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The continuous increase in the penetration rate of renewable energy has posed severe challenges to the flexibility of power systems, especially in coastal and island areas where local power supply is insufficient while electricity demand keeps growing. Focusing on the regional water–energy nexus system (WENS), this paper fully taps into the flexibility potential of seawater desalination plants (SWDPs) as adjustable loads, and proposes a bi-level optimal dispatch model. First, the operational characteristics of reverse osmosis (RO) seawater desalination loads are analyzed, and an operational model encompassing water intake equipment, high-pressure pumps, clear water tanks and product water tanks is established. Second, a dispatch framework for the regional WENS incorporating SWDP is designed, on the basis of which a bi-level optimal dispatch model is constructed: the upper-level model takes maximizing wind power accommodation and minimizing wind power output fluctuation as the objectives, so as to determine the wind power output and the charging/discharging strategy of supercapacitors; constrained by the decisions made by the upper-level model, the lower-level model comprehensively takes into account the operation cost of thermal power units (TPUs), the wind curtailment penalty cost of the system, the operation cost of energy storage systems and the operation cost of SWDP, and thus establishes an optimization model with the goal of minimizing the comprehensive operation cost of the system. Finally, a comparative analysis is carried out under different scenarios. The results show that compared with the optimal scheduling scheme in which the seawater desalination load does not participate in regulation, the proposed method can reduce the wind curtailment rate by 43.71%, the energy consumption cost of the seawater desalination load by 50.98%, and the total system operation cost by 22.51%, thus providing a feasible approach for the collaborative optimization of water–energy systems in coastal areas. Full article

The adoption of electric vehicles (EVs) is a promising strategy for reducing emissions and promoting sustainable mobility. The increasing adoption of EVs has created a demand for efficient and sustainable charging infrastructure. The integration of rooftop solar-powered EV charging stations into distribution networks is a promising solution for reducing carbon emissions and improving grid efficiency. This integration also introduces challenges, such as power quality issues, grid instability, and the impact of environmental factors on solar generation. This study proposes a novel system that integrates a smart control algorithm for a central battery management system (CBMS) with advanced bidirectional DC-DC converters for optimised power distribution. Unlike existing systems that focus on individual components, this study combines real-time environmental monitoring with adaptive power management algorithms to handle variations in generation owing to solar irradiance, temperature, and shading, and ensure maximum power harvesting. This study also presents the role of the DC-to-DC converter integrated with a smart charging control and CBMS in smart grid-enabled EV charging station. The proposed system was validated using MATLAB 2025b Simulink simulations. This study demonstrates an improvement in overall grid stability and highlights the potential of DC-DC converter technologies for smart grid applications and decarbonisation efforts. Full article

The rapid proliferation of electric vehicles (EVs) has introduced significant challenges to the efficient operation of hydrogen-containing integrated energy systems (H-IESs). To cope with these challenges, this paper develops a bi-level optimal scheduling strategy for H-IESs that simultaneously incorporates a ladder-type carbon emission trading mechanism, demand response, and the operational characteristics of EVs. A demand response model is formulated by considering the coupling characteristics of electric and thermal loads. Price-based incentive signals are further designed to coordinate the interactions between the H-IES operator and EV users, enabling flexible resources to actively participate in system scheduling. In the proposed bi-level framework, the upper-level problem aims to minimize the total operating cost of the H-IES, while the lower-level problem seeks to reduce the charging cost of EV users. The resulting bi-level optimization problem is reformulated and solved using the Karush–Kuhn–Tucker (KKT) conditions. Case study results demonstrate that, compared with the single-level benchmark, the proposed bi-level strategy reduces the total operating cost by 34.79% and lowers the EV charging cost by 4.50%. Full article

