Energy Domain Research Articles

ЗАСТОСУВАННЯ МЕТОДУ РОЮ ЧАСТОК В ОПТИМІЗАЦІЙНИХ ЗАДАЧАХ ЕНЕРГЕТИКИ

The article considers the application of the particle swarm method in energy domain. The problem of effective load distribution of energy-generating capacities under the conditions of minimum fuel consumption is one of those that arises most often. The economic expediency of the operation of one or another power plant at the appropriate capacity determines the distribution of generating capacities in the power system. It is no secret that power units of thermal power plants, which were built in different time periods, differ in their cost characteristics. This makes it necessary to find optimal configurations of the power system, in which the relevant energy objects are involved. Particle Swarm Optimization (PSO) is a computational optimization method inspired by the social behavior of birds in a flock or fish in a shoal. This method was first proposed by Kennedy and Eberhart in 1995. In PSO, a population of possible solutions, called particles, moves through the search space according to a set of mathematical rules. The motion of each particle affects its own bestknown position and the global best-known position of the entire population. The basic idea is that each particle adjusts its position based on its own experience and that of the entire swarm. This correction takes place with the help of two main components: 1. Cognitive component (personal best result): The particle remembers the best solution it found before. 2. Social component (global best result): A particle also takes into account the best solution found by any other particle in the swarm. These components are used to update the particle's velocity and position iteratively, with the goal of converging to an optimal solution. PSO is widely used in various optimization problems, including engineering design, robotics, finance, and data analysis. It is known for its simplicity, ease of implementation, and ability to solve non-linear, non-convex optimization problems. However, like any optimization algorithm, its performance can be sensitive to the parameters and the nature of the problem to be solved. The article solves a typical problem of distributing the total load between two thermal power plants under the conditions of minimum fuel consumption. The obtained values of the solutions confirm commonly known the statements about the achievement of adequate indicators in the range from 10 to 30 particles, in our case - 20.Analyzing the obtained results, one can see that the objective function changes almost linearly from the very beginning until the 30th iteration, after which the improvement in the result is almost imperceptible. The main reason is that at this moment the result of the algorithm is as close as possible to the reference value, namely 250. That is, in fact, it can be considered that the solution comes at the 31st iteration. Carrying out a comparison with the solution of such a problem using the genetic algorithm from the previous work, it can be seen that when solving such a problem, the algorithms demonstrate similar performance with comparable accuracy of the result. From the above studies, it can be concluded that evolutionary algorithms can be used to solve similar energy problems.

PowerPulse: Power energy chat model with LLaMA model fine‐tuned on Chinese and power sector domain knowledge

AbstractRecently, large‐scale language models (LLMs) such as chat generative pre‐trained transformer and generative pre‐trained transformer 4 have demonstrated remarkable performance in the general domain. However, inadaptability in a particular domain has led to hallucination for these LLMs when responding in specific domain contexts. The issue has attracted widespread attention, existing domain‐centered fine‐tuning efforts have predominantly focused on sectors like medical, financial, and legal, leaving critical areas such as power energy relatively unexplored. To bridge this gap, this paper introduces a novel power energy chat model called PowerPulse. Built upon the open and efficient foundation language models (LLaMA) architecture, PowerPulse is fine‐tuned specifically on Chinese Power Sector Domain Knowledge. This work marks the inaugural application of the LLaMA model in the field of power energy. By leveraging pertinent pre‐training data and instruction fine‐tuning datasets tailored for the power energy domain, the PowerPulse model showcases exceptional performance in tasks such as text generation, summary extraction, and topic classification. Experimental results validate the efficacy of the PowerPulse model, making significant contributions to the advancement of specialized language models in specific domains.

