Search Results (63,767)

Nanotechnology has emerged as a key driver in radioisotope batteries, which offer unique advantages for long-term, maintenance-free energy supply in deep space exploration, medical implants, and nuclear waste utilization. This review summarizes recent progress in applying nanomaterials and nanostructures to overcome the limitations of nuclear batteries, including low energy conversion efficiency and poor stability. The main content focuses on the three primary conversion mechanisms of thermoelectric, radio-voltaic, and radio-photovoltaic batteries, discussing high-performance thermoelectric nanomaterials such as SiGe alloys, wide-bandgap semiconductors including diamond and SiC for enhanced carrier collection, and nanoscale radionuclide ources to mitigate self-absorption losses. This review further elaborates on how nanostructure regulation and interface engineering have significantly improved carrier collection efficiency and device stability. These advances have enabled notable civilian applications, such as the BV100 and “Zhulong No.1” nuclear batteries. Despite this progress, challenges remain in ensuring long-term material stability under extreme environments, maintaining performance consistency during macroscopic device integration, and addressing the high fabrication costs. The review concludes by outlining future research directions, including the development of novel nanomaterial systems, innovative nanostructure designs, scalable manufacturing processes, and enhanced device stability and safety, to further advance next-generation radioisotope batteries. Full article

Building-Integrated Photovoltaics (BIPV) can contribute to decarbonisation, but its large-scale deployment requires accurate energy yield predictions that justify these systems during the decision-making process to ensure cost-effectiveness. In urban contexts, boundary conditions involve modelling strategies that can reliably represent the effect of shading from nearby elements. However, specific tools for proper modelling BIPV are not generally available and the workflow frequently requires the combination of different tools. Nowadays there is still no clear nor unique strategy for modelling BIPV, and expert groups are currently working on benchmarking analyses. This work compares energy yield estimations from two PV simulation software tools, System Advisor Model and PVsyst to seven years of experimental data (2017–2023) from five BIPV façade arrays distributed across three orientations (east, south and west). The main focus was twofold. Firstly, to analyse their management of shadows by following two different shading approaches: their built-in 3D modelling tools and a Digital Surface Model (DSM). Secondly, to evaluate the capability of these tools to simulate the performance of real BIPV systems. Results manifest that conventional and accessible PV software can be suitable for BIPV modelling as long as care is taken to properly assess the effect of shading, especially from urban tree canopies. The novel DSM strategy proposed is proven effective and can be a valid alternative in certain cases when the availability of in situ data is limited. Full article

The effect of A-site substitution on the morphological and electrochemical properties of La_1-xSr_xMnO₃ (x = 0, 0.25, 0.50) perovskites was investigated to evaluate their potential as electrode materials for supercapacitors. X-ray diffraction analysis confirmed the formation of the perovskite structure, with minor peak shifts and distortion of crystal structure induced by Sr substitution. Scanning electron microscopy analysis revealed irregularly shaped particulate morphology across all perovskite compositions. The increasing amount of Sr as in La_0.5Sr_0.5MnO₃ (LSM-50) favored the formation of nanosized particles, and energy dispersive X-ray (EDX) analysis confirmed the presence of all constituent elements; EDX elemental mapping also showed a uniform distribution of all elements in the various perovskite compositions. Among all compositions, La_0.75Sr_0.25MnO₃ (LSM-25) possessed the highest specific capacitance (C_sp) of 483 Fg⁻¹ at 1 Ag⁻¹ current density in 3 M KOH electrolyte, as determined by electrochemical analysis. This perovskite material also exhibited a capacitance retention of 87.8% after 5000 charge–discharge cycles. Electrochemical impedance spectroscopy revealed that LSM-25 showed the lowest solution resistance (0.68 Ω*cm²) and charge transfer resistance (1.52 Ω*cm²), indicating strong electrode–electrolyte interaction. Detailed analysis of cyclic voltammetry data revealed that the predominant charge storage mechanism was diffusive in nature, with 88% of the diffusive contribution registered for LSM-25. These findings demonstrate that Sr substitution at the A-site significantly enhances the energy storage performance of LaMnO₃, making it a promising candidate for supercapacitor applications. Full article

Extreme disasters often induce multiple simultaneous faults in distribution networks, posing significant risks to power supply reliability. Although network reconfiguration and intentional islanding are critical strategies for enhancing system resilience, existing studies typically address them separately and fail to adequately account for the uncertainties associated with renewable energy generation and load demand. To address these limitations, this paper presents a collaborative optimization model for resilient distribution network restoration. A multi-time-step dynamic restoration framework is developed to coordinate network reconfiguration, emergency repair scheduling, distributed generation dispatch, and load shedding. This framework enables unified decision-making for island formation and topology reconfiguration, and incorporates an island integration mechanism to broaden the feasible solution space. To manage source–load uncertainties, chance-constrained programming is introduced, transforming probabilistic security constraints into deterministic equivalents using risk indicator variables, thereby striking a balance between operational security and economic efficiency. In addition, the model optimizes repair sequences under multi-fault conditions to enhance resource utilization. Simulations on a modified IEEE 33-node system validate the effectiveness of the proposed approach in reducing load curtailment, accelerating restoration, and achieving a favorable trade-off between operational risk and economic performance. Full article

