Energy Resources Research Articles

A characterization study of Wadi Thamad oil shale: Towards a new source of energy in Jordan

AbstractJordan's energy sector faces significant challenges due to rising fuel prices, making the exploration of local energy resources crucial. The abundant oil shale deposits in Wadi Thamad present a promising opportunity. Since Wadi Thamad oil shale has never been studied before, this research focuses on the Wadi Thamad basin near Madaba, Jordan, aiming to comprehensively characterize its oil shale using advanced analytical techniques. Using X‐ray diffraction, Fourier transform infrared spectroscopy, X‐ray fluorescence, scanning electron microscopy, thermogravimetric analysis (TGA), and differential scanning calorimetry, this study assesses the mineralogical, chemical, and thermal properties of Wadi Thamad oil shale. The findings reveal calcite and quartz as the primary minerals, with significant aliphatic, CO2, hydroxyl, and carboxyl groups. Elemental analysis highlights essential oxides, such as CaO and SiO2. Fischer assay results indicate an oil content of 5.3–10.1 wt%, a gross‐calorific value of 4.56–7.69 MJ/kg, and a sulfur content of 1.77–2.10 wt%. The peak pyrolysis temperature is 432.4°C from TGA. This research's novelty lies in its comprehensive approach to characterizing the underexplored Wadi Thamad oil shale basin. The findings enhance the understanding of Wadi Thamad's geological composition and underscore its potential as a local energy resource, contributing valuable data to Jordan's energy portfolio and offering economic benefits.

Rational Design of Heterostructured MXene‐based Nanomaterials in Electrocatalytic Water Splitting

Hydrogen energy is widely considered the potential energy resource for energy conversion systems owing to its high energy density and pollution‐free nature. This review provides a comprehensive overview of recent advancements in heterostructured MXenes for hydrogen production. The fundamental properties of MXenes and their unique contribution to catalytic application are addressed, including the synthesis strategies, interlayer modification, and hybridization with other materials to enhance catalytic performance. Comparative analysis highlights the effect of different heterostructured MXenes on enhanced catalytic efficiency by tuning their electronic properties and increasing the surface interactions. We provide the future research direction and challenges of functionalized MXene composites, such as material stability, scalability, and environmental impact. Ultimately, the recent progress underscores the potential of heterostructured MXenes in advancing hydrogen production technologies, offering a path toward cleaner energy solutions.

Two-stage multi-objective distributionally robust operation optimization and benefits equalization of an off-grid type electric-hydrogen-ammonia-methanol coupling system

As the proportion of renewable energy has increased significantly, electric-hydrogen coupling technology has emerged as a pivotal strategy for optimizing the utilization of renewable energy resources. In particular, within the power and chemical sectors, the preparation of ammonia and methanol using electric-hydrogen coupling technology has emerged as the two principal avenues for the utilization of hydrogen energy. However, as a nascent technology, the preparation of ammonia and methanol by electric-hydrogen coupling is confronted with a multitude of challenges, including the considerable uncertainty surrounding the generation of renewable energy, the dearth of economic benefits, and the need to ensure a fair allocation of benefits among the various stakeholders. To enhance the risk resistance of the electric-hydrogen-ammonia-methanol coupling system (EHAMCS), improve the economic benefits, ensure the environmentality and stability of the system, and realize the fair distribution of benefits among the energy participants, this paper constructs a multi-objective distributionally robust optimization (DRO) and multi-dimensional benefits equilibrium model for the EHAMCS and performs a numerical analysis with an off-grid type EHAMCS as an example. The results demonstrate that: 1) The cooperative operation of the EHAMCS offers notable economic advantages. When compared with only pursuing economic benefits, under multi-objective optimization, the economic benefits of the coupling system can be reduced by 1.49 %, but the carbon emission can be reduced by 10.06 %, and the instability is reduced by 0.02, which can create derivative benefits. 2) The two-stage DRO model is an effective means of addressing the impact of system operation uncertainty and the affine strategy enables the rapid adjustment of the day-ahead dispatching plan, thereby balancing the power deviation. 3) The multi-dimensional factor benefit balancing scheme is more conducive to the participation of clean energy and downstream ammonia and methanol energy subsystems in cooperative operation and promotes the consumption of clean energy while improving the energy conversion value of the EHAMCS to increase economic benefits. 4) EHAMCS operators should adjust their operational strategies in time with market and policy signals, and select historical samples reasonably, so as to realize the balance of the DRO model in terms of economy, robustness, and practicality. The effectiveness of the model and the superiority of the EHAMCS are thus verified.