Risk perception as a barrier to renewable energy finance – a study of debt investors in the Indian context

Purpose This study aims to explore the perceptions of investors regarding the risks associated with funding renewable energy projects in India, as well as the various factors that influence these perceptions. The investigation is limited to debt providers and seeks to pinpoint the primary risks that bankers perceive and the drivers that shape these perceptions. Design/methodology/approach This study draws on interviews and surveys of Indian bank executives, investigating how finance providers perceive risks in the Indian context and the factors driving such perceptions. Qualitative interviews have been used for operationalizing “risk perception” within the renewable energy domain, followed by a quantitative survey and exploratory factor analysis. Findings The authors find that experience and capacity are the most important factors that account for 30% of the overall variance. The second factor, which accounts for 15% of the variance, includes the perceived risks in funding renewable energy projects as compared to infrastructure projects. Among individual risks, the authors find that bankers perceive technological risk to be the lowest (5%) and contractual and regulatory risks as the highest (66%) in renewable energy projects. Research limitations/implications The study contextualizes risk perception toward renewable energy investments in the Indian context by drawing from the risk perception literature and qualitative interviews with senior bankers. It presents empirical evidence on the decision-making behavior of bankers, who are important stakeholders of the renewable energy ecosystem. The main limitation of the study is the relatively small sample, and generalizing the results to the broader population might require a larger sample. This will facilitate the use of confirmatory factor analysis and structural equation modeling, which can facilitate a more comprehensive understanding of risk perceptions in renewables financing. Originality/value Insights gained can be used to provide policy recommendations for improving the financing ecosystem of renewable energy projects. The research significantly contributes to the extant literature within the renewable energy financing domain for emerging economies.

Research on Internet Technology Innovation and Information Security Challenges in New Vehicle Energy Sector

With the rapid proliferation and advancement of new energy vehicles, Internet technology has garnered significant attention and application in this field. This study focuses on innovations in Internet technology in the domain of new automotive energy and the associated information security challenges. The innovations in Internet technology relevant to new energy vehicles, including Telematics, intelligent driving, and remote control, are initially explored. Subsequently, the information security challenges brought about by these innovations are analyzed, encompassing various aspects such as data privacy breaches, remote attacks, and malware infections. The significance of information security in the development of new energy vehicles is emphasized. Solutions and suggestions, including the strengthening of encryption technology, the establishment of a robust security framework, and the enhancement of user education and awareness, are put forward. In conclusion, the current state of research on Internet technology innovation and information security challenges is summarized, and it is suggested that future research should delve deeper into the trajectory of Internet technology development in the field of new energy vehicles, along with the attendant information security challenges, to provide theoretical and practical support for promoting the healthy development and secure application of the new energy vehicle industry.

Experimental study and first-principles calculation of enhanced electrochemical properties of Bi0.7Sr0.3FeO3-δ (BSFO) – La0.8Sr0.2MnO3-δ (LSM) composite cathodes for intermediate-temperature solid oxide fuel cells

In order to enhance the electrochemical performance of cobalt-free Bi0.7Sr0.3FeO3-δ (BSFO) cathodes for intermediate-temperature solid oxide fuel cells (IT-SOFCs), a composite cathode consisting of BSFO and La0.8Sr0.2MnO3-δ (LSM) is developed. The optimized polarization resistance of the composite cathode (0.11 Ω cm2 at 800 °C) is found to be only 61 % that of pure BSFO (0.18 Ω cm2 at 800 °C), indicating a significant improvement in performance. The first-principles calculation indicates that the enhanced electrochemical performance is mainly due to the domain of low oxygen vacancy formation energies of BSFO-LSM-L interfaces (Evac, −3.63 eV) and LSM (Evac, −3.11 eV). The anode-supported single cell of NiO-SDC/SDC/BSFO－LSM shows a peak power density of 825.83 mW cm−2 at 800 °C using wet H2 (∼3 vol% H2O) as fuels and ambient air as oxidants. In addition, the single cell exhibits a good stability at the electric current density 400 mA cm−2 for 50 h measured at 650 °C.

Good Quality of Life among People Living with Diabetes Mellitus Visiting the Outpatient Department of Endocrinology in a Tertiary Care Centre.

