Search Results (373)

The dry conversion of CO₂ into CO and O₂ provides an attractive path for CO₂ utilization which allows for the use of the CO produced for the synthesis of valuable hydrocarbons. In the following work, the CO₂ conversion is driven by an arc discharge at atmospheric pressure, producing hot plasma. This study presents a series of experiments aiming to optimize the process. The results obtained include the energy efficiency and the conversion rate of the process, as well as the electrical parameters of the discharge (current and voltage signals). In addition, optical emission spectroscopy diagnostics based on an analysis of C₂’s Swan bands are used to determine the gas temperature in the discharge. The data is analyzed according to several aspects—an analysis of the arc’s motion based on the electrical signals; an analysis of the effect of the gas flow and the discharge current on the discharge performance for CO₂ conversion; and an analysis of the vibrational and rotational temperatures of the arc channel. The results show significant improvements over previous studies. Relatively high gas conversion and energy efficiency are achieved due to the arc acceleration caused by the Lorentz force. The rotational (gas) temperatures are in the order of 5500–6000 K. Full article

Lithium-polymer (LiPo) batteries are valued for their high energy density, stable voltage output, low self-discharge, and strong reliability, making them a popular choice for high-performance and portable applications. Despite these advantages, the charging behavior of LiPo batteries—especially during rapid charging—remains an area with limited understanding. This research examines the electro-thermal characteristics of VARTA LiPo batteries when subjected to high charging currents (2C, 3C, and 4C rates). A temperature-sensitive charging model is developed to address safety and efficiency concerns during fast charging. Experimental data indicate that charging at 45 °C yields the best performance, achieving 80% state of charge (SoC) within 25 min. However, charging at temperatures above or below this level (such as 25 °C) reduces efficiency due to increased internal resistance and accelerated battery aging. The model, validated across a range of temperatures (25 °C, 35 °C, 45 °C, and 60 °C), shows that longer constant-current (CC) charging phases at higher temperatures are associated with lower internal resistance. These results highlight the importance of effective thermal management for optimizing both safety and performance in LiPo battery applications. Full article

The integration of lithium-ion (Li-ion) battery energy storage systems (LiBESSs) with photovoltaic (PV) generation offers a promising solution for powering auxiliary services (ASs) in high-voltage power stations. This study conducts a systematic literature review (SLR) to evaluate the feasibility, benefits, and challenges of this integration. The proposed SLR complies with the PRISMA 2020 statement, and it is also registered on the international PROSPERO platform (ID 1073599). The selected methodology includes the following key steps: definition of the research questions; search strategy development; selection criteria of the studies; quality assessment; data extraction and synthesis; and discussion of the results. Through a comprehensive analysis of scientific publications from 2013 to 2024, trends, advancements, and research gaps are identified. The methodology follows a structured review framework, including data collection, selection criteria, and evaluation of technical feasibility. From 803 identified studies, 107 were eligible in accordance with the assessed inclusion criteria. Then, a custom study impact factor (SIF) framework selected 5 out of 107 studies as the most representative and assertive ones on the topics of this SLR. The findings indicate that Li-ion BESSs combined with PV systems enhance reliability, reduce reliance on conventional sources, and improve grid resilience, particularly in remote or constrained environments. The group of reviewed studies discuss optimization models and multi-objective strategies for system sizing and operation, along with practical case studies validating their effectiveness. Despite these advantages, challenges related to cost, regulatory frameworks, and performance variability remain. The study concludes that further experimental validations, pilot-scale implementations, and assessment of long-term economic impacts are necessary to accelerate the adoption of BESS-PV systems in high-voltage power substations. This study was funded by the R&D program of the Brazilian National Electric Energy Agency (ANEEL) via project number PD-07351-0001/2022. Full article