Review of the Transition to Energy 5.0 in the Context of Non-Renewable Energy Sustainable Development

The problems of achieving the UN’s sustainable development goals related to providing both developed and developing countries with cheap and accessible energy, as well as in the context of taking climate action, cannot be solved, on the one hand, without a transition to Energy 5.0, within the framework of the upcoming Fifth Industrial Revolution. On the other hand, it cannot be carried out without ensuring a “seamless” Fourth Energy Transition, which poses new challenges for the technological modernization of power production from non-renewables. Along with this, the expected transition to a human-centric Industry 5.0 challenges researchers to identify obstacles to the diffusion of technologies within hydrocarbon production industries and ways to overcome them in regard to the upcoming Mining 5.0 and Oil and Gas 5.0 environment. In this regard, the purpose of this review is to analyze the structure of scientific publications in this field of research on the human-centric development of technologies in terms of these platforms in order to outline the basis for further research. To achieve this goal, this review provides a multifaceted overview of the main technologies of Industry 5.0, embodied within Energy 5.0, Mining 5.0, and Oil and Gas 5.0, such as collaborative artificial intelligence and co-bots, digital tees, the industrial Internet of Everything, smart cities, and industry; their human-centric nature is revealed as the basis for achieving significant sustainable development goals. This review concludes that there is a need for further analysis of certain areas of the transition to Energy 5.0, such as the human-centric development of digital technologies of Industry 5.0 in the fuel and energy sector, and the revision of its role in terms of achieving the sustainable development goals in the future.

Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables

Energy consumption analysis has often faced challenges such as limited model accuracy and inadequate consideration of the complex interactions between energy usage and meteorological data. This study is presented as a solution to these challenges through a detailed analysis of energy consumption across UBC Campus buildings using a variety of machine learning models, including Neural Networks, Decision Trees, Random Forests, Gradient Boosting, AdaBoost, Linear Regression, Ridge Regression, Lasso Regression, Support Vector Regression, and K-Neighbors. The primary objective is to uncover the complex relationships between energy usage and meteorological data, addressing gaps in understanding how these variables impact consumption patterns in different campus buildings by considering factors such as seasons, hours of the day, and weather conditions. Significant interdependencies among electricity usage, hot water power, gas, and steam volume are revealed, highlighting the need for integrated energy management strategies. Strong negative correlations between Vancouver’s temperature and energy consumption metrics are identified, suggesting opportunities for energy savings through temperature-responsive strategies, especially during warmer periods. Among the regression models evaluated, deep neural networks are found to excel in capturing complex patterns and achieve high predictive accuracy. Valuable insights for improving energy efficiency and sustainability practices are offered, aiding informed decision-making for energy resource management in educational campuses and similar urban environments. Applying advanced machine learning techniques underscores the potential of data-driven energy optimization strategies. Future research could investigate causal relationships between energy consumption and external factors, assess the impact of specific operational interventions, and explore integrating renewable energy sources into the campus energy mix. UBC can advance sustainable energy management through these efforts and can serve as a model for other institutions that aim to reduce their environmental impact.