Assessing the quality of life of individuals living with diabetes is crucial for ensuring optimal care and effective management of complications related to their condition. Diabetes is one of the leading causes of preventable mortality and morbidity among non-communicable diseases. The study aims to find out the prevalence of the good quality of life of people living with Diabetes mellitus visiting a tertiary care centre. A descriptive cross-sectional study was conducted among type 2 diabetic patients visiting the Outpatient Department of Endocrinology in a tertiary care centre from 30 June 2022 to 20 July 2022. Ethical approval was obtained from the Institutional Review Committee. A systematic random sampling technique was used. D-39 questionnaires were administered via face-to-face interviews. Point estimate was calculated at 95% Confidence Interval. Among 118 patients living with diabetes, good quality of life was seen in 97 (82.20%) (75.30-89.10, 95% Confidence Interval). This study found that the energy and mobility domain has the highest mean score of 26.7±7.8. The prevalence of good quality life of people living with Diabetes mellitus was found to be higher than other similar studies done in similar settings. diabetes mellitus; patients; quality of life.

Doctor Who and the seeds of anxiety: exploring popular narratives of energy justice and exploitation

Achieving and implementing sustainable socio-technical transitions requires high levels of public understanding and acceptance across multiple sectors including energy, transport, heating, and other domains. However, the level of public understanding of even the most “obvious” high carbon sector, energy production, and of the surrounding class relations around its production and consumption, remains under-examined. A specific gap is how energy narratives diffuse through and are shaped by widely accessible popular sources. The extent that public understanding may be informed by how popular culture presents issues of energy, class and access, merits consideration, yet is underexplored. This article contributes to the emerging field of energy humanities by examining five stories from the popular BBC science fiction series Doctor Who, broadcast in the 1960s, 70s and 80s. It situates them in the time they were created and finds that the program provides representations of energy production and class and that these stories yield critique of political repression of subjugated peoples. However, Doctor Who ultimately does not convey a meaningful sense of endogenous social change as questions of justice are resolved by a saviour arriving from without. Our contention is that Doctor Who distils and represents via drama a degree of support for energy justice critique present in a particular place and time, the UK from the 1960s to the 1980s. Yet we also contend the energy justice critique apparent in Doctor Who is limited by several interlocking characteristics: the paternalistic rescue of victims of oppression matched by the uncritical validation of other and newer energy sources; therefore, the need for technological progress is endorsed. These limitations in normative energy justice appear in a programme viewed by millions and we contend these limitations prevailed in the wider culture which produced and watched it. Ultimately therefore interpretation of Doctor Who reflects on the theme of denialism, of the need for a just energy transition, which is conspicuously relevant today across multiple nations.

Progress of photonuclear cross sections for medical radioisotope production at the SLEGS energy domain

Progress of photonuclear cross sections for medical radioisotope production at the SLEGS energy domain

Do renewable energy sources improve air quality? Demand- and supply-side comparative evidence from industrialized and emerging industrial economies.

This study is an attempt to comparatively analyze the impact of renewable energy sources on air quality represented by particulate matter 2.5 concentrations utilizing panel data of 60 countries which are divided into two sub-panels industrialized economies and emerging industrial economies over the period 2010-2019. The study adopts both demand- and supply-side approaches and hence renewable sources are handled in two different structures, i.e., renewable energy consumption and production. Empirical results from both demand- and supply-side regressions strongly confirm the positive impact of renewable sources on air quality in all country groups, meaning that higher renewable energy production and consumption bring about improvement in air quality. In addition, this positive impact of renewables on air quality turned out to be higher in emerging industrial economies than that in industrialized ones. To be more precise, as all control variables are considered, a 10% increase in the production of renewable energy sources brings about a 0.66% improvement in air quality in industrialized economies while its impact is a value of 1.33% in emerging industrial economies. On the other hand, a 10% increase in consumption of renewable energy sources leads to a 0.62% improvement in air quality in industrialized economies and a 1.97% improvement in emerging industrial economies. As for control variables, industrialization gives rise to an increase in air pollution in all country groups, whereas economic growth and trade openness function as favorable factors for air quality. Although population density improves air quality in industrialized economies, it is found as one of the main pollutant factors in emerging industrial economies. Overall results proved that renewable sources improve air quality by reducing particulate matter 2.5 concentrations. Therefore, these countries, especially emerging industrial economies, should replace primitive energy sources like fossil fuels with renewables to bring down environmental degradation up to a reasonable level and increasingly continue to invest in renewable energy domain to reach their environmental sustainability targets. The study also provides some additional policy implications.