State estimation in distribution power systems is increasingly challenged by the proliferation of distributed energy resources (DERs), bidirectional power flows, and the growing complexity of unbalanced network topologies. Physics-Informed Neural Networks (PINNs) offer a compelling solution by integrating machine learning with the physical laws that govern power system behavior. This paper introduces a PINN-based framework for state estimation in unbalanced distribution systems, leveraging available data and embedded physical knowledge to improve accuracy, computational efficiency, and robustness across diverse operating scenarios. The proposed method is evaluated on four IEEE test feeders—IEEE 13, 34, 37, and 123—using synthetic datasets generated via OpenDSS to emulate realistic operating scenarios, and demonstrates significant improvements over baseline models. Notably, the PINN achieves up to a 97% reduction in current estimation errors while maintaining high voltage prediction accuracy. Extensive simulations further assess model performance under noisy inputs and partial observability, where the PINN consistently outperforms conventional data-driven approaches. These results highlight the method’s ability to generalize under uncertainty, accelerate convergence, and preserve physical consistency in simulated real-world conditions without requiring large volumes of labeled training data. Full article

This study introduces a Multiscale Dual-Attention U-Net (TS-MSDA U-Net) model for long-term time series synthesis. By integrating multiscale temporal feature extraction and dual-attention mechanisms into the U-Net backbone, the model captures complex temporal dependencies more effectively. The model was evaluated in two distinct applications. In the first, using multivariate datasets from 70 real-world electric vehicle (EV) trips, TS-MSDA U-Net achieved a mean absolute error below 1% across key parameters, including battery state of charge, voltage, acceleration, and torque—representing a two-fold improvement over the baseline TS-p2pGAN. While dual-attention modules provided only modest gains over the basic U-Net, the multiscale design enhanced overall performance. In the second application, the model was used to reconstruct high-resolution signals from low-speed analog-to-digital converter data in a prototype resonant CLLC half-bridge converter. TS-MSDA U-Net successfully learned nonlinear mappings and improved signal resolution by a factor of 36, outperforming the basic U-Net, which failed to recover essential waveform details. These results underscore the effectiveness of transformer-inspired U-Net architectures for high-fidelity multivariate time series modeling in both EV analytics and power electronics. Full article

Proton exchange membrane water electrolyzers (PEMWEs) are a promising technology for green hydrogen production. However, the adoption of PEMWE-based hydrogen production systems remains limited due to several challenges, including high material costs, limited performance and durability, and difficulties in scaling the technology. Computational modeling serves as a powerful tool to address these challenges by optimizing system design, improving material performance, and reducing overall costs, thereby accelerating the commercial rollout of PEMWE technology. Despite this, conventional models often oversimplify key components, such as porous transport and catalyst layers, by assuming constant porosity and neglecting the spatial heterogeneity found in real electrodes. This simplification can significantly impact the accuracy of performance predictions and the overall efficiency of electrolyzers. This study develops a mathematical framework for modeling variable porosity distributions—including constant, linearly graded, and stepwise profiles—and derives analytical expressions for permeability, effective diffusivity, and electrical conductivity. These functions are integrated into a three-dimensional multi-domain COMSOL simulation to assess their impact on electrochemical performance and transport behavior. The results reveal that although porosity variations have minimal effect on polarization at low voltages, they significantly influence internal pressure, species distribution, and gas evacuation at higher loads. A notable finding is that reversing stepwise porosity—placing high porosity near the membrane rather than the channel—can alleviate oxygen accumulation and improve current density. A multi-factor comparison highlights this reversed configuration as the most favorable among the tested strategies. The proposed modeling approach effectively connects porous media theory and system-level electrochemical analysis, offering a flexible platform for the future design of porous electrodes in PEMWE and other energy conversion systems. Full article

