With the rapid proliferation of portable and wearable electronics, energy autonomy through efficient energy harvesting has become paramount. Thermoelectric generators (TEGs) stand out as promising candidates due to their silent operation, high reliability, and maintenance-free nature. This paper presents the design, fabrication, and analysis of a micro-scale TEG for powering such devices. A planar configuration was employed for its inherent miniaturization advantages. Finite element analysis using ANSYS reveals that a double-layer device under a 50 K temperature gradient generates an impressive open-circuit voltage of 1417 mV and a power output of 2.4 μW, significantly exceeding its single-layer counterpart (226 mV, 0.12 μW). Validation against the analytical model results yields errors within 2.44% and 2.03% for voltage and power, respectively. Furthermore, a single-layer prototype fabricated using paper shadow masks and sputtering deposition exhibits a voltage of 131 mV for a 50 K temperature difference, thus confirming the feasibility of the proposed design. This work establishes a foundation for developing highly efficient micro-TEGs for powering next-generation portable and wearable electronics.

Aiming at the problems of high stochasticity and volatility of power loads as well as the difficulty of accurate load forecasting, this paper proposes a power load forecasting method based on CEEMDAN (Completely Integrated Empirical Modal Decomposition) and TCN-LSTM (Temporal Convolutional Networks and Long-Short-Term Memory Networks). The method combines the decomposition of raw load data by CEEMDAN and the spatio-temporal modeling capability of TCN-LSTM model, aiming to improve the accuracy and stability of forecasting. First, the raw load data are decomposed into multiple linearly stable subsequences by CEEMDAN, and then the sample entropy is introduced to reorganize each subsequence. Then the reorganized sequences are used as inputs to the TCN-LSTM model to extract sequence features and perform training and prediction. The modeling prediction is carried out by selecting the electricity compliance data of New South Wales, Australia, and compared with the traditional prediction methods. The experimental results show that the algorithm proposed in this paper has higher accuracy and better prediction effect on load forecasting, which can provide a partial reference for electricity load forecasting methods.

This article represents a novel study of the design and analysis of a wind turbine system that includes a line-side permanent magnet synchronous generator (PMSG) with an ultra-step-up DC-DC converter for voltage regulation. Integrating renewable energy sources such as wind power into the grid requires efficient and reliable power conversion systems to handle fluctuating power and ensure a stable power supply. The wind turbine system utilizes a PMSG, which offers several advantages over traditional induction generators, including higher efficiency, reduced maintenance, and better power quality. The line-side configuration allows for increased control and flexibility, allowing the system to respond dynamically to grid conditions. This wind turbine system involves the integration of a grid-side PMSG-fed DC-DC converter between the PMSG and the grid. The converter enables a seamless flow of electricity between the wind turbine and the grid. By actively controlling the intermediate circuit voltage, the converter efficiently regulates the output voltage of the wind turbine and thus enables constant power generation regardless of fluctuating wind speeds. The simulation outcomes illustrate the efficacy of the proposed system in achieving voltage regulation and seamless integration with the grid. Performance is evaluated under various operating conditions and compared to conventional wind turbines.

Microbial fuel cells (MFCs) have the potential to directly convert the chemical energy in organic matter into electrical energy, making them a promising technology for achieving sustainable energy production alongside wastewater treatment. However, the low extracellular electron transfer (EET) rates and limited bacteria loading capacity of MFCs anode materials present challenges in achieving high power output. In this study, three-dimensionally heteroatom-doped carbonized grape (CG) monoliths with a macroporous structure were successfully fabricated using a facile and low-cost route and employed as independent anodes in MFCs for treating brewery wastewater. The CG obtained at 900 °C (CG-900) exhibited excellent biocompatibility. When integrated into MFCs, these units initiated electricity generation a mere 1.8 days after inoculation and swiftly reached a peak output voltage of 658 mV, demonstrating an exceptional areal power density of 3.71 W m-2. The porous structure of the CG-900 anode facilitated efficient ion transport and microbial community succession, ensuring sustained operational excellence. Remarkably, even when nutrition was interrupted for 30 days, the voltage swiftly returned to its original level. Moreover, the CG-900 anode exhibited a superior capacity for accommodating electricigens, boasting a notably higher abundance of Geobacter spp. (87.1%) compared to carbon cloth (CC, 63.0%). Most notably, when treating brewery wastewater, the CG-900 anode achieved a maximum power density of 3.52 W m-2, accompanied by remarkable treatment efficiency, with a COD removal rate of 85.5%. This study provides a facile and low-cost synthesis technique for fabricating high-performance MFC anodes for use in microbial energy harvesting.

This retrospective begins with Galvani\'s experiments on frogs at the end of the 18th century and his discovery of \'animal electricity\'. It goes on to illustrate the numerous contributions to the field of physical chemistry in the second half of the 19th century (Nernst\'s equilibrium potential, based on the work of Wilhelm Ostwald, Max Planck\'s ion electrodiffusion, Einstein\'s studies of Brownian motion) which led Bernstein to propose his membrane theory in the early 1900s as an explanation of Galvani\'s findings and cell excitability. These processes were fully elucidated by Hodgkin and Huxley in 1952 who detailed the ionic basis of resting and action potentials, but without addressing the question of where these ions passed. The emerging question of the existence of ion channels, widely debated over the next two decades, was finally accepted and, a decade later, many of them began to be cloned. This led to the possibility of modelling the activity of individual neurons in the brain and then that of simple circuits. Taking advantage of the remarkable advances in computer science in the new millennium, together with a much deeper understanding of brain architecture, more ambitious scientific goals were dreamed of to understand the brain and how it works. The retrospective concludes by reviewing the main efforts in this direction, namely the construction of a digital brain, an in silico copy of the brain that would run on supercomputers and behave just like a real brain.