Multi-Agent Systems and Machine Learning for Wind Turbine Power Prediction from an Educational Perspective

Artificial intelligence (AI) is an umbrella term that encompasses different fields of study, and topics related to these fields are addressed separately or within the scope of AI. Multi-agent systems (MASs) and machine learning (ML) are the core concepts of AI that are taught during AI courses. The separate explanation of these core research areas is common, but the emergence of federated learning has triggered their combined usage. This paper describes a practical scenario in the energy domain where these technologies can be used together to provide a sustainable energy solution for predicting wind turbine active power production. The projects in the AI course were assigned prior to the step-by-step learning of MASs and ML. These concepts were applied using a wind turbine energy dataset collected in Turkey to predict the power production of wind turbines. The observed performance improvements, achieved by applying various agent architectures and data partitioning scenarios, indicate that boosting methods such as LightGBM yield better results even when the settings are modified. Additionally, a questionnaire about the assignments was filled out by the student groups to assess the impact of learning MASs and ML through project-based education. The application of MASs and ML in a hybrid way proves valuable for learning core concepts related to AI education, as evidenced by feedback from students.

A special phenomenon of wave interactions: An application of nonlinear evolution equation in (3+1)-dimension

The Lie group transformation approach can be used in the reduction of the generalized (3+1)-dimensional nonlinear evolution equation to ordinary differential equations (ODEs). In this work, infinitesimal generators, commutator table of Lie algebra, adjoint representation of subalgebras, symmetry group, and similarity reduction for (3+1)-dimensional shallow-water-like (gSWL) equation are obtained. Interactions of soliton solutions of a gSWL equation are evaluated that depict how near-inertial waves affect the dynamics of barotropic and baroclinic balanced flows. It is shown that the temporo-spatial localization of multi-soliton complexes (MSCs), gradually escalating incoherent interactivity amidst multi parameter group of MSCs along with spatial inhomogeneity alter the shape of quasi-periodic MSCs. The interactions among bright and dark solitons are shown. The increased number of solitons and increased self-interactions among solitary waves contribute to an unstable wave field. Breathers have a well-separating and initial localized energy domain. A limiting case of breather solutions is the peregrine soliton. Thus, interactions among these soliton structures show that multiple breathers retain their wave characteristic.

Democratizing AI from a Sociotechnical Perspective

Artificial Intelligence (AI) technologies offer new ways of conducting decision-making tasks that influence the daily lives of citizens, such as coordinating traffic, energy distributions, and crowd flows. They can sort, rank, and prioritize the distribution of fines or public funds and resources. Many of the changes that AI technologies promise to bring to such tasks pertain to decisions that are collectively binding. When these technologies become part of critical infrastructures, such as energy networks, citizens are affected by these decisions whether they like it or not, and they usually do not have much say in them. The democratic challenge for those working on AI technologies with collectively binding effects is both to develop and deploy technologies in such a way that the democratic legitimacy of the relevant decisions is safeguarded. In this paper, we develop a conceptual framework to help policymakers, project managers, innovators, and technologists to assess and develop approaches to democratize AI. This framework embraces a broad sociotechnical perspective that highlights the interactions between technology and the complexities and contingencies of the context in which these technologies are embedded. We start from the problem-based and practice-oriented approach to democracy theory as developed by political theorist Mark Warren. We build on this approach to describe practices that can enhance or challenge democracy in political systems and extend it to integrate a sociotechnical perspective and make the role of technology explicit. We then examine how AI technologies can play a role in these practices to improve or inhibit the democratic nature of political systems. We focus in particular on AI-supported political systems in the energy domain.