Hydrogen embrittlement poses a recognized risk to the structural integrity of carbon steels used in maritime and hydrogen-related infrastructure. This study presents an experimental, numerical, and microstructural assessment of hydrogen embrittlement in ASTM A36 steel under four-point bending loading. Specimens with and without pre-existing notches were subjected to controlled cathodic hydrogen charging for exposure times up to 36 h to evaluate the combined effects of hydrogen diffusion and stress concentration. Experimental force–vertical displacement responses showed a progressive degradation of mechanical performance with increasing hydrogen exposure, characterized by reductions in yield force, ultimate force, and flexural stiffness, with more evident effects in notched specimens. Quantitative analysis indicated reductions of up to approximately 15% in yield force and 4% in flexural rigidity. Finite element models were developed to reproduce the experimental force–displacement behavior, showing good agreement and supporting the adopted numerical approach. Microstructural analysis by scanning electron microscopy revealed hydrogen-assisted damage mechanisms, including intergranular and transgranular microcracking, interfacial decohesion, hydrogen trapping at inclusions, and localized surface blistering near notch roots. The combined results indicate that hydrogen exposure leads to measurable reductions in stiffness and load-bearing capacity, particularly in the presence of geometric discontinuities. Full article

As the number of electric vehicles (EV) increases, the demand for recommending the best charging location when using a large-scale charge network to charge is also increasing. A successful recommendation will utilize the user’s preference and the operational constraints of the charging network to make sure that it also takes into account the real-time operational requirements of the network. Most current papers focus on optimizing individual algorithmic components in isolation; consequently, many of these papers neglect to provide a holistic view of an integrated system. In addition, there are many operational requirements that current research does not consider, such as cold-start personalization for new users and enforcing real-time operational constraints like station availability, power capacity, maintenance windows, etc. This paper describes a deployable multi-stage recommendation system that creates a candidate list based on location and ranks preferences based on user, station and context features. The recommendation system also adds a configurable rule-based re-ranking layer to ensure that both hard constraints (i.e., charger availability and power-cap limits) and soft objectives (i.e., load balancing and operator priority) are enforced. A method for enabling mixed use between stable Bayesian and adaptive Bayesian methods was developed to provide users starting with cold-start performance that do not have adequate histories. Evaluation of this method using 100k+ real charging sessions showed that the fraction of sessions where the ground-truth station appears in the top-two recommendations (Hit@2) for the recommendation system was 0.82, representing a 37% increase in performance compared to proximity-based recommendation methods. The online deployed recommendation system has a 99th-percentile serving latency (P99) of less than 200 ms. The findings of this paper provide a framework for the implementation of operationally-relevant user-centric recommendation systems for EV services at scale. Full article

The manipulation of the electrocatalyst nanoarchitecture, particularly transition metal compounds, regarding size, shape, facets, and composition, significantly enhances the electrocatalytic activity in energy transformations. This study introduces a novel methodology for the precipitation of mesoporous nanoparticles of nickel tungstate (meso-NiWO₄) using direct chemical deposition from a template of Brij^®78 surfactant liquid crystal. Physicochemical analyses revealed the formation of amorphous meso-NiWO₄ nanoparticles with dual sizes of 10 ± 3 and 120 ± 8 nm and a specific surface area of 34.2 m²/g, exceeding that of nickel tungstate deposited in the absence of surfactant (bare-NiWO₄, 4.0 m²/g). The meso-NiWO₄ nanoparticles exhibit improved electrocatalytic stability, reduced charge-transfer resistance (R_ct = 1.11 ohm), and a current mass activity of ~365 mA/cm² mg at 1.6 V vs. RHE during the electrolysis of urea in alkaline solution. Furthermore, by employing meso-NiWO₄ in a two-electrode urea electrolyzer, a remarkable 4.8-fold increase in the cathodic hydrogen concurrent production rate was achieved (373.40 µmol/h at a bias potential of 2.0 V), compared to that of the bare-NiWO₄ catalyst. The exceptional urea oxidation electroactivity and the enhanced hydrogen evolution rate arise from substantial specific surface area and mesoporous structure, facilitating effective charge transfer and mass transport through the meso-NiWO₄ catalyst. Using the surfactant liquid crystal template for electrocatalyst synthesis enables a one-pot deposition of diverse nanoarchitectures and compositions with high surface area at ambient conditions for an improved electrocatalytic and hydrogen green production process. Full article

