Energy Shortage Research Articles

Investigation of thermal, acoustic, mechanical, and radiation shielding performance of waste and natural fibers

It is crucial to address two pressing global issues, energy shortage and environmental pollution, when producing building insulation materials. Using waste and natural fiber groups can be part of the solution. The insulation material was produced using pumpkin fiber, chicken fiber, cotton waste, vermiculite, and epoxy as binders. The samples were tested for thermal conductivity coefficient, ultrasonic sound transmission rate, density, water absorption rate, compressive and bending strength, and fire resistance at temperatures of 75, 100, 125, and 150C. The samples produced using natural and waste materials yielded a thermal conductivity value of 0.041 W/mK, an ultrasonic sound transmission speed of 0.25 km/s, a compressive strength value of 1.57 MPa, and bending strength values of 0.91 MPa. It has been clearly demonstrated that, with its low volume loss, it can serve as an alternative to the EPS-XPS types available in the market. Furthermore, the linear attenuation coefficients (LAC) were examined to obtain radiation shielding properties of the samples at 1173 and 133 keV energies using a 60Co gamma source. Also, LAC values determined between 0,1167 ± 0,0452 cm−1-0,2315 ± 0,0065 cm−1 for 1173 keV and 0,1042 ± 0,0488 cm−1 - 0,2141 ± 0,0062 cm−1 for 1333 keV. Accordingly, it has been revealed that waste compositions are effective in protecting against radiation.

Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis

Background Energy shortages and global warming have been significant issues throughout history. Therefore, the search for environmentally friendly renewable energy sources is crucial for achieving sustainability. Biomass energy is gaining global attention as a renewable energy option, particularly through the process of hydrothermal liquefaction, which converts biomass into bio-crude oil. Methods Hydrothermal liquefaction is a complex process that is challenging to explain, leading to research on machine learning models for this process. These models aim to predict values and investigate the impact of variables on the hydrothermal liquefaction process. However, the development of machine learning in hydrothermal liquefaction is still limited due to its novelty and the time required for comprehensive study. Thus, the objective of this study was to analyze relevant publications in the Scopus database, focusing on indexed ML and HTL keywords, to understand keyword associations and co-citations. Results The results reveal an increasing trend in the study of ML in the HTL process, with a growing interest from various countries. Conclusion Notably, China currently holds the largest share of ML research in HTL processes, with most published works falling within the field of engineering. The keyword “liquefaction” emerges as the most popular term in these publications.

Active Sites for CO2 Hydrogenation to Methanol: Mechanistic Insights and Reaction Control.

Catalytic CO2 conversion to methanol is a promising way to extenuate the adverse effects of CO2 emission, global warming and energy shortage. Understanding the fundamental features of CO2 activation and hydrogenation at the molecular level is essential for carbon utilization and sustainable chemical production in the current climate crisis. This review explores the recent advances in understanding the design of catalysts with desired active sites, including single-atom, dual-atom, interface, defects/vacancies and promoters/dopants. We focused on the design of various catalytic systems to enhance their catalytic performances by stabilizing active metal in a catalyst, identifying the unique structure of active species, and engineering coordination environments of active sites. Mechanistic insights provided by advanced operando and in situ spectroscopies were also discussed. Moreover, the review highlights the key factors affecting active sites and reaction mechanisms, such as local environments, oxidation states, and metal-support interactions. By integrating recent advancements and relating knowledge gaps this review aims to endow an inclusive overview of the field and guide future research toward more efficient and selective catalysts for CO2 hydrogenation to methanol.

Joint intelligent optimizing economic dispatch and electric vehicles charging in 5G vehicular networks