With natural gas demand growing rapidly in this century, solidified natural gas technology holds great potential for strengthening energy resilience and delivering secure global gas supply. However, this technology is still impeded by insufficient gas uptake capacity and sluggish hydrate formation rate. Environmentally benign peptides have recently emerged as a novel class of green hydrate promoters. Different from single amino acids, peptides exhibit significant structural diversity owing to their varying sequences and combinations of their constituent amino acid monomers, showing great potential in hydrate-based applications. In this work, a unique tripeptide promoter, L-glutathione reduced (GSH), was employed, and the thermodynamic influence factors in methane hydrate formation were systematically investigated. Furthermore, as a highly hydrophilic amino acid, L-arginine was chosen for a comparative kinetic investigation with extremely hydrophilic GSH. The results revealed that experimental pressure showed a strong effect on the methane uptake rate, while it presented little influence on final methane storage capacity. The initial temperature greatly affected the average induction time, the rate of hydrate growth, and the yields of hydrates promoted by GSH. Increasing temperature resulted in a significant reduction in both the hydrate formation rate and methane uptake at 3 h. Therefore, in the GSH-promoted hydrate formation process, suitable pressure and temperature should be carefully chosen for desirable hydrate performance. Furthermore, the initial 15 min hydrate formation rate of 0.3 wt% L-arginine is 52.4% lower than that of 0.3 wt% GSH. The final methane uptake of 0.3 wt% arginine is substantially smaller than that of 0.3 wt% GSH. Although both GSH and arginine exhibit strong hydrophilic properties, the tripeptide GSH is more effective than the amino acid arginine in enhancing methane hydrate formation. The insights gained from this work offer a theoretical foundation for the application of peptide-based promoters in solidified natural gas technology. Full article

Machine learning interatomic potentials (MLIPs) are typically developed for globally ordered homogeneous systems (GOHomS), which exhibit only minor local deviations from equilibrium configurations. Consequently, most existing MLIPs trained on GOHomS often perform inadequately when applied to locally ordered heterogeneous systems (LOHetS), e.g., substitutional alloying elements in multicomponent alloys. To describe doping alloy systems, we develop a fine-tuned MLIP based on the MACE foundation model, specifically tailored for Mo-based dilute alloys containing one or two out of 20 substitutional elements: Cr, Fe, Mn, Nb, Re, Ta, Ti, V, W, Y, Zr, Al, Zn, Cu, Ag, Au, Hg, Co, Ni, and Hf. The model is built on more than 7000 equilibrium and non-equilibrium structures derived from first-principles density functional theory (DFT) calculations. The optimized large-scale fine-tuned model attains state-of-the-art accuracy, with a mean absolute error (MAE) and root-mean-square error (RMSE) of 2.27 meV/atom and 3.79 meV/atom for energy predictions, and 13.83 meV/Å and 24.26 meV/Å for force predictions, respectively. Systematic evaluation under different data-splitting protocols shows that unknown element extrapolation remains challenging under strict dopant hold-out, whereas substantially improved accuracy can be achieved in partial-exposure transfer settings. The fine-tuned models reduce the MAE by approximately 7–10 times compared to models trained from scratch, and by 10–20 times relative to zero-shot foundation models. This performance gain remains consistent across varying dataset sizes (equilibrium vs. non-equilibrium structures) and model scales. Our work illustrates the efficacy of transfer learning from globally ordered homogeneous systems to locally ordered heterogeneous multicomponent alloy environments. However, direct transfer to entirely unknown elements remains challenging, especially when proxy embeddings are employed without fine-tuning. Thus, to achieve high accuracy without incurring additional cost, it is essential to include unknown elements in the training dataset while minimizing the number of configurations containing known elements. Moreover, the current findings are primarily validated for dilute Mo-based alloy systems. Extending this approach to more compositionally complex alloy spaces may necessitate additional data and further fine-tuning. Full article

Background/Objectives: Healthcare facilities are among the most energy-intensive public buildings, yet hospital decision-support models rarely integrate energy-related performance indicators alongside operational metrics. This study aims to address this gap by developing a discrete-event simulation framework capable of jointly evaluating clinical efficiency and energy consumption in elective orthopedic surgical pathways. Methods: A comprehensive discrete-event simulation model was developed to represent the diagnostic imaging and orthopedic surgical process. The model was parameterized using a hybrid data-collection approach that combined clinical activity data, scientific literature, and expert judgment. Energy consumption was modeled by differentiating fixed loads, such as heating, ventilation, and air-conditioning systems and lighting, from activity-dependent loads associated with diagnostic and surgical equipment. Baseline performance was assessed and compared with alternative scenarios for organizational and technological improvements. Results: The analysis showed that fixed infrastructural loads, particularly HVAC systems, were the main drivers of per-patient energy consumption, with inefficient space utilization and prolonged idle times. Scenario analysis demonstrated that organizational interventions, such as increasing operating room throughput and optimizing MRI scheduling, can substantially reduce energy intensity by diluting fixed loads and decreasing idle consumption. Technological interventions, such as replacing conventional surgical lamps with LED systems, produced smaller but still beneficial reductions. The combined implementation of organizational and technological strategies yielded the greatest overall improvement. Conclusions: Integrating energy metrics into discrete-event simulation provides effective support for hospital decision-making by revealing the interaction between workflow design, resource utilization, and environmental performance. The findings indicate that organizational redesign, particularly when combined with technological upgrades, can significantly improve both operational efficiency and sustainability in hospital settings. This study highlights discrete-event simulation as a promising tool for energy-aware healthcare planning. Full article

