and supply wood for construction, fuel, and a multitude of essential products such as fruits, to name a few. Therefore, it is important to monitor and preserve them to protect the natural environment for future generations and ensure the sustainability of our planet. Remote sensing is the rapidly advancing and powerful tool that enables us to monitor and manage trees and forests efficiently and at large scale. Statistical methods, machine learning, and more recently deep learning are essential for analyzing the vast amounts of data collected, making data the fundamental component of these methodologies. The advancement of these methods goes hand in hand with the availability of sample data; therefore, a review study on available high-resolution aerial datasets of trees can help pave the way for further development of analytical methods in this field. This study aims to shed light on publicly available datasets by conducting a systematic search and filter and an in-depth analysis of them, including their alignment with the FAIR—findable, accessible, interoperable, and reusable—principles and the latest trends concerning applications for such datasets. Full article

Microalgae are promising candidates for renewable biofuel production and nutrient-rich animal feed. Cultivating microalgae using wastewater can lower production costs but often results in biomass contamination and increases downstream processing requirements. This study presents a novel system that integrates an algae cultivator (AC) with a single-chamber microbial fuel cell (MFC) equipped with photosynthetic and air-cathode functionalities, separated by a ceramic membrane. The system enables the generation of electricity and production of clean microalgae biomass concurrently, in both light and dark conditions, utilizing wastewater as a nutrient source and renewable energy. The MFC chamber was filled with simulated potato processing wastewater, while the AC chamber contained microalgae Chlorella vulgaris in a growth medium. The ceramic membrane allowed nutrient diffusion while preventing direct contact between algae and wastewater. This design effectively supported algal growth and produced uncontaminated, harvestable biomass. At the same time, larger particulates and undesirable substances were retained in the MFC. The system can be operated with synergy between the MFC and AC systems, reducing operational and pretreatment costs. Overall, this hybrid design highlights a sustainable pathway for integrating electricity generation, nutrient recovery, and algae-based biofuel feedstock production, with improved economic feasibility due to high-quality biomass cultivation and the ability to operate continuously under variable lighting conditions. Full article

Catalytic upgrading of vacuum residue (VR) is critical for enhancing fuel yield and reducing waste in petroleum refining. This study explores VR cracking over a novel cerium-loaded acidified metakaolinite catalyst (MKA800–20%Ce) prepared via calcination at 800 °C, acid leaching, and wet impregnation with 20 wt.% Ce. The catalyst was characterized using FTIR, BET, XRD, TGA, and GC–MS to assess structural, textural, and thermal properties. Catalytic cracking was carried out in a fixed-bed batch reactor at 350 °C, 400 °C, and 450 °C. The MKA800@Ce20% catalyst showed excellent thermal stability and surface activity, especially at higher temperatures. At 450 °C, the catalyst yielded approximately 11.72 g of total liquid product per 20 g of VR (representing a ~61% yield), with ~3.81 g of coke (~19.1%) and the rest as gaseous products (~19.2%). GC-MS analysis revealed enhanced production of light naphtha (LN), heavy naphtha (HN), and kerosene in the 400–450 °C range, with a clear temperature-dependent shift in product distribution. Structural analysis confirmed that cerium incorporation enhanced surface acidity, redox activity, and thermal stability, promoting deeper cracking and better product selectivity. Kinetics were investigated using an eight-lump first-order model comprising 28 reactions, with kinetic parameters optimized through a genetic algorithm implemented in MATLAB. The model demonstrated strong predictive accuracy taking into account the mean relative error (MRE = 9.64%) and the mean absolute error (MAE = 0.015) [MAE: It is the absolute difference between experimental and predicted values; MAE is dimensionless (reported simply as a number, not %). MRE is relative to the experimental value; it is usually expressed as a percentage (%)] across multiple operating conditions. The above findings highlight the potential of Ce-modified kaolinite-based catalysts for efficient atmospheric pressure VR upgrading and provide validated kinetic parameters for process optimization. Full article