To reduce the carbon footprint, electric vehicles (EVs) are considered an alternative transportation choice. However, increased use of EVs could lead to overloading the existing power network when accounting for all installed chargers. With the increasing deployment of EV chargers, universities are potential locations for the oversized power network issue. This paper applies reinforcement learning (RL) to optimize for EV charging infrastructure at the university scale using real-world data, directly contributing to sustainable energy management by reducing grid burden and increasing renewable energy utilization. The RL-based charger aims to reduce the burden on the grid while increasing renewable energy utilization. This study investigated practical relevance in real-world systems, considering three demand scenarios: random, stochastic historical demand from Qatar University, and actual online data from Caltech University. Three RL algorithms—Deep Q-Network (DQN), Advantage Actor–Critic (A2C), and Proximal Policy Optimization (PPO)—are applied. While training, the historical stochastic data requires more tuning of the RL framework than the random demand, emphasizing the importance of realistic demand profiles. The performance of the RL approach depends on the type of demand. The results show that the proposed RL approach can efficiently mitigate the peak charging currents. For the Qatar University historical demand scenario, the PPO algorithm minimized the peak charging currents by 50% relative to uncontrolled charging (160 A to 80 A) and Model Predictive Control maintained the energy transfer capability at 99.710%. For the random demand type, the peak charging currents are minimized by 38.3% as compared to uncontrolled charging (128 A to 79 A), with a nominal reduction in energy transfer capability to 95.89%. Scalability is tested by integrating the model into the IEEE-33 bus network. Without solar integration, the proposed RL-based EV charging management model improves the voltage drop by 0.05 p.u., leading to reduction in the line losses by 17% as compared to the MPC benchmark method and by 32% as compared to the uncontrolled charging scheme. Further, the proposed RL approach leads to a 9% reduction in line current during peak hours in the IEEE-33 bus system. With solar integration into the IEEE-bus system, the proposed framework of the RL approach improved the sustainability of the charging infrastructures by enhancing solar energy utilization by 42.5%. These findings validate the applicability of the proposed model used for optimizing the sustainable EV charging infrastructure while managing the charging coordination problem. Full article

SrTiO₃ has attracted considerable attention owing to its favorable electronic structure and chemical stability among various semiconductor photocatalysts. However, its practical application is hindered by a wide bandgap and rapid recombination of photogenerated charge carriers. Herein, we report the fabrication of a SrTiO₃/carbon quantum dot (CQD) heterojunction via a two-step hydrothermal method for efficient CO₂-to-CH₄ photocatalysis, a strategy that circumvents the need for high-temperature treatment and noble metals. TEM images revealed well-defined lattice fringes and intimate interfacial contact between SrTiO₃ and CQDs, suggesting efficient charge transfer pathways. Optical measurements confirmed that CQD modification extends the visible-light absorption range of SrTiO₃ to 420 nm while significantly enhancing charge separation efficiency. The SrTiO₃/CQDs composite with 10 wt% CQD loading exhibited optimal activity, achieving a CH₄ evolution rate of 1.16 μmol·g⁻¹·h⁻¹—16.3 times higher than that of pristine SrTiO₃. Mechanistic investigations demonstrate that CQDs serve as efficient electron reservoirs, facilitating interfacial charge transfer and suppressing the recombination of photogenerated charge carriers. The catalyst maintained stable performance over four consecutive cycles, confirming its structural robustness and reusability. This work demonstrates that CQD modification effectively enhances the visible-light response and charge separation efficiency of SrTiO₃, offering a viable strategy for designing high-performance photocatalysts toward solar fuel production. Full article