Antecedent of Event Management Intention Toward Sustainable Practices: A Theoretical Framework

The incidence of rising global temperature, extreme weather, depletion of finite resources, and other environmental impacts has shown as one of the most crucial issues to deal with. The situation to create low-carbon communities and sustainable systems through sustainable event management—that is, the switch from conventional event management concept to more environmentally friendly and socially responsible practices among the event company by engaging with various stakeholders. Recent studies suggested that evidence on sustainable intentions concerning events in Malaysia is still limited. Therefore, this study aims to explore the relationship between attitude, subjective norms, perceived behavioral control, environmental consciousness and economic benefit toward sustainable capabilities. This paper will contribute to future literature regarding understanding what drives the event management intention toward sustainability contributing to the existing green behavior and body of literature. It will increase stakeholder awareness and policymakers to be more innovative in reducing environmental impact. This paper is expected to better understand company behavior toward adopting ecologically ethical and socially accountable methods. More research needs to be done to validate and reinforce the proposed theoretical framework. Therefore, future research should concentrate on both qualitative and quantitative research designs by examining the relationship between the antecedents and intention. Ideally, insights from practitioners and academicians are highly encouraged in substantiating the proposed theoretical framework.

Influence of Starvation on Biochemical, Physiological, Morphological, and Transcriptional Responses Associated with Glucose and Lipid Metabolism in the Liver of Javelin Goby (Synechogobius hasta).

In this study, the influence of fasting on hepatic glucose and lipid metabolism was explored by examining biochemical, antioxidative, and morphological indicators and transcriptional expression in the liver of javelin goby (Synechogobius hasta) after 0, 3, 7, or 14 days of starvation. Marked reductions in hepatic glycogen and triglycerides occurred from the seventh day of starvation until the end of the trial (p < 0.05). However, no alterations in hepatic cholesterol or protein were detected throughout the entire experiment (p > 0.05). During fasting, the activities of pyruvate kinase, lactate dehydrogenase, and glycogen phosphorylase a all rose firstly and then fell (p < 0.05). The activities of hepatic fatty acid synthase and acetyl-CoA carboxylase were minimized to their lowest levels at the end of food deprivation (p < 0.05), while lipase was elevated after 7-14 days of fasting (p < 0.05). Catalase, glutathione, and the total antioxidative capacity were increased and maintained their higher values in the later stage of fasting (p < 0.05), whereas malondialdehyde was not significantly changed (p > 0.05). Hepatic vein congestion, remarkable cytoplasmic vacuoles, and irregular cell shape were present in S. hasta which endured 3-7 days of fasting and were less pronounced when food shortage was prolonged. In terms of genes associated with glucose and lipid metabolism, the hepatic phosphofructokinase gene was constantly up-regulated during fasting (p < 0.05). However, the mRNA levels of glycogen synthase and glucose-6-phosphatase were obviously lower when the food scarcity extended to 7 days or more (p < 0.05). Fatty acid synthase, stearoyl-CoA desaturase 1, and peroxisome proliferator-activated receptor γ were substantially down-regulated in S. hasta livers after 7-14 days of food deprivation (p < 0.05). However, genes involved in lipolysis and fatty acid transport were transcriptionally enhanced to varying extents and peaked at the end of fasting (p < 0.05). Overall, starvation lasting 7 days or more could concurrently mobilize hepatic carbohydrates and fat as energy resources and diminished their hepatic accumulation by suppressing biosynthesis and enhancing catabolism and transport, ultimately metabolically and structurally perturbing the liver in S. hasta. This work presents preliminary data on the dynamic characteristics of hepatic glucose and lipid metabolism in S. hasta in response to starvation, which may shed light on the sophisticated mechanisms of energetic homeostasis in fish facing nutrient unavailability and may benefit the utilization/conservation of S. hasta.