In direct-current gas-insulated transmission lines (DC GIL), complex heat transfer processes accelerate surface charge accumulation on insulators, causing local electric field distortion and elevating the risk of surface flashover. This study develops a three-dimensional multi-physics coupled mathematical model for ±200 kV DC GIL basin-type insulators. The bulk and surface conductivity of insulator materials were experimentally measured under varying temperature and electric field conditions, with fitting equations derived to describe their behavior. The model investigates surface charge accumulation and electric field distribution under DC voltage and polarity-reversal conditions, incorporating multi-field coupling effects. Results show that, at a 3150 A current in a horizontally arranged DC GIL, insulator temperatures reach approximately 62.8 °C near the conductor and 32 °C near the enclosure, with the convex surface exhibiting higher temperatures than the concave surface and distinct radial variations. Under DC voltage, surface charge accumulates faster in high-temperature regions, with both charge and electric field distributions stabilizing after approximately 300 h, following significant changes within the first 40 h. Following stabilization, the distribution of surface charge and electric field varies across different radial directions. During polarity reversal, residual surface charges cause electric field distortion, increasing maximum field strength by 13.6% and 47.2% on the convex and concave surfaces, respectively, with greater distortion on the concave surface, as calculated from finite element simulations with a numerical accuracy of ±0.5% based on mesh convergence and solver tolerance. These findings offer valuable insights for enhancing DC GIL insulation performance. Full article

Recent advancements in photovoltaic (PV) and battery technologies, combined with improvements in power electronic converters, have accelerated the adoption of rooftop PV systems and electric vehicles (EVs) in distribution networks, while these technologies offer economic and environmental benefits and support the transition to sustainable energy systems, they also introduce operational challenges, including voltage fluctuations, increased system losses, and voltage regulation issues under high penetration levels. Traditional Voltage and Var Control (VVC) strategies, which rely on substation on-load tap changers, voltage regulators, and shunt capacitors, are insufficient to fully manage these challenges. This study proposes a novel Voltage, Var, and Watt Control (VVWC) framework that coordinates the operation of PV and EV resources, conventional devices, and demand responsive loads. A mixed-integer nonlinear multi-objective optimization model is developed, applying a Chebyshev goal programming approach to balance objectives that include minimizing PV curtailment, reducing system losses, flattening voltage profile, and minimizing demand not met. Unserved demand has, in particular, been modeled while incorporating the concepts of distributional and recognition energy justice. The proposed method is validated using a modified version of the IEEE 123-bus test distribution system. The results indicate that the proposed framework allows for high levels of PV and EV integration in the grid, while ensuring that EV demand is met and PV curtailment is negligible. This demonstrates an equitable access to energy, while maximizing renewable energy usage. Full article

This study investigates the effects of electric field pretreatment parameters such as electric field strength (0.1–0.2 kV/cm), waveform (sinusoidal vs. square), and application mode (continuous vs. pulsed) on the quality attributes of dried Fuji apple slices, including ascorbic acid (vitamin C) retention, β-carotene content, and hydroxymethylfurfural (HMF) formation. Electric-field-treated samples were compared to untreated controls after convective drying at 75 °C. Results revealed that vitamin C was significantly influenced by waveform, with sinusoidal waves preserving about 27% more vitamin C than square waves, likely due to reduced oxidative degradation from gentler electroporation. Conversely, square waves caused the highest β-carotene losses (25% vs. control), attributed to prolonged peak voltage destabilizing carotenoids. HMF formation was reduced by 10–23% in electric-field-treated samples compared to controls, linked to accelerated drying rates limiting Maillard reaction time. Low electric field strengths (0.1–0.15 kV/cm) enhanced antioxidant activity; however, higher intensities showed a potential decline. The square waveform had a more detrimental effect on phenolic compounds than the sinusoidal waveform. These findings suggest that low electric field pretreatment, particularly with sinusoidal waveforms at 0.2 kV/cm, enhances drying efficiency while balancing nutrient retention and HMF mitigation, offering a promising strategy for producing high-quality dried fruits. Full article