In recent years, with the rapid development of 5G networks, the road traffic network composed of vehicles with different energy sources has become more and more complex, and the problems of environmental pollution and road congestion have also become increasingly serious. Electric vehicles are favored by people due to their environmental protection and energy-saving characteristics. However, improper charging dispatching will cause excess energy in charging stations, affecting the power grid and road traffic, such as energy shortages and lower traffic throughput. Therefore, how to design a reasonable charging strategy that can maximize the user’s charging satisfaction and consume the energy of the charging station as much as possible becomes a challenge. Meanwhile, this strategy should consider power economic dispatch to reduce power generation costs and polluting gas emissions. With the support of 5G’s high-bandwidth and low-latency characteristics, this paper designs an intelligent charging model which indirectly reflects the charging satisfaction through the time cost, energy consumption cost, charging cost, and the user’s range anxiety, while consuming the remaining energy of the charging station as much as possible. Due to the uncertainty of wind and photovoltaic power generation, this paper proposes a two-stage economic dispatch model to improve the accuracy of power dispatch and reduce power generation costs and carbon emissions. Due to the highly variable traffic environment and energy demand, we employ proximal policy optimization-based deep reinforcement learning algorithms to realize electric vehicle charging dispatching and charging station power dispatching. Numerical results show the efficiency of our proposed strategy for electric vehicle charging in terms of the convergence speed.

Optimal design of PdAu/In2O3 catalysts for CO2 hydrogenation

Efficient catalyst design has garnered significant interest in recent decades due to its potential to address both the challenges of the greenhouse effect and energy shortages by facilitating the conversion of CO2 into valuable chemicals through catalytic reactions. To investigate maximizing the synergistic effects of supported PdAu catalysts, we conducted first-principles calculations on the activation and decomposition of CO2 and H2 on the PdAu/In2O3(110) system. The results demonstrate that the incorporation of a secondary metal (Au) into the supported Pd catalyst, in conjunction with precise control over Au concentration, exerts influence on both reactant binding energy and activation. The adsorption and activation of CO2 at the interface sites of Au4/In2O3(110) and PdAu3/In2O3(110) are not observed. The transition state for the dissociation of CO2 into *CO and *O is determined based on adsorbed CO2, providing insights into the properties of activated CO2. The Bronsted–Evans–Polanyi relation, which correlates activation barriers (Ea) with reaction energies (Er), was established for the CO2 dissociation mechanism on PdAu/In2O3(110) catalysts using equation E = 0.4Ea + 0.63. It was carried out to investigate the H2-dissociated adsorption processes and mobility energy on various PdAu/In2O3(110) catalysts. Finally, a highly efficient Pd2Au2/In2O3 catalyst for the hydrogenation of CO2 into methanol has been proposed. This research provides valuable insights into the hydrogenation of CO2 to methanol using bimetal-oxide catalysts and contributes to the optimization of the design of PdAu/In2O3 catalysts for CO2 reactions.

Exploring engineering applications of two-sided partial destructive disassembly line balancing problems under electrical limiting and time-of-use pricing

With the rising demand for energy and resource shortages, disassembly as energy-intensive industry should be more attention to energy conservation. However, the non-disassemblability of components (CND) and fixed-constant disassembly costs make this process relatively expensive compared to the potential profit. This paper introduces a two-sided partial destructive disassembly model to solve problem of CND. To more accurately reflect real-world conditions, this study incorporates into the model, which was not considered in previous work. A mixed integer planning model is developed by exploring the impact of time-of-use tariffs on energy consumption and profit, and its correctness is verified in a small-scale case. Subsequently, we propose an improved water cycle algorithm (IWCA) to increase the model solving efficiency. Using non-destructive, destructive, and human-robot hybrid disassembly as benchmark cases, we compare 11 algorithms from existing research, and verify the superiority of IWCA in solving different cases. Finally, the proposed method is validated using automotive engine as an example. The results show that: (1) IWCA is superior to the four latest algorithms in solving the two-sided partially destructive disassembly line balancing problem under electrical limiting and time-of-use electricity pricing. (2) The two-sided partial destructive disassembly effectively addresses the CND issue, reducing the smoothing index by 10.24% and increasing disassembly profit by 3.19% compared to other algorithms. (3) The partial destructive disassembly is better suited for addressing issues in real production processes than non-destructive or complete destructive disassembly methods.