Given the growing concern over the operational safety and long-term reliability of lithium-ion batteries, the accurate assessment of battery state of health (SOH) is of paramount importance. With the aim of elevating the SOH estimation exactitude and remedying the model degradation induced by data paucity, this paper proposes an SOH estimation method that integrates a data-augmentation strategy with a Long Short-Term Memory (LSTM)-iTransformer model. Specifically, multiple health characteristic factors characterizing the aging behavior are first extracted from the battery charge–discharge curves and incremental capacity (IC) curves, and the features that are highly correlated with the SOH are screened by a Pearson correlation coefficient analysis. Subsequently, the data augmentation technique is used to extend the degradation sample set. The LSTM-iTransformer model is trained based on the extended samples and evaluated on multiple performance metrics. A comparative analysis reveals a marked enhancement in predictive accuracy achieved by this method over the baseline model trained with the initial data, which validates the effectiveness of the data augmentation strategy in improving the performance of SOH estimation models. Additionally, in scenarios characterized by abundant data availability, the direct application of this model facilitates enhanced predictive precision. Full article

In this work, we investigate the shadow properties of the Kerr–Newman–Anti-de Sitter black hole coupled to nonlinear electrodynamics. The shadow is constructed by employing the celestial coordinate approach for an observer located at a finite distance, which is required due to the non-asymptotically flat structure of the spacetime. The size, distortion, area, and oblateness of the shadow are analyzed in terms of the black hole parameters, namely, the spin, the effective charge, and the nonlinearity parameter. We show that the nonlinear electrodynamics significantly modifies the photon region and therefore changes the shadow observables, while the rotation mainly controls the deformation of the silhouette. We further confront the theoretical results with the Event Horizon Telescope observations of M87* and Sgr A* in order to constrain the parameter space of the model. The allowed ranges of the effective charge depend sensitively on the nonlinearity parameter, and the combination of both sources leads to tighter and physically more consistent bounds. In addition, we study the energy emission rate derived from the shadow radius and the Hawking temperature and discuss how it is affected by the rotation and the nonlinear electromagnetic field. Our analysis shows that the considered black hole solution provides a consistent extension of the Kerr geometry in a non-asymptotically flat background and that the shadow observables can be used as an efficient tool to test the effects of nonlinear electrodynamics in strong gravity. Full article

Vehicle recharge time is a key barrier to widespread adoption of battery electric trucks, where megawatt class charging could be used to achieve refueling times comparable to internal combustion vehicles. This work presents the design and validation of a megawatt-capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current sensor mismatch and to verify protection logic for multiple bus voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs-class charging -capable rechargeable energy storage system (144 kWh, 40P384S) together with a physics-based modeling framework for safe 1 MW operation. The pack architecture is reconfigurable, enabling nominal 750 V (80P192S) propulsion mode as well as 1125 V and 1500 V charging modes compatible with the Megawatt Charging System (MCS). An equivalent-circuit model is developed to relate cell-level parameters to pack-level power, heat generation, and temperature rise, providing guidance on feasible charge profiles and thermal limits. A Simulink-based digital twin of the reconfigurable pack is then used to analyze sensitivity to current–sensor mismatch and to verify protection logic for multiple bus-voltage configurations. Finally, pack tests up to 1 MW confirm the model-predicted operating envelope and illustrate practical constraints imposed by charger voltage and pack resistance. The combined hardware and modeling approach provides a reusable platform for studying extreme fast charging of medium- and heavy-duty BEV packs. Full article

Osmium is an element of the Periodic Table with an atomic number Z equal to 76. In Tokamaks with divertors made of tungsten ($Z=74$), it is produced in the neutron-induced transmutation of the latter. Therefore one can expect that their sputtering may generate ionic impurities of all possible charge states in the fusion plasma. As a consequence, these could contribute to radiation losses in these controlled nuclear devices. The knowledge of radiative rates in all the spectra of osmium is thus important in this field. In this framework, a multiplatform approach has been used to determine the Os V radiative properties and estimate their accuracy. The transition probabilities have been computed for the 2677 electric dipole (E1) transitions falling in the spectral range from 400 Å to 12,000 Å. Three independent atomic structure models have been considered; one based on the fully relativistic ab initio multiconfiguration Dirac–Hartree–Fock (MCDHF) method and two based on the semi-empirical pseudo-relativistic Hartree–Fock (HFR) method. Full article