Review of peer-to-peer energy trading: Advances and challenges

The power system is confronting progress due to the high integration of distributed energy resources (DERs). These DERs are expected to cause challenges for power system operations. Therefore, innovative management approaches were proposed for integrating DERs in future power systems and maximizing DER owners’ benefits, such as peer-to-peer (P2P) energy trading. Direct energy trading between users is made possible by P2P energy trading, which supports bulk power infrastructure operations while supporting local power and energy balance. P2P energy trading is a promising approach to expanding the installation of renewable energy sources and achieving the system flexibility required for the shift to low-carbon energy. The grid is anticipated to gain from P2P energy trading by having lower reserve requirements, peak demand, and network losses. Many studies and pilot projects have shown how P2P energy trading benefits prosumers as well as the grid. However, the widespread use of such trading models remains limited in today's electrical markets. This paper reviews recent advances in the P2P energy system and a perceptive discussion of the challenges that are keeping P2P from becoming a viable energy management solution in the present electrical market. First, the energy network is covered in this paper's description of these new P2P markets; next, the types of P2P energy trading, moving on to the market structure. Then, the technologies and technical approaches behind P2P energy trading are covered. After that, P2P energy trading advances in different systems are discussed. Finally, we identify challenges before making some concluding remarks.

Optimizing Microgrid Operation: Integration of Emerging Technologies and Artificial Intelligence for Energy Efficiency

Microgrids have emerged as a key element in the transition towards sustainable and resilient energy systems by integrating renewable sources and enabling decentralized energy management. This systematic review, conducted using the PRISMA methodology, analyzed 74 peer-reviewed articles from a total of 4205 studies published between 2014 and 2024. This review examines critical areas such as reinforcement learning, multi-agent systems, predictive modeling, energy storage, and optimization algorithms—essential for improving microgrid efficiency and reliability. Emerging technologies like artificial intelligence (AI), the Internet of Things, and flexible power electronics are highlighted for enhancing energy management and operational performance. However, challenges persist in integrating AI into complex, real-time control systems and managing distributed energy resources. This review also identifies key research opportunities to enhance microgrid scalability, resilience, and efficiency, reaffirming their vital role in sustainable energy solutions.

The future of flying: Explore more energy-efficient airplane wings

In recent years, there has been a substantial rise in the popularity of air travel, bringing with it a surge in the consumption of jet fuel, a nonrenewable resource responsible for 2.4% of global carbon emissions. As air travel continues to gain prevalence, the anticipation of a significant increase in carbon emissions and a subsequent surge in jet fuel costs looms large. Maintaining affordability in air travel while mitigating carbon emissions necessitates a focus on maximizing airfoil efficiency. This study investigates how airplane wing design affects the aerodynamic performance of aircraft, with a specific focus on the impact of wing angle of attack, wing length, and winglets. In this research, this study use Computational Fluid Dynamics software to simulate and analyze the aerodynamic forces acting upon an aircraft to deduce its drag and lift coefficients. Employing diverse fluid dynamics formulas, the research calculates the aerodynamic performance of various aircraft airfoil designs, aiming to identify the optimal design. The envisioned optimal configuration is likely to feature an elongated wing, a 20 angle of attack, and suitable winglets stalled, strategically incorporated to enhance overall efficiency in air travel.

Exploring the impact of economic growth and sources of electricity production in India: New insights from sectoral analysis using NARDL approach

This study aims to explore the asymmetric impact of renewable energy on the sectoral output of the Indian economy by analyzing the time series data from 1971 to 2019. The nonlinear autoregressive distributed lag approach (NARDL) is employed to examine the short- and long-run relationships between the variables. Most studies focus on economic growth, ignoring sectoral dynamics. The result shows that the sectoral output shows a differential dynamism with respect to the type of energy source. For instance, agricultural output responds positively to the positive shock in renewable energy, whereas industry and service output behave otherwise. Since the latter sectors depend heavily on non-renewable energy sources, they behave positively towards them. Especially, electricity produced from non-renewable energy sources significantly influences service sector output. However, growing evidence across the world is portraying the strong relationship between the growth of renewable energy sources and economic growth. However sectoral dynamism is crucial to frame specific policies. In this regard, the present paper’s result indicates that policies related to promoting renewable energy sources will significantly influence sectoral output in the long run in India.