Modified (2′-deoxy)adenosines activate autophagy primarily through AMPK/ULK1-dependent pathway

Autophagy is a conserved self-digestion process, which governs regulated degradation of cellular components. Autophagy is upregulated upon energy shortage sensed by AMP-dependent protein kinase (AMPK). Autophagy activators might be contemplated as therapies for metabolic neurodegenerative diseases and obesity, as well as cancer, considering tumor-suppressive functions of autophagy. Among them, 5-aminoimidazole-4-carboxamide ribonucleoside (AICAr), a nucleoside precursor of the active phosphorylated AMP analog, is the most commonly used pharmacological modulator of AMPK activity, despite its multiple reported “off-target” effects. Here, we assessed the autophagy/mitophagy activation ability of a small set of (2′-deoxy)adenosine derivatives and analogs using a fluorescent reporter assay and immunoblotting analysis. The first two leader compounds, 7,8-dihydro-8-oxo-2′-deoxyadenosine and -adenosine, are nucleoside forms of major oxidative DNA and RNA lesions. The third, a derivative of inactive N6-methyladenosine with a metabolizable phosphate-masking group, exhibited the highest activity in the series. These compounds primarily contributed to the activation of AMPK and outperformed AICAr; however, retaining the activity in knockout cell lines for AMPK (ΔAMPK) and its upstream regulator SIRT1 (ΔSIRT1) suggests that AMPK is not a main cellular target. Overall, we confirmed the prospects of searching for autophagy activators among (2′-deoxy)adenosine derivatives and demonstrated the applicability of the phosphate-masking strategy for increasing their efficacy.

Comprehensive evaluation of the physicochemical properties and pyrolysis mechanism of products from the slow pyrolysis of waste coffee shells

Slow pyrolysis is an efficient method for converting agricultural and forestry wastes into valuable resources, thereby addressing energy shortage and environmental pollution. In this study, a slow-pyrolysis experiment of coffee shells (CS) at 450–850 °C was conducted in a tube furnace. The resulting pyrolysis products were comprehensively characterized via FTIR, Raman spectroscopy, nitrogen adsorption analysis via BET theory, SEM, XPS, and PY-GC-MS. Moreover, the pyrolysis reaction mechanism and N transformation pathway of CS were proposed. The results showed that with the increase in pyrolysis temperature, the yield of biochar gradually decreased, and the yield of biogas gradually increased. At 850°C, the yields of biochar, bio-oil and biogas of CS were 27.13, 35.16 and 37.71 %, respectively (on a dry, ash-free basis). The bio-oil yield was highest at 550 °C, reaching 35.7 %. Notably, biochar exhibited favorable characteristics such as high specific surface area and porosity, demonstrating its potential as both a biomass fuel and an adsorption material. Acetone (78.30 %) and caffeine (8.29 %) was the predominant substance in bio-oil. Biogas was predominantly composed of CO2, CO, CH4 and H2. The pyrolysis process of CS mainly involved the degradation of macromolecules into small molecules, the repyrolysis of small molecules, dehydrogenation, deoxygenation, alkylation, isomerization, and self-condensation. These results provide a theoretical basis for the recycling of waste biomass CS, which has environmental protection significance and economic value.

Solvent self-doping synthesis of nitrogen/oxygen co-doped porous carbon from cellulose as high performance material for multipurpose energy storage

Developing novel heteroatoms co-doped biomass porous carbon with low-cost, tunable physical/chemical properties, and environmental friendliness is an important candidate to face energy shortage and environmental pollution currently. Herein, a novel solvothermal avenue was designed using triethanolamine as self-doping solvent to treat rice straw powders with KOH. The rice straw with triethanolamine derived carbon (RSTCs-1) possessed hierarchical porous structure, N/O diatomic doping, and large specific surface area. The electrochemical energy storage performance of RSTCs-1 was evaluated in the systems of supercapacitors, aqueous zinc ion hybrid supercapacitors (AZHSs), and lithium-ion batteries (LIBs) respectively. As the results, the RSTCs-1 based symmetric supercapacitor exhibited the maximum energy density of ca. 98.4 Wh·kg−1 with the excellent cycling stability. Moreover, both RSTCs-1 AZHSs and RSTCs-1 LIBs achieved the relative high discharge specific capacities of ca. 407.1 and 1906.7 mAh·g−1 at current density of 0.1 A·g−1. These results highlighted the huge potential of the obtained with notable electrochemical performance acting as multifunctional electrode material for the different energy storage devices.