A Concise Review of Nanoparticles Utilized Energy Storage and Conservation

Nanoparticles have revolutionized the landscape of energy storage and conservation technologies, exhibiting remarkable potential in enhancing the performance and efficiency of various energy systems. This review explores the versatile applications of nanoparticles in three key domains: battery technologies, supercapacitors, and solar energy conversion. In the realm of battery technologies, nanostructured particles have emerged as crucial catalysts and electrode materials, significantly elevating the energy density, cycling stability, and charge/discharge rates of batteries. By manipulating the surface chemistry and structure of nanoparticles, researchers have achieved breakthroughs in overcoming traditional limitations, paving the way for next-generation high-capacity and long-lasting batteries. The integration of tiny particles in supercapacitors has led to remarkable advancements in energy storage and rapid energy delivery. Nanoparticle-based electrodes have exhibited exceptional surface area, porosity, and conductivity, contributing to enhanced energy and power densities. The synergy of nanomaterials with novel electrolytes has also extended the operational lifespan of supercapacitors, addressing concerns regarding energy loss over cycles. Furthermore, nanoparticles have played a pivotal role in the field of solar energy conversion. In photovoltaics, nanoparticles with tailored optoelectronic properties have enabled improved light absorption, charge separation, and electron transport, ultimately boosting the efficiency of solar cells. Moreover, nanoparticles have been employed as catalysts in photocatalytic systems for solar fuel generation, driving the sustainable production of clean energy carriers. In this concise review, we highlight the recent advancements, challenges, and future prospects of nanoparticles in these critical energy domains. While the transformative impact of nanoparticles is evident, several challenges such as large-scale synthesis, cost-effectiveness, and long-term stability must be systematically addressed to ensure their seamless integration into practical energy applications. As researchers continue to explore novel synthesis techniques and innovative nanoarchitectures, nanoparticles are poised to reshape the energy landscape, accelerating the transition toward a more sustainable and efficient energy future.

Quantifying chaos and randomness in magnetar bursts

ABSTRACT In this study, we explore the dynamical stability of magnetar bursts within the context of the chaos-randomness phase space for the first time, aiming to uncover unique behaviours compared to various astrophysical transients, including fast radio bursts (FRBs). We analyse burst energy time series data from active magnetar sources SGR J1550−5418 and SGR J1935+2154, focusing on burst arrival time and energy differences between consecutive events. We find a distinct separation in the time domain, where magnetar bursts exhibit significantly lower randomness compared to FRBs, solar flares, and earthquakes, with a slightly higher degree of chaos. In the energy domain, magnetar bursts exhibit a broad consistency with other phenomena, primarily due to the wide distribution of chaos-randomness observed across different bursts and sources. Intriguingly, contrary to expectations from the FRB-magnetar connection, the arrival time patterns of magnetar bursts in our analysis do not exhibit significant proximity to repeating FRBs in the chaos-randomness plane. This finding may challenge the hypothesis that FRBs are associated with typical magnetar bursts but indirectly supports the evidence that FRBs may primarily be linked to special magnetar bursts like peculiar X-ray bursts from SGR J1935+2154 observed simultaneously with Galactic FRB 200428.