Day-ahead dynamic operating envelopes using stochastic unbalanced optimal power flow

Driven by the energy transition, distribution networks are dealing with increasing uptake of distributed energy resources, including solar photovoltaic generation. Residential rooftop solar allows customers to minimize exposure to price increases in the market, which has led to high penetration of PV in regions with high amounts of solar hours such as Australia. Eventually, this leads to congestion in the network, either due to voltage rise, or due to overcurrent in lines or transformers. Distribution utilities are now moving on from static export limits for customers in congested networks, to dynamic limits that are based on the state of the network. It is considered thoughtful to give customers advance warning, e.g. day-ahead, of the moments and degrees of export limitation, despite uncertainty surrounding the future state of the network. Therefore, in this paper, we consider the application of general polynomial chaos expansion based stochastic unbalanced optimal power flow to the day-ahead determination of dynamic export limits. We perform a numerical study on a European style low voltage feeder and illustrate the impact of fairness principles on chance-constrained stochastic nonlinear optimization without the need of sampling, linearizing the power flow equations, or applying relaxations. Case studies show the necessity of considering unbalanced study of distribution system. It was observed that equality measures reduce the overall output of the system in an attempt to achieve equal relative injection, while alpha fairness with a higher value of alpha is a compromise between the efficiency and fairness in DOEs.

Techno-economic-environmental assessment of green hydrogen and ammonia synthesis using solar and wind resources for three selected sites in Egypt

The primary motivation of the present study is to mitigate the severe impact of ongoing energy resource shortages, while offering clean and sustainable energy carriers, such as hydrogen and ammonia. The present system mainly encompasses water splitting and the Haber-Bosch (HB) processes for green hydrogen and ammonia synthesis using solar and wind power, respectively. Pointwise quantification analyses are conducted to quantify the power, hydrogen, and ammonia as well as the economic parameters, specifically the levelized cost of energy (LCOE), levelized cost of hydrogen (LCOH), and levelized cost of ammonia (LCOA). This analysis is based on meteorological data from three sites in Egypt, considering the specific water and nitrogen requirements for hydrogen and ammonia synthesis, respectively. Furthermore, carbon dioxide mitigation from solar and wind systems is estimated. These respective sites are Jarjoub on the coastlines of the Mediterranean Sea, and Ain Sokhna and Jabal Al-Zait on the coastlines of the Red Sea. The results indicate that the lowest values of LCOE, LCOH, and LCOA are 12.58 $/MWh, 1.91 $/kg H2, and 396.1 $/Ton NH3, respectively, which were attained using solar resources at Ain Sokhna geographical site at the Red Sea. Besides, Jarjoub, which is located in the Mediterranean Sea, could attain LCOH of 2.15 $/kg, which is still a promising option due to its export potential to Europe. However, the use of wind resources is incompetent for solar counterparts in the respective sites; their potential application in Egypt is still promising. The results demonstrate that Jabal Al-Zait stands as a favorable location for green power, hydrogen, and ammonia synthesis using wind resources, which has LCOE, LCOH, and LCOA of 23.67 $/MWh, 2.75 $/kg H2, and 547.8 $/Ton NH3, respectively.

Assessing China's solar power potential: Uncertainty quantification and economic analysis

Solar power is vital for China's future energy pathways to achieve the goal of 2060 carbon neutrality. Previous studies have suggested that China's solar energy resource potential surpass the projected nationwide power demand in 2060, yet the uncertainty quantification and cost competitiveness of such resource potential are less studied. Therefore, we applied an integrated framework to simulate China's solar photovoltaic (PV) technical potential, and incorporated potential uncertainty stemming from climate change, land use dynamics, and technological advancements. In addition, we constructed the solar energy supply curve for each province and calculated the economic potential. According to our results, approximately 78.6 % and 99.9 % of China's technical solar PV potential are priced lower than the benchmark price of coal-fired energy in pessimistic and optimistic scenario. These findings highlight the significant technical and economic potential of solar PV as a cost-effective alternative to coal-fired electricity to meet China's growing electricity demands.