Numerical study on air-cooled battery thermal management system considering the sheer altitude effect

Due to energy shortage and environmental pollution, vehicles and aircraft powered by Li-ion batteries have now received widespread attention. Among various types of battery thermal management systems (BTMSs), the air-cooled BTMS is still the preferred choice due to its affordability, longevity, and simplicity. To ensure the reliable operation of electric vehicles and aircraft at different altitudes, it is extremely meaningful and significant to study the thermal behavior of batteries at different altitudes. Therefore, for the investigation of altitude impact, this paper firstly proposes an indirect decoupling method to address the limitations of using ambient temperature as a single variable. Based on this, several different configurations of air-cooled BTMS have been investigated through numerical simulation. Then, for the tapering-type BTMS with the best thermal performance, the battery behavior at different altitudes is investigated and the influence law of sheer altitude factor is summarized. Subsequently, to address the thermal performance issues at higher altitudes, this research proposes three optimization measures, which include increasing inlet velocity, decreasing inlet temperature, and incorporating phase change material (PCM) layers, respectively. Ultimately, the entropy weight-TOPSIS method is adopted to seek the optimal measure at different parameters. The results indicate that as altitude increases from the standard altitude to 4000 m, the maximum temperature rises significantly, exceeding the permissible temperature range of Li-ion batteries. Besides, in order to effectively confine the maximum temperature within the permissible temperature range at an altitude of 4000 m, the inlet velocity should be increased by at least 2 m/s or the inlet temperature should be reduced by at least 3 °C. Among all optimized solutions, the solution with the addition of 0.4 mm PCM layers is the best based on the comprehensive evaluation of multiple indicators.

Mycorrhizal types determined the response of yield of woody bioenergy crops to environmental factors.

Meeting the demand for energy solely through fossil fuels has posed challenges. To mitigate the risk of energy shortage, woody bioenergy crops as a renewable energy feedstock have been the subject of many researchers. Also, mycorrhizas play an important role in crop productivity and inevitably affect the biomass yield of woody bioenergy crops. Based on a global synthesis of biomass yield of woody bioenergy crops, a framework for identifying and comparing bioenergy crop biomass in response to mycorrhizal type was developed. Our results found that the biomass yield of woody bioenergy crops in descending order was ectomycorrhizas (ECM) crops (10.2 ton DM ha-1year-1) > arbuscular mycorrhizas (AM)+ECM crops (8.8 ton DM ha-1year-1) > AM crops (8.0 ton DM ha-1year-1). In addition, our analysis revealed that the climate had the strongest effect on biomass yield in AM and ECM crops, whereas geography exerted the most significant influence on biomass yield in AM+ECM crops. Furthermore, there were differences in the biomass yield response of different mycorrhizal and plant types to geographic (latitude and elevation) and climatic factors (mean annual temperature (MAT) and mean annual precipitation (MAP)). When cultivating AM crops, we can focus more on temperature conditions-warmer locations, whereas for ECM crops, selecting regions with higher precipitation levels is advantageous. This study revealed the relationship between mycorrhizae and bioenergy crops. It provides data and theoretical support to rationalize differences in different woody bioenergy crops and their different responses to global change and increased production of bioenergy crops.

MPC Optimization Algorithm and Strategy for HVAC System under Smart City Construction

As a giant in energy consumption, buildings urgently need to optimize control strategies for the main energy consuming equipment inside buildings. It is of great significance to design advanced control algorithms to improve the efficiency of the main energy consuming equipment in buildings, namely air conditioning, in the current energy shortage. The model predictive control algorithms and strategies were used in this study to control the HVAC system to improve the energy utilization of the city. Then linear matrix inequality with robust model predictive feedback controller was used to optimize and get the model predictive control optimization algorithm. The research results showed that, under the influence of different factors, the three regions controlled by the model predictive control optimization algorithm showed a little overshooting in the initial state. But it was quickly corrected after adjustment. Meanwhile, the average tracking error of temperature and humidity in each region was 0.139◦ C and 0.13g/kg dry air, respectively. The average predicted mean vote was 0.32. In actual office buildings, the proposed algorithm controlled the temperature within the reference value range of 0.1◦ C throughout the entire process. The total electricity consumption and electricity price costs were reduced by 12.11% and 22.54%, respectively. In summary, the proposed method has good performance for HVAC system application, which can effectively realize energy saving and emission reduction and improve human comfort. This method makes important contributions to promote the construction of smart cities and the development of green buildings.