This study investigates the potential of Quercus robur leaves as a bio-coagulant for the removal of heavy metal ions, including zinc (II), iron (III), copper (II), and chromium (VI), from water. The Quercus robur leaves were used in two forms: Quercus robur powder (QRP) and Quercus robur extract (QRE). The extract was prepared using distilled water to extract the active compounds responsible for coagulation, such as proteins, polysaccharides, and total phenolics. The QRP was characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), X-ray diffraction (XRD), and zeta potential analysis to identify the active functional groups, surface morphology, crystallinity, and surface charge, all of which are key factors influencing its performance in the coagulation–flocculation process. In this work, the Response Surface Methodology (RSM)-based Central Composite Design (CCD), with two factors (bio-coagulant dosage and initial metal concentration), was used examine the effects of each factor and their interaction, while the responses were zinc (II) removal, iron (III) removal, copper (II) removal, and chromium (VI). The results revealed high removal efficiency for these metal ions, reaching up to 100% for all metal ions treated with QRP and QRE. The quality of the model predictions was evaluated using analysis of variance (ANOVA). For all metal ions, the R² (≥97%), R² adjusted (≥95%), and p-values (<0.05), indicating an excellent model accuracy. These results show that bio-coagulants (QRP and QRE) based a Quercus robur leaves are a promising, effective, and reliable option for removing heavy metal ions from water, and that the models developed can be used to optimize the coagulation-flocculation process. Full article

This study explores a novel class of polynuclear superhalogen anions featuring heterovalent central atoms from groups 13 (B, Al) and 15 (P, As). The investigated species follow a modified general formula, (X_nY_n’F_{{(3n+5n’)+1}})⁻ where X = B and/or Al, Y = P and/or As, and n + n′ = 2–4. Low-energy isomers were identified using the Coalescence Kick method and subsequently optimized at the MP2/aug-cc-pVDZ level of theory. Electronic stability was assessed via the outer valence Green’s function (OVGF) approach with the same aug-cc-pVDZ basis set. All examined anions exhibit exceptional electronic stability, with vertical electron detachment energies (VDEs) ranging from 10.70 to 12.37 eV, significantly exceeding the superhalogen threshold of 3.65 eV. Thermodynamic analyses indicate that aluminum atoms play a crucial role in stabilizing larger clusters by acting as a structural “glue”, thereby suppressing fragmentation through the loss of neutral XF₃ or YF₅ units. In contrast, larger non-metallic analogs show an increased propensity toward dissociation. The potential of the heterovalent polynuclear superhalogen anions as weakly coordinating anions (WCAs) was further evaluated through molecular electrostatic potential (ESP) analysis. The results demonstrate that combining different central atoms within boron-based frameworks leads to a more homogeneous charge distribution, enhancing weakly coordinating behavior. Full article

This paper investigates the deployment of four electric vehicle (EV) fast-charging stations (FCSs) in a commercial facility’s parking area, where multiple service centers operate on varying schedules. The commercial load demand is modeled using Monte Carlo Simulation (MCS), introducing realistic stochastic variability and overlapping power patterns with FCS operations. A single-point sensing strategy at the point of common coupling (PCC) is adopted for load disaggregation. Continuous Wavelet Transform (CWT) is employed for feature extraction, and multiclass classification is performed using Error-Correcting Output Codes (ECOC). Under commercial load interference, conventional machine-learning classifiers achieve a macro classification accuracy of 89.53%, with the lowest class accuracy dropping to 76.74%. To address this limitation, a deep learning (DL)-based framework is implemented. Simulation results demonstrate that the proposed DL approach improves overall classification accuracy from 89.53% to 100%, corresponding to a 10.47 percentage-point absolute improvement, an 11.7% relative gain, and complete elimination of misclassification errors. Notably, the most affected charging station class (FCS₂) accuracy increases from 76.74% to 100%. These results demonstrate that the proposed deep learning framework reliably detects FCS activations even under overlapping, variable, and high-power commercial load conditions, enabling more efficient energy management and optimal utilization of electrical resources, reduced energy waste, and enhanced sustainability of EV charging infrastructure within commercial facilities. Full article