It is of the utmost importance to accurately solve the transformer temperature field, as it governs the overall performance and operational stability of the transformer. However, the intricate structure of high- and low-voltage windings, insulating materials, and other components presents numerous challenges for modeling. Temperature exerts a significant influence on insulation aging, and elevated temperatures can notably accelerate the degradation process of insulation materials, reducing their service life and increasing the risk of electrical failures. In view of this, this paper proposes an equivalent modeling method of the temperature field of the transformer HLV winding and studies the refined modeling of the winding part. First of all, in order to reduce the difficulty of temperature field modeling, based on the principle of constant thermal resistance, the fine high- and low-voltage windings are equivalent to large conductors, and the equivalent thermal conductivity coefficient of the high- and low-voltage windings is obtained, which improves the calculation accuracy and shortens the calculation time. Secondly, we verify the feasibility of the equivalent model before and after the simulation, analyze the influence of different boundary conditions on the winding temperature field distribution, and predict the local hotspot location and temperature trend. Finally, a 50 kVA amorphous alloy winding-core transformer is tested on different prototypes to verify the effectiveness of the proposed method. Full article

The accelerating global demand for sustainable and efficient energy storage has driven substantial interest in supercapacitor technology due to its superior power density, fast charge–discharge capability, and long cycle life. However, the low energy density of supercapacitors remains a key bottleneck, limiting their broader application. This review provides a comprehensive and focused overview of the latest breakthroughs in supercapacitor research, emphasizing strategies to overcome this limitation through advanced material engineering and device design. We explore cutting-edge developments in electrode materials, including carbon-based nanostructures, metal oxides, redox-active polymers, and emerging frameworks such as metal–organic frameworks (MOFs) and covalent organic frameworks (COFs). These materials offer high surface area, tunable porosity, and enhanced conductivity, which collectively improve the electrochemical performance. Additionally, recent advances in electrolyte systems—ranging from aqueous to ionic liquids and organic electrolytes—are critically assessed for their role in expanding the operating voltage window and enhancing device stability. The review also highlights innovations in device architectures, such as hybrid, asymmetric, and flexible supercapacitor configurations, that contribute to the simultaneous improvement of energy and power densities. We identify persistent challenges in scaling up nanomaterial synthesis, maintaining long-term operational stability, and integrating materials into practical energy systems. By synthesizing these state-of-the-art advancements, this review outlines a roadmap for next-generation supercapacitors and presents novel perspectives on the synergistic integration of materials, electrolytes, and device engineering. These insights aim to guide future research toward realizing high-energy, high-efficiency, and scalable supercapacitor systems suitable for applications in electric vehicles, renewable energy storage, and next-generation portable electronics. Full article

The growing demand for reliable micropower sources in extreme environments has accelerated the development of betavoltaic cells (BV cells) with high energy conversion efficiency and superior radiation resistance. This study demonstrates an advanced BV cell architecture utilizing three-dimensional TiO₂ nanorod arrays (TNRAs) integrated with a NiO_x hole transport layer (HTL). Monte Carlo simulations were employed to optimize the cell design and determine the fabrication parameters for growing TNRAs on FTO substrates via hydrothermal synthesis. The performance evaluation employed both electron beam (2.36 × 10⁹ e/cm²·s) and ⁶³Ni (3.4 mCi/cm²) irradiation methods. The simulation results revealed optimal energy deposition characteristics, with ~96% of β-particle energy effectively absorbed within the 2 μm thick FTO/TNRA/NiO_x/Au structure. The NiO_x-incorporated device achieved an energy conversion efficiency of 4.84%, with a short-circuit current of 119.9 nA, an open-circuit voltage of 324.2 mV, and a maximum power output of 24.0 nW, representing a 3.76-fold enhancement over HTL-free devices. Radioactive source testing confirmed stable power generation and linear efficiency scaling, validating electron beam irradiation as an effective accelerated testing methodology for BV cell research. Full article