Revealing the mechanism of bifunctional PtLa electrocatalyst for highly efficient methanol oxidation, hydrogen evolution, and coupling reaction

The development of clean energy solutions, such as fuel cells and hydrogen energy, is crucial for addressing the global energy shortage. Platinum (Pt)-based catalysts are widely used in fuel cells and hydrogen energy generation (for example, via water electrolysis). However, reducing the amount of Pt used while maintaining the catalytic performance of such catalysts is essential. Herein, PtLa catalysts (PtxLay/C) doped with rare earth element lanthanum (La) with different Pt/La atomic ratios were synthesized using a simple chemical reduction method, resulting in Pt65La35/C, Pt78La22/C, Pt97La3/C, and Pt100/C. These PtxLay/C catalysts exhibited excellent electrocatalytic activity and stability in methanol oxidation reaction (MOR), hydrogen evolution reaction (HER), and their coupling reaction as MOR||HER under alkaline conditions. The mechanism by which La doping enhances the electrocatalytic properties and stability of Pt-based catalysts was investigated using X-ray diffraction (XRD), X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), aberration-corrected scanning transmission electron microscopy (AC-STEM), in-situ Fourier transform infrared (FTIR) and operando Raman spectroscopy. For HER, La doping facilitated the adsorption and activation of H2O at Pt sites, improving water dissociation and *OH desorption and reducing Pt poisoning by *OH. This enhances both the catalytic performance and stability of PtxLay/C for HER. Pt78La22/C exhibited a considerably lower overpotential of only 111 mV at 100 mA cm−2 compared to commercial 20 wt% Pt/C (Pt/C-Johnson Matthey (JM)), which requires 153 mV. For MOR, La promotes CO bond cleavage and reduces CO adsorption at the Pt sites, thereby enhancing both the performance and stability of the catalysts. The mass activity (MA) of Pt78La22/C for MOR is 4.44 A mg−1Pt, which is 12.33 times higher than that of Pt/C-JM (0.36 A/mgPt), and surpasses those of Pt65La35/C, Pt97La3/C, and Pt100/C (2.93, 0.24, and 2.91 A mg−1Pt, respectively). Additionally, Pt78La22/C exhibited outstanding catalytic performance for MOR||HER, with a current density of 20 mA cm−2 at 1.030 V, demonstrating good stability with negligible voltage changes after a 15 h chronoamperometry (CA) testing. This study provides a new strategy for synthesizing Pt-based catalysts with enhanced efficiency and low-energy input for HER||MOR.

EUROPE’S ENERGY SECURITY AMID RUSSO-UKRAINE WAR: NAVIGATING THE FUTURE BY EMPLOYING MODIFIED SCENARIO BUILDING TECHNIQUE

The Russo-Ukraine War has exposed the vulnerabilities of Western Europe’s energy security, which has traditionally relied heavily on Russian energy supplies. This excessive dependency has resulted in energy shortages, price volatility, and inflation due to continuing disruptions. By employing the scenario-building technique, this study offers insight into the implications of the War on energy security, strategic choices, and policy considerations for stakeholders as a result of scenario building. The scenario building technique used in this study is modified version that has been modelled by the author by totally overhauling the conventional model in practice. The study projects potential scenarios by leveraging the modified scenarios building and development technique. The findings signify the necessity of thoroughly reevaluating Europe’s stance towards the conflict, identifying looming threats, strategic adaptability regarding the diversification of energy resources and routes, and creating a collaborative European Union framework to articulate collective responses to mitigate the risks. Finally, the results are presented in four major probable future scenarios, each displaying unique challenges and strategic choices. Concluding with geopolitical, geostrategic and geoeconomic triggers and their graduation on impact and probability indices, offer a whole sum view to the stakeholders of the Western Europe to mitigate threats to their energy security amid ongoing Russo-Ukraine War. In a nutshell, while advancing the discourse on Western Europe’s energy security, the study offers a nuanced analysis and detailed strategic roadmap for the future of energy, where these scenarios underscore the critical need to pacify the conflict, anticipate the long-term risks and their mitigation, diversify the energy sources, enhance energy efficiency, formulate resilient energy policies, the collaborative framework within the European Union, collective energy purchase and storage mechanism.