The excessive use of fossil fuels and the increasing generation of solid waste, driven by population growth, industrialization, and economic development, have led to serious environmental, energy, and public health issues. In light of this problem, it is crucial to adopt sustainable solutions that promote the transition to renewable energy sources, such as biogas. Although progress has been made in optimizing biogas production, there is still no adaptable model for various environments that allows for the determination of optimal quantities of different organic wastes, simultaneously considering their composition, moisture content, and control of critical factors for biogas production, as well as the biodigester’s capacity and other relevant elements. In practice, the dosing of waste is conducted empirically, leading to inefficiencies that limit the potential for biogas production in real scenarios. The objective of this article is to propose a linear optimization model with nonlinear constraints that maximizes biogas production, considering fundamental parameters such as the moisture percentage, pH, carbon/nitrogen ratio (C/N), substrate volume, organic matter, volatile solids (VS), and biogas production potential from different wastes. The model estimates the optimal waste composition based on the biodigester capacity to ensure balanced substrates. The results for the proposed scenarios demonstrate its effectiveness: Scenario 1 achieved 3.42 m³ (3418.67 L) of biogas, while Scenario 2, with a greater diversity of waste, reached 8.06 m³ (8061.43 L). The model maintained pH (6.49–6.50), C/N ratio (20.00), and moisture (60.00%) within optimal ranges. Additionally, a Monte Carlo sensitivity analysis (1000 simulations) validated its robustness with a 95% confidence level. This model provides an efficient tool for optimizing biogas production and waste dosing in rural contexts, promoting clean and sustainable technologies for renewable energy generation. Full article

Social media has evolved into a central force in handling national and local crises. This prompts the question: Do all stakeholders in a local crisis grasp its significance when it predominantly unfolds in the digital realm of online social media? This article investigates this issue through a case study of the Roman Zadorov justice movement in Israel. Despite Zadorov’s wrongful imprisonment for Tair Rada’s murder, social media support grew, reshaping perceptions of Katsrin, the town where the murder took place. The four-fold analysis draws on social media content, youth interviews, municipal officials’ perspectives, and a population survey. It reveals how Tair Rada’s case became central to Katsrin’s image, fueled by social media’s influence. However, local officials failed to recognize social media’s crisis significance, highlighting a disconnect. The article concludes by exploring this dissonance, shedding light on crisis management challenges in the social media era and their impact on local governance. Full article

This work provides a systematic evaluation of the performance and regulated emissions of binary n-pentanol–diesel blends under steady-state conditions, thereby clarifying condition-dependent efficiency–emission trade-offs across multiple loads and speeds. A single-cylinder, air-cooled diesel engine was operated at two speeds (1700 and 2700 rpm) and four brake mean effective pressure (BMEP) levels (0.25–0.49 MPa) using commercial diesel (D100) and three n-pentanol–diesel blends at volume ratios of 10%, 30%, and 50% (designated D90P10, D70P30, and D50P50, respectively). Brake thermal efficiency (BTE), brake specific energy consumption (BSEC), and brake specific fuel consumption (BSFC) were measured alongside exhaust emissions of nitrogen oxides (NO_x), carbon monoxide (CO), hydrocarbon (HC), carbon dioxide (CO₂), and smoke opacity. The results show that due to a lower cetane number, high latent heat of vaporization, and reduced heating value, n-pentanol blends incur efficiency and fuel consumption penalties at light to moderate loads. However, these disadvantages diminish or reverse at high loads and speeds: D50P50 surpasses D100 in BTE and matches or improves BSEC and BSFC at 2700 rpm and 0.49 MPa. Emission data reveal that the blend’s fuel-bound oxygen and enhanced mixing provide up to 16% NO_x reduction; 35% and 45% reductions in CO and HC, respectively; and a 74% reduction in smoke opacity under demanding conditions, while CO₂ per unit work output aligns with or falls below D100 at high load. These findings demonstrate that optimized n-pentanol–diesel blends can simultaneously improve efficiency and mitigate emissions, offering a practical pathway for low-carbon diesel engines. Full article

This article examines how populist radical right parties (PRR) in three contrasting European contexts—Slovenia, France, and Poland—strategically instrumentalize Christianity within their anti-immigration agendas. Rather than using religion as a matter of faith, these parties recast Christianity as a cornerstone of national and European identity, positioning it in opposition to Islam and non-European migration. The study argues that such instrumentalization serves not only to construct a religiously defined national identity, but also to legitimize exclusionary policies. By analyzing selected political speeches, party manifestos, and media discourse, we explore how far-right actors frame Islam as incompatible with European values, reinforcing the division between “Christian Europe” and “foreign non-Christian migrants.” Drawing on recent scholarship on civilizational populism and religious boundary-making, we further assess how processes of globalization and European integration have been interpreted by populist parties to fuel anti-immigrant sentiment. Methodologically, we employ qualitative content analysis to identify recurring themes and rhetorical strategies, with a focus on the intersection of religion, nationalism, and migration. The findings contribute to debates on religious pluralism in contemporary Europe, shedding light on how far-right populism reframes pluralism and challenges secular principles across different political and cultural settings. Full article