In recent years, the use of solar photovoltaic (PV) energy, which is one of the leading renewable energy sources, has become increasingly widespread around the world due to its numerous advantages. However, PV-based electricity generation necessitates a large amount of land. Agrivoltaic (AV) systems, an innovative approach to combining agricultural and electricity production in the same area through solar modules positioned several meters above the surface of the ground, are growing rapidly in renewable energy and farming communities. This study explores Turkey\'s solar power generation and agricultural activities, combining crop cultivation and electricity generation for sustainable development on the same land. Furthermore, the AV potential for the most agriculture ten cities in different climate zones in Turkey is investigated using the PVsyst program. A list of the most commonly grown crops in the ten selected cities and the types of AV systems that can be employed with these crops is provided. The results show that AV systems present a great opportunity for the optimal integration of solar power generation with food production, especially for the cities of Konya, Kayseri, and Manisa, with the most ideal conditions for agricultural and solar power production. By combining the solar power potential of the country with the production capacity of arable lands, the increasing energy needs can be met and more efficient agricultural production can be provided. This study is expected to demonstrate that in specific regions of Turkey, AV farming will be suitable for certain crops.

The demand for renewable energy-based Electric Vehicle (EV) charging infrastructure is increasing in recent years. Solar PV based EV charging method is preferred as it has simple energy harvesting technique. The PV system is an uncertain power source, where the power generation is varied with respect to the availability of sunlight. So, that the charging station requires a backup power supply for the uninterrupted charging. For the integrated power sources, the charging station requires a simple and efficient conversion unit for the DC/AC/DC conversion. In this work, a modified Z-source inverter (MZSI) is developed for the multiport EV charger using PV and grid. The proposed MZSI is connected between the input and output sides to boost the voltage as per the demand at the battery side. In order to connect many battery units with the charger, the capacitors used in the MZSI are split as per the required number of charging ports. This developed converter topology operates the systems in four different modes like PV-Grid, PV-battery, grid-battery, and battery-grid. The performance of this proposed work has been validated in MATLAB/Simulink® and in the experimental setup. The experimental setup has been developed with two charging ports for obtaining 250W at each charger end which cumulatively produces 500W output across both chargers with an efficiency of 90.18%.

In this study, highly selenite-resistant strains belonging to Brevundimonas diminuta (OK287021, OK287022) genus were isolated from previously operated single chamber microbial fuel cell (SCMFC). The central composite design showed that the B. diminuta consortium could reduce selenite. Under optimum conditions, 15.38 Log CFU mL-1 microbial growth, 99.08% Se(IV) reduction, and 89.94% chemical oxygen demand (COD) removal were observed. Moreover, the UV-visible spectroscopy (UV) and Fourier transform infrared spectroscopy (FTIR) analyses confirmed the synthesis of elemental selenium nanoparticles (SeNPs). In addition, transmission electron microscopy (TEM) and scanning electron microscope (SEM) revealed the formation of SeNPs nano-spheres. Besides, the bioelectrochemical performance of B. diminuta in the SCMFC illustrated that the maximum power density was higher in the case of selenite SCMFCs than those of the sterile control SCMFCs. Additionally, the bioelectrochemical impedance spectroscopy and cyclic voltammetry characterization illustrated the production of definite extracellular redox mediators that might be involved in the electron transfer progression during the reduction of selenite. In conclusion, B. diminuta whose electrochemical activity has never previously been reported could be a suitable and robust biocatalyst for selenite bioreduction along with wastewater treatment, bioelectricity generation, and economical synthesis of SeNPs in MFCs.

In the context of the evolving landscape of reduction in carbon emissions and integration of renewable energy, this study uses system dynamics (SD) modeling to explore the interconnected dynamics of carbon trading (CT), tradable green certificate (TGC) trading, and electricity markets. Using differential equations with time delays, the study provides a comprehensive analysis of structural relationships and feedback mechanisms within and between these markets. Key findings reveal the intricate interplay between carbon prices, green certificate prices, and electricity prices under various coupling mechanisms. For example, under the three-market coupling mechanism, carbon trading prices stabilize around 150 Yuan/ton, while green certificate prices reach a peak of 0.45 Yuan/KWH, impacting electricity prices, which fluctuate between 0.33 and 1.09 Yuan / KWH during the simulation period. These quantitative results shed light on nuanced fluctuations in market prices and the dynamics of anticipated purchases and sales volumes within each market. The insights gleaned from this study offer valuable implications for policy makers and market stakeholders in navigating the complexities of carbon emission reduction strategies, the integration of renewable energy and market equilibrium. By understanding the dynamics of multi-market coupling, stakeholders can better formulate policies and strategies to achieve sustainable energy transitions and mitigate impacts of climate change.

Cloud data centers present a challenge to environmental sustainability because of their significant energy consumption. Additionally, performance degradation resulting from energy management solutions, such as virtual machine (VM) consolidation, impacts service level agreements (SLAs) between cloud service providers and users. Thus, to achieve a balance between efficient energy consumption and avoiding SLA violations, we propose a novel VM consolidation algorithm. Conventional algorithms result in unnecessary migrations when improperly selecting VMs. Therefore, our proposed E2SVM algorithm addresses this issue by selecting VMs with high load fluctuations and minimal resource usage from overloaded servers. These selected VMs are then placed on normally loaded servers, considering their stability index. Moreover, our approach prevents server underutilization by either applying all or no VM migrations. Simulation results demonstrate a 12.9% decrease in maximum energy consumption compared with the minimum migration time VM selection policy. In addition, a 47% reduction in SLA violations was observed when using the medium absolute deviation as the overload detection policy. Therefore, this approach holds promise for real-world data centers because it minimizes energy waste and maintains low SLA violations.