Blockchain for energy efficiency training in the construction industry

The construction sector faces the urgent need to prioritize energy efficiency due to an increasingly stringent regulatory landscape in response to the climate change agenda. Central to this transition is the pivotal role of education in equipping professionals with the necessary knowledge and skills. Educational solutions have emerged as powerful tools for promoting awareness and interventions to mitigate climate change. This article provides a case study that highlights the successful utilisation of computer technology in delivering digital solutions to advance energy education and promote more informed energy practices in the construction industry. The utilisation of digital technologies can enhance collaborative efforts in energy efficiency training, which is of critical significance in ensuring the security, sovereignty, transparency, immutability, and decentralisation of interventions related to energy education. This paper presents a framework that utilises Blockchain technology to facilitate training labelling and authenticity based on smart contracts and mobile passports to provide a secure and efficient solution for the delivery of training and education in the energy domain. Our research examines the challenges and opportunities related to energy efficiency training within the construction industry. By integrating industry-specific insights, exemplifications, and case studies, we provide an in-depth understanding of the interconnection between energy efficiency education and digital solutions with the unique context of the construction industry. We underscore the importance of leveraging digital platforms as educational tools to foster a deeper understanding and adoption of energy-efficient practices. We demonstrate that educational solutions play a pivotal role in driving awareness and interventions for mitigating climate change, greatly empowering individuals and organizations to adopt energy-efficient practices and to address sustainability objectives.

Facilitating Energy Monitoring and Fault Diagnosis of Pneumatic Cylinders with Exergy and Machine Learning

Pneumatic systems are widely used in industrial production sectors. Increasing penetrations of Intelligent Manufacturing and Green Manufacturing are highlighting the drawbacks of pneumatic technology in terms of particularly low energy efficiency and low-level fault diagnosis intelligence. Here we propose that a combined energy-based maintenance and fault diagnostic approach for pneumatic systems could be a game-changer for pneumatics. In this study, a pneumatic cylinder with internal and external leakages is examined and a typical pneumatic experimental system is built. Exergy is adopted for evaluating the available energy of compressed air. Data-driven machine learning models, SAE + SoftMax neural network model and SAE + SVM model, are developed for fault detection and diagnosis. By comparing different machine learning methods with various pressure, flowrate, and exergy data, it is found that the diagnostic accuracy when using pressure and flowrate data is highly dependent on operating conditions, while the diagnostic accuracy when using exergy data is always high regardless of operating conditions. This indicates the promise of developing an exergy-based maintenance paradigm in pneumatic systems. Besides, with exergy and machine learning, more downstream faults can be detected and diagnosed with fewer upstream sensors. This study is the first attempt to develop an exergy-based maintenance paradigm in pneumatic systems. We hope it could inspire the following investigations in other energy domains.

Built environment, daily activities and carbon emissions: Insights from an eight-week app-based survey in the Province of Utrecht (Netherlands)

The built environment affects our lifestyles and associated carbon emissions. Most studies investigating this link, however, have used census data or surveys that can hardly capture patterns of daily activities over time. In this paper, we present the results of an eight-week monitoring campaign during which over 300 residents of the Province of Utrecht (Netherlands) recorded their activities pertaining to the domains of travel, housing energy and food consumption using a mobile app. Data were analyzed through structural equation models (SEMs) accounting for residential self-selection.Once controlling for socio-demographics, an aggregate measure of compactness encompassing population density, intensity of development and accessibility of transit and grocery stores was found to be associated with emissions in all three domains via multiple mediating variables. More compact neighborhoods may curb emissions by reducing car ownership and therefore driving, but seem to have no impact on other travel habits. Yet, they tend to be associated with residential settings (i.e. older multi-dwelling units) showing higher energy emission factors due to less reliance on green sources. Additionally, lower rates of car ownership in more compact neighborhoods are associated with less carbon-intensive diets. While our findings endorse the compact city as an instrument to help cut individual carbon emissions, they also raise warnings about the ability of low-carbon technologies (e.g. electric vehicles, photovoltaic systems) to make this spatial planning model look obsolete in the fight against climate change. Mobile apps like ours may help disentangle this puzzle though greater automation is needed to get unbiased information.