OPTIMAL DISTRIBUTION OF ADDITIONAL ELECTRICAL POWER

The problem of optimal distribution of additional electric power for private houses and households with solar panels or stations in adverse weather conditions over a long period of time is considered. This problem is very relevant for regions where the supply of electric power to houses and households is carried out mainly by green technology. Based on this, it is shown that when weather conditions worsen, which can last for a long time, a problem arises of providing these objects with energy allocated from backup power lines, the capacity of which is insufficient for full energy supply. To solve this problem, especially in the case of a shortage of allocated power, when it is not enough to satisfy all consumers to the maximum, the criteria for distributing the allocated limited power among houses are analyzed, examples are given for calculating the residual energy of a deep-discharge battery for each household in the event of prolonged bad weather conditions. Two options for solving the problem of optimal distribution of the allocated power are considered, taking into account the full simulation model of houses and households with electricity supply from solar panels: - distribution by residual energy taking into account the energy required to charge the batteries of private houses; - distribution by residual energy without charging the batteries. The solution to the problem of distributing limited energy between houses with solar panels is shown using the example of 5 households. For this purpose, the initial conditions and restrictions for each house are modeled. The problem of optimal distribution of allocated energy between these objects is formulated. The model of the problem itself is built and solved using the SOLVER subroutine of the Excel program. The main tables are given, in which it is necessary to enter restrictions, optimization criteria and the objective function, screenshots of the solution of this problem in two versions with the introduction of all restrictions in separate Excel windows. It is shown that solution of the issue of optimal distribution of additional energy allocated to eliminate the shortage of energy in conditions of limitation allows to the following opportunities: - provision of additional energy depending on the time of complete depletion of energy in the house prevents overloading of the electric network, incorrect openings in power lines and other unpleasant situations; - the inclusion of the module of optimal distribution of additional energy into the model during simulation, allows to save time for the seeking of additional energy and thus to increase the overall efficiency of the system. Keywords: solar panels, bad weather, additional energy, optimal distribution.

Studies on Research Methods and Factors Influencing the Urban Heat Island Effect: A Review

In view of the energy shortage and climate warming that are caused by rapid urbanization, the energy structure that relies on traditional fossil fuels brings serious ecological problems, such as the urban heat island effect, acid rain, fog, and haze. To promote urban carbon emission reduction, energy saving, and quality improvement, it is urgent to change the energy structure and develop more new energies. This study provides a systematic review of domestic and international research on the urban heat island effect, its resulting hazards, and related research methods. The correlations between heat island effect and anthropogenic activities as well as energy structure were elucidated. Simultaneously, the deficiencies of the promotion trend of new energy in recent studies were put forward to the point. To promote the "double carbon" orientation based on the natural non-loss method, it provides a valuable reference for further alleviating a series of ecological problems caused by urbanization

Leveraging machine learning to optimize renewable energy integration in developing economies

The integration of renewable energy sources into power grids is a critical challenge for developing economies, where infrastructure limitations, unpredictable energy demand, and policy gaps hinder effective energy transitions. Machine learning (ML) offers transformative potential in addressing these challenges, enabling more efficient and reliable energy systems through advanced data analytics, predictive modeling, and real-time decision-making. This review explores how ML can optimize renewable energy integration by improving forecasting accuracy, enhancing grid stability, and optimizing resource allocation in solar, wind, and hydropower systems. Machine learning algorithms are particularly effective in predicting energy demand and renewable resource availability, allowing for better alignment between energy supply and consumption patterns. By leveraging ML-based predictive models, grid operators can mitigate the risks of energy shortages or oversupply, improve grid stability, and reduce operational costs. Furthermore, the application of ML in renewable energy systems provides opportunities for developing economies to leapfrog traditional energy infrastructure limitations by adopting smart grids that integrate real-time data to enhance decision-making and efficiency. This review also reviews case studies from Africa and Latin America, highlighting successful implementations of ML in renewable energy systems. These examples underscore the potential for ML to accelerate the deployment of sustainable energy solutions, while also addressing technical, economic, and policy barriers that exist in developing contexts. With continued advancements in machine learning, combined with supportive regulatory frameworks and investment in digital infrastructure, developing economies have the potential to realize substantial gains in renewable energy integration. This review concludes by discussing future trends, challenges, and opportunities for leveraging machine learning to optimize renewable energy integration, ultimately contributing to sustainable development and energy security in emerging markets.