RELATIONSHIP BETWEEN ENERGY AND FOOD PRICES IN THE EURO AREA

Over the past five years, global economic developments have been accompanied by a number of challenges. After a period of economic development, the global economy is facing a significant challenge caused by the COVID-19 crisis. There have been factory closures, border closures and supply chain disruptions. All this is reflected in the EU and the euro area. This was followed by a period of recovery and accelerating inflation. The war on the territory of Ukraine is also having a significant impact on inflationary processes. This further jeopardises the supply of energy resources and their prices begin to rise at a very high rate. The shock of rising energy prices has also been reflected in sharp increases in food prices. In the euro area, there have been periods when food prices have risen by more than 17% on an annual basis and energy products by almost 60% in some months. All these developments highlight the need to examine the links between energy and food prices. This is necessary in order to be able to take quick decisions in the event of new volatility in energy prices. The aim of the study is to establish a causal relationship between the dynamics of energy and food prices and the time lag over which food prices react to increases in energy prices. A correlation analysis is carried out and a causal relationship between the two indicators is established by regression analysis at different time lags. The hypothesis tested is that, over the last five years, there has been a strong month-to-month correlation between energy prices and food prices with a lag of six months. For the purpose of regression analysis and hypothesis testing, monthly data for food inflation and inflation for the group Electricity, gas and other fuels have been used. The data are taken from the structure of the Harmonised Index of Consumer Prices published by Eurostat. The detailed regression analysis shows that there is a statistically significant linear relationship between the price indices of the two product groups with a lag of six months. The Granger causality test confirms the results of the dynamic, correlation and regression analysis. The results of the test show not only the existence of a causal relationship, but also the ability of energy price changes to predict food price dynamics. All this leads to the conclusion that in periods of significant changes in energy prices, food prices can be expected to follow similar dynamics within six months. This relationship could be used both for forecasting and for taking specific economic measures.

Understanding the interplay of GDP, renewable, and non-renewable energy on carbon emissions: Global wavelet coherence and Granger causality analysis

This study examines the causality of Per Capita Gross Domestic Production (PGDP), Renewable Energy Consumption (REC), and Non-Renewable Energy Consumption (NREC) on Carbon dioxide (CO2) emissions at the global level utilising data gathered from 1995 to 2020 across various countries categorised based on income levels as High, Low, Upper Middle and Lower Middle and analysed through wavelet coherence. The findings reveal both bidirectional and unidirectional causality between the variables which have evolved. Globally, a bi-directional relationship is observed with a positive correlation between PGDP and NREC and in contrast, a negative correlation with REC. Furthermore, the analysis highlights varying causalities between CO2 emissions and PGDP, except for high-income and lower-middle-income country categories, all other shows one-way causality in different periods in the short term. Moreover, CO2 and REC, show unidirectional causality throughout the short-term, exceptionally medium &amp; long term have both unidirectional and bidirectional causalities across all country categories with a positive correlation. In contrast, CO2 and NREC depict similar causalities to REC, however, with a negative correlation. A cross-country analysis was performed between CO2 and PGDP, CO2 and REC, and CO2 and NREC using Granger causality which shows mixed relationships. The findings hold significant implications for policymakers, providing valuable insights into the trade-offs between economic growth, energy consumption, and carbon emissions.

Multi-objective robust optimization for optimal operation of gas-electricity networks with the penetration of integrated P2G and electric vehicle parking lots

Energy is considered one of the most important human needs. Therefore, various energy resources are exploited to meet human needs. Among all available resources, electricity and natural gas have accounted for the largest share in meeting these needs. Accordingly, management of these resources is one of the most important issues in different societies. This paper presents a multi-objective optimization model for optimal management and operation of the power distribution network and natural gas network as well as reducing carbon emissions. In this modeling, the natural gas network and distribution power network are connected through two electric vehicle parking lots (EVPLs) and two gas-fired units (GFUs), and these two EVPLs and GFUs exchange energy with each other. The EVPLs are equipped with various facilities including power-to-gas (P2G) systems and natural gas storage that facilitate energy exchange between the two networks. The modeling presented in this article is based on mixed integer linear programming, so the nonlinear equations including power flow equations, the Weymouth equation and the cost functions of GFUs are also linearized using various methods. It should be noted that the price of purchased electricity poses uncertainty which is modeled using robust optimization techniques. The proposed model is tested on the 33-bus power distribution network and the 20-node natural gas network. The results show that the presence of EVPLs significantly reduces the operational costs of the natural gas network and power distribution network and carbon emissions.