Electrocatalytic CO₂ reduction driven by renewable electricity offers a sustainable approach to producing valuable chemicals, though it is often hindered by low activity and selectivity. CuO, an important transition metal oxide, exhibits unique advantages in photoelectrocatalysis due to its high intrinsic catalytic activity and ability to serve as an active site for CO₂ reduction. SiO₂, a widely used substrate, facilitates Cu loading and increases the specific surface area of the catalyst. Meanwhile, g-C₃N₄ provides excellent visible-light responsiveness and efficient charge carrier mobility. Together, CuO, SiO₂, and g-C₃N₄ are earth-abundant, low-cost, and chemically stable, making them ideal for solar-to-fuel applications. Here, a novel ternary heterojunction photocatalyst was constructed using SiO₂, CuO, and g-C₃N₄. The heterostructure significantly improves light-harvesting efficiency, promotes efficient charge separation and transport, and simultaneously mitigates photogenerated carrier recombination and catalyst corrosion. The resulting SiO₂@CuO/g-C₃N₄ catalyst demonstrates outstanding CO₂ conversion performance, achieving a CO yield of 17 mmolg⁻¹h⁻¹ at 1.2 V_RHE with nearly 100% selectivity. Moreover, this work systematically investigates the electrocatalytic CO₂ reduction reaction (CO₂RR) mechanism on Cu-based catalysts, offering insights into the formation of high-value multicarbon products and highlighting the potential of rational heterojunction design in enhancing solar-driven fuel production efficiency. Full article

This review sheds some light on the emerging niche of the reuse of spent adsorbents in electrochemical devices. Reuse and repurposing extend the adsorbent’s life cycle, remove the need for long-term storage, and generate additional value, making it a highly eco-friendly process. Main adsorbent-type materials are overviewed, emphasising desired properties for initial adsorption and subsequent conversion to electroactive material step. The effects of the most frequent regeneration procedures are compared to highlight their strengths and shortcomings. The latest efforts of repurposing and reuse in supercapacitors, fuel cells, and batteries are analysed. Reuse in supercapacitors is dominated by materials that, after a regeneration step, lead to materials with high surface area and good pore structure and is mainly based on the conversion of organic adsorbents to some form of conductive carbon adlayer. Additionally, metal/metal-oxide and layered-double hydroxides are also being developed, but predominantly towards fuel cell and battery electrodes with respectable oxygen reduction characteristics and significant capacities, respectively. Repurposed adsorbents are being adopted for peroxide generation as well as direct methanol fuel cells. The work puts forward electrochemical devices as a valuable avenue for spent adsorbents and as a puzzle piece towards a greener and more sustainable future. Full article

The depletion of fossil fuels and the rise of environmental concerns have increased the importance of renewable energy sources, positioning wind energy as a key alternative. Modern wind turbine blades are predominantly manufactured from composite materials due to their light weight, high strength, and resistance to corrosion. In offshore applications, approximately 95% of the composite content is glass fiber-reinforced polymer (GFRP), while the remaining 5% is carbon fiber-reinforced polymer (CFRP). GFRP is favored for its low cost and fatigue resistance, whereas CFRP offers superior strength and stiffness but is limited by high production costs. This study investigates the durability of adhesively bonded GFRP and CFRP joints under marine exposure. Seven-layer GFRP and eight-layer CFRP laminates were produced using a 90° unidirectional twill weave and prepared in accordance with ASTM D5868-01. Specimens were immersed in natural Aegean Sea water (21 °C, salinity 3.3–3.7%) for 1, 2, and 3 months. Measurements revealed that GFRP absorbed significantly more moisture (1.02%, 2.97%, 3.78%) than CFRP (0.49%, 0.76%, 0.91%). Four-point bending tests conducted according to ASTM D790 showed reductions in Young’s modulus of up to 9.45% for GFRP and 3.48% for CFRP. Scanning electron microscopy (SEM) confirmed that moisture-induced degradation was more severe in GFRP joints compared to CFRP. These findings highlight the critical role of environmental exposure in the mechanical performance of marine composite joints. Full article