Gothenburg Digital Twin. Modelling and communicating the effect of temperature change scenarios on building demand

The article presents the first prototype of a web-based City Digital Twin (CDT) viewer developed for the city of Gothenburg to promote the understanding of the interrelation between the spatial, energy and climate change domain related to building stocks and to support stakeholders’ collaboration. The focus is on the development process of the viewer and decisions taken to target the users. 3d spatial data about the current city combined with building energy demand modelling following the Intergovernmental Panel on Climate Change scenarios provide the basis that potentially allows local actors to take informed decisions about decarbonization measures in a long-term perspective. First, future climate conditions are modelled for Gothenburg following the IPCC pathways 2.6, 4.5, and 8.5 for the year 2050. Second, a Building Stock Model is employed to simulate the energy use of buildings by using the climate scenarios as climate boundary conditions. Finally, results are visualized in the CDT viewer showing that future climate conditions contribute to an overall decrease in final energy demand up to 18% in spring and autumn months and an increase up to 1.5% in summer months.

Genetic atom search-optimized in vivo bioelectricity harnessing from live dragon fruit plant based on intercellular two-electrode placement

Bioelectricity is a promising alternative renewable energy source that can be produced from live plants and trees. However, previous experimental studies mostly applied non-sustainable bioelectricity extraction techniques from cut-off stem or leaves and neglected the optimum placement of electrodes for maximizing energy extraction without impeding plant growth. Electrode placement and penetration are crucial in energy extraction since they greatly influence electrical generated output enhancement. Relatively, along with the common plants used for bioelectricity extraction, the dragon fruit tree has the potential to be explored as an alternative bioelectricity source since it is widely abundant in many regions. With that, this work introduced a novel integrated genetic-population metaheuristic-based optimization model that was developed centered on in vivo stem bioelectricity extraction from dragon fruit tree to determine the exact optimum distance of silver-coated copper pin-type anodes and cathodes for maximum bioelectricity extraction through intercellular across vascular bundle (icVB) and inter-parenchymal cells (iPC) electrode penetration techniques, and incorporated the cradle-to-gate Life Cycle Assessment methodology to properly account the environmental impacts of the two intercellular penetration approaches. Multigene genetic programming was performed to formulate the fitness function followed by a comparative atom search (ASO), shuffle frog-leaping, and elephant herding-based bioelectricity harnessing optimization. Thus, ASO demonstrated the highest attainable fitness value and conformed well with both electrode placement treatments. This subsequently verified that ASO-based iPC penetration, yielding 58.923 J, surpasses icVB, which only yielded 13.909 J in terms of the total harnessed energy stored throughout the 30-day experiment. Overall, the genetic ASO-iPC with an electrode distance of 4.488 inches produced a higher yield of harnessed bioelectricity while incurring no significant damage and causing fewer environmental impacts compared to the ASO-icVB treatment. This developed technique can minimize greenhouse gas emissions while also expanding the application of evolutionary computing in agriculture and alternative energy domains.

Deterministic approach to decoupling amplitude from phase via a single-layered metasurface

Controlling the phase and amplitude of reflected waves has been challenging due to the inherent coupling between them. Changes in the phase of a wave often result in changes in its amplitude, and vice versa. This coupling makes it difficult to control both parameters independently. Here we report a deterministic approach, illustrated by an equivalent circuit model, to decouple the phase from the amplitude of reflected waves based on a single-layer metasurface. With the variables conversion and appropriate impedance patterns, this approach allows for the generation of 1-bit phase meta-atoms with a natural phase difference of 180° and controllable amplitude ranging from 0 to 0.9. Furthermore, the decoupling frequency can be customized by using different permittivities and dielectric thickness. These 1-bit phase meta-atoms, whose amplitude can be independently controlled, have potential in wavefront manipulation such as multi-beam antennas and hybrid radar cross section reduction metasurfaces. As a proof of concept, a metasurface for backscatter suppression is demonstrated and measured. The reflected wave is scattered by wavefront modulation with phase control in the spatial domain, while the energy is absorbed by amplitude control in the energy domain, resulting in extremely low backward scatterings in a wideband. In addition to the discussion of passive metasurfaces, the active case is also expanded on. The results of our paper demonstrate the potential of the phase- and amplitude-decoupled approach in designing multi-function metasurfaces for wavefront manipulation and radar applications.