Magnetic Phase-Change Microcapsules with High Encapsulation Efficiency, Enhancement of Infrared Stealth, and Thermal Stability

Due to energy shortages and the greenhouse effect, the efficient use of energy through phase-change materials (PCMs) is gaining increased attention. In this study, magnetic phase-change microcapsules (Mag-mc) were prepared by suspension polymerization. The shell layer of the microcapsules was formed by copolymerizing methyl methacrylate and triethoxyethylene silane, with the latter enhancing the compatibility of the shell layer with the magnetic additive. Ferric ferrous oxide modified by oleic acid (Fe3O4(m)) was added as the magnetic additive. Differential scanning calorimetry (DSC) testing revealed that the content of phase-change materials in microcapsules without and with ferric ferrous oxide were 79.77% and 96.63%, respectively, demonstrating that the addition of Fe3O4(m) improved the encapsulation efficiency and enhanced the energy storage ability of the microcapsules. Laser particle size analysis showed that the overall average particle sizes for the microcapsules without and with ferric ferrous oxide were 3.48 μm and 2.09 μm, respectively, indicating that the incorporation of magnetic materials reduced the size and distribution of the microcapsules. Thermogravimetric analysis indicated that the thermal stability of the microcapsules was enhanced by the addition of Fe3O4(m). Moreover, the infrared emissivity of the microcapsule-containing film decreased from 0.77 to 0.72 with the addition of Fe3O4(m) to the shell of microcapsules.

Energy management strategy of integrated adaptive fuzzy power system in fuel cell vehicles

Fuel cell vehicles are a reliable solution to address energy shortages. However, when the road conditions are complex, the system distributes power unevenly between fuel cells and lithium batteries, and cannot effectively absorb the energy generated by braking. In response to this issue, an adaptive control strategy is adopted to allocate the required power of the car to two types of batteries in real time. Fuzzy logic is used to continuously optimize the relevant parameters of the controller based on the vehicle state, and a multi-island genetic algorithm is used to optimize the control strategy, enhancing the global search ability of the control strategy and increasing the vehicle’s ability to absorb and reuse the energy generated by braking. The experiment findings denote that the optimized control strategy increases the remaining capacity of lithium batteries by an average of 1.67%, increases energy recovery by an average of 135 W, increases the overall energy recovery rate by an average of 2.8%, and reduces vehicle fuel consumption by an average of 0.24 L/100 Km. It can be concluded that the optimized adaptive fuzzy control strategy can reduce the probability of over-charging and discharging of lithium batteries and improve the battery life. Meanwhile, the optimized strategy can improve the energy reuse rate, reduce vehicle fuel consumption, lower usage costs. The optimized strategy provides a reference for subsequent research on energy management of fuel cell vehicles.

Elastocaloric Heat Pump by Twist Induced Periodical Non-Linear Stress for Low Hysteresis and High Carnot Efficiency.

Elastocaloric cooling is one of the most promising solid-state cooling approaches to address the issues of energy shortage and global warming. However, the cooling efficiency and cycle life of this technology need to be improved, and the required driving force shall be reduced. Here, a novel elastocaloric heat pump by periodic non-linear stress is developed by employing fiber twisting and separated cooling and heating media. The non-linear stress generated by fiber twisting yields a hierarchical, rigid-yet-flexible architecture and a periodic entropy spatial distribution, which result in a low mechanical hysteresis work and a high cooling efficiency (a maximum material coefficient of performance (COP) of 30.8 and a maximum Carnot efficiency of 82%). The torsional non-linear stress inhibits crack propagation and results in a highly extended cycle life (14752 cycles, more than ten times of fiber stretching). The heat pump exhibits a maximum average temperature span of 25.6 K, a maximum specific cooling power of 1850W Kg-1, a maximum device COP of 19.5, and a maximum device power of 5.0W, under each optimal condition.