Anesthesia is a cornerstone of modern medicine, enabling surgery, pain management, and critical care. Despite its widespread use, the precise molecular mechanisms of anesthetic action remain incompletely understood. Recent advancements in structural biology, including cryo-electron microscopy (Cryo-EM), X-ray crystallography, and computational modeling, have provided high-resolution insights into anesthetic–target interactions. This review examines key molecular targets, including GABA_A receptors, NMDA receptors, two-pore-domain potassium (K2P) channels (e.g., TREK-1), and voltage-gated sodium (Nav) channels. General anesthetics modulate GABA_A and NMDA receptors, affecting inhibitory and excitatory neurotransmission, while local anesthetics primarily block Nav channels, preventing action potential propagation. Structural studies have elucidated anesthetic binding sites and gating mechanisms, providing a foundation for drug optimization. Advances in computational drug design and AI-assisted modeling have accelerated the development of safer, more selective anesthetics, paving the way for precision anesthesia. Future research aims to develop receptor-subtype-specific anesthetics, Nav1.7-selective local anesthetics, and investigate the neural mechanisms of anesthesia-induced unconsciousness and postoperative cognitive dysfunction (POCD). By integrating structural biology, AI-driven drug discovery, and neuroscience, anesthesia research is evolving toward safer, more effective, and personalized strategies, enhancing clinical outcomes and patient safety. Full article

Long-term ECMOs are expected to be put into practical use in order to prepare for the next emerging severe infectious diseases after the novel coronavirus pandemic in 2019–2023. While polypropylene (PP) and polymethylpentene (PMP) are currently the mainstream materials for the hollow fiber membranes of ECMO, the PP membrane coated with a silicone layer on the outer surface has also been commercialized. In this study, we sought a method to accurately observe the detailed pore morphologies of the PP membrane by suppressing irreversible changes in the morphology in SEM observation, which is a general-purpose observation with higher resolution. As a result, the convex surface morphologies of the PP membrane, which was a non-conductive porous structure, were confirmed in detail by utilizing the lower secondary electron image (LEI) mode (FE-SEM, JSM-7610F, JEOL Ltd., Tokyo, Japan) at low acceleration voltage, low magnification, and long working distance, to minimize morphological alterations caused by osmium (Os) sputtering. On the other hand, although the sputter-coating on non-conductive samples is mandatory for imaging morphologies with SEM, the non-sputtering method is also worthwhile for porous and fragile structures such as this sample to minimize morphological alterations. Furthermore, we propose a method to confirm the morphology of the deep part of the sample by utilizing the secondary electron image (SEI) mode at an appropriate acceleration voltage and high magnification with higher resolution. Full article

Perovskite solar cells (PSCs) are emerging as leading candidates for sustainable energy generation due to their high power conversion efficiencies and low fabrication costs. However, their performance remains constrained by non-radiative recombination losses primarily at grain boundaries, interfaces, and within the perovskite bulk that are difficult to characterize under realistic operating conditions. Traditional methods such as photoluminescence offer valuable insights but are complex, time-consuming, and often lack scalability. In this study, we present a novel Long Short-Term Memory (LSTM)-based deep learning framework for dynamically predicting dominant recombination losses in PSCs. Trained on light intensity-dependent current–voltage (J–V) characteristics, the proposed model captures temporal behavior in device performance and accurately distinguishes between grain boundary, interfacial, and band-to-band recombination mechanisms. Unlike static ML approaches, our model leverages sequential data to provide deeper diagnostic capability and improved generalization across varying conditions. This enables faster, more accurate identification of efficiency limiting factors, guiding both material selection and device optimization. While silicon technologies have long dominated the photovoltaic landscape, their high-temperature processing and rigidity pose limitations. In contrast, PSCs—especially when combined with intelligent diagnostic tools like our framework—offer enhanced flexibility, tunability, and scalability. By automating recombination analysis and enhancing predictive accuracy, our framework contributes to the accelerated development of high-efficiency PSCs, supporting the global transition to clean, affordable, and sustainable energy solutions. Full article