Sewage sludge ash as filler in asphalt mastic: Low-temperature towards high-temperature performance

The sewage sludge ash (SSA) is characterized as a by-product used in the pavement industry to mitigate the environmental risks, enhance the landfill capacity and conserve the non-renewable resources. This research utilized SSA as a filler substitute in asphalt mastic, and its performance-based properties from low towards high temperatures were evaluated. For this goal, the physical and chemical properties of SSA and rock powder (used as base filler) were determined using BET surface area measurement, X-ray fluorescence, and scanning electron microscopy tests. The performance of base and SSA-modified asphalt mastics in four filler/bitumen weight ratios (0.6, 0.8, 1, and 1.2) was investigated using dynamic shear rheometer, multiple stress creep and recovery, linear amplitude sweep, and bending beam rheometer tests across a wide temperature range. The results indicate that SSA has a higher specific surface area, better bitumen absorption, and lower density compared to the base filler. Additionally, unlike the acidic nature of the base filler, SSA possesses strong basic properties, leading to a stronger chemical bond with bitumen and enhanced resistance to aging, particularly at high temperatures. Moreover, the lower density of SSA escalates the amount of filler per unit volume of the SSA-modified asphalt mastic, leading to the increased stiffness and elasticity. This trend improves resistance to permanent deformation and cracking at medium temperatures, although it has an adverse effect on performance of mastic at low temperatures. Moreover, due to the improved performance of SSA-modified asphalt mastic, this study can contribute to the sustainable development of the pavement industry.

Facile Synthesis of Porous Zinc Nanosheets for Highly Selective Electrochemical CO2 Reduction to CO

AbstractWith the increasing levels of CO2 emissions and the decreasing availability of energy resources, the electrocatalytic CO2 reduction reaction (CO2RR) offers a promising method for utilizing CO2 as a valuable resource using sustainable electrical energy. However, developing cost‐effective and highly efficient catalysts remains a significant challenge. Although zinc, an abundant element on Earth, has shown potential for converting CO2 into CO, its effectiveness as a catalyst for CO2 reduction is hindered by its low selectivity and stability. This study introduces a fast, efficient, and simple electrochemical deposition method for preparing porous zinc nanosheets (NS) using the hydrogen bubble template approach. The as‐prepared zinc NS catalyst demonstrates exceptional catalytic activity due to its highly porous structure and high specific surface area. At a voltage of −1 V versus RHE, it achieves a maximum CO Faradaic efficiency of 89.5 %. The performance of this system is characterized by a partial current density of around −6.17 mA cm−2 in an H‐cell configuration. Additionally, our catalyst exhibits remarkable durability over 10 h, highlighting its significant potential for real‐world utilization in CO2RR.

Design and Manufacturing of Thermosyphon Solar Water Heater Using Locally Available Materials in Nsanke Village

The weather conditions of Nsanke village starting from April to October emphasis the need for judicious utilization of warm water. We designed, constructed and tested a Solar Water Heater based on Thermosyphon principle in Nsanke village using locally available materials. Solar energy is received by two flat-plate collectors of 1.68 m2 each, consisting of absorber plate carrying copper tubes and placed in an insulated casing with a transparent glass cover of thickness 4 mm. The collectors are connected in series and are assumed to operate on the same efficiency. The resultant collector is connected to a storage tank, which is an association of two barrel nested overall and separated with an insulator. The radiation emitted by the absorber plates cannot escape through the glass, thus increasing its temperature. The system’s performance evaluation has shown that it can heat water from 23°C to at least 51°C on sunny days. The water heated flows into a storage tank through Thermosyphon principle. The Solar Water Heating System uses the Thermosyphon principle and can therefore be called Thermosyphon Solar Water Heater (TSWH) for simplicity. This system finds useful application in serving as a renewable energy resource in Nsanke village and neighboring villages.