Decentralized sustainable microgrids are emerging as a promising approach for addressing the increasing complexity of modern power systems while ensuring reliable and efficient operation. A fundamental driver of this transition is the partitioning of distribution networks into self-sufficient microgrids supported by the effective integration of Distributed Energy Resources (DERs) and Energy Storage Systems (ESSs), enabling improved power flow management and enhanced voltage stability. In this regard, this paper proposes a tri-stage optimization framework designed to segment power distribution systems into multiple self-sustaining microgrids while maintaining optimal network performance. In the first stage, the distribution grid is partitioned into microgrid clusters based on electrical distance metrics and bus correlation analysis. The second stage focuses on the optimal sizing and operational management of DERs and ESSs within each identified microgrid to ensure energy self-sufficiency and minimize greenhouse gas (GHG) emissions. In the third stage, an optimal resource allocation strategy is implemented, where the resources determined in the previous stage are optimally placed within the distribution network to achieve optimal power flow, reduce system losses, and maintain voltage stability under worst-case operating conditions. The proposed framework is validated using the IEEE 33-bus test system. Simulation results demonstrate its effectiveness in multi-microgrid classification, coordinated planning, and resource allocation, highlighting its superiority in enhancing system performance and resilience. Full article

With the increasing occurrence of extreme high-temperature weather events, the traditional bundled operation of wind power (WP), photovoltaic power (PV), and pumped storage hydropower (PSH) is facing dual challenges, namely intensified renewable energy fluctuations and insufficient flexible regulation capability of PSH. Therefore, this paper proposes an optimal scheduling strategy for bundled operation based on capacity interval matching of PSH with WP and PV under extreme high-temperature weather. First, typical scenarios are generated based on a Time-series Generative Adversarial Network (TimeGAN), and an interval matching transaction model is established based on the forecast intervals of WP and PV capacity and the corrected intervals of PSH capacity. Second, considering PSH as an independent market entity, a bi-level optimization model is constructed, in which the upper-level objective is to maximize the revenue of PSH, while the lower-level objective is to minimize the total cost of the joint clearing of the energy and ancillary service markets. Finally, simulation case studies verify that under extreme high-temperature weather, the proposed optimal scheduling method increases the bundled operation capacity by 17.9% and improves the revenue of PSH in the reserve ancillary service market by 14.8%, thereby effectively enhancing the economic performance of PSH while ensuring the safe and stable operation of the system. Full article

Electric energy metering cabinets serve as critical nodes in power grid operations, providing essential protection for key components in distribution networks. Under environmental stressors, the non-metallic casings of electric energy metering cabinets are susceptible to aging-induced performance degradation, which may result in electrical safety hazards. However, rapid and precise methods for evaluating the performance of these non-metallic casings are still lacking. Laser-induced breakdown spectroscopy (LIBS), capable of rapid multi-element detection with non-contact analytical advantages, was employed in this study. Thermal aging experiments were conducted to investigate the performance degradation mechanisms of sheet molding compound (SMC)—a representative non-metallic cabinet material. The research analyzed time-dependent trends in material performance and microstructural evolution during aging. By integrating LIBS with multi-analytical techniques, this study further explored the feasibility of quantitatively evaluating the bending strength of thermally aged SMC, which has rarely been reported in previous studies. Based on LIBS spectral data, bending strength characterization revealed its attenuation patterns with aging duration. The relationships between bending strength and plasma temperature, as well as the characteristic line intensity ratios of K, Al, and Ca, were systematically examined. A multivariate linear regression model incorporating these key variables was subsequently developed, yielding a high coefficient of determination (R² = 0.9657) between the predicted and measured bending strength values. This model represents a promising initial step, but further validation with a larger dataset is necessary to enhance its reliability and generalizability. Full article

Thermoacoustic power generation holds significant promise for applications such as solar thermal utilization, industrial waste heat recovery, and distributed energy systems, owing to its high efficiency and reliability. Conventional standing-wave and traveling-wave thermoacoustic generators, however, are often limited by bulky resonators and substantial acoustic power dissipation. Replacing the resonator with a linear alternator (LA) offers an effective means to improve system compactness and output performance. Nonetheless, under standing-wave acoustic conditions, the LA’s large piston swept volume increases the device size, thereby constraining overall compactness. To address this limitation, a novel moving-magnet LA with electromagnetic components integrated into the moving piston is proposed. Compared to conventional configurations, this design significantly reduces the size and weight of the alternator. Furthermore, the influence of different magnetic circuit configurations on output performance is systematically investigated, enabling optimization of the alternator design. Results demonstrate that the proposed alternator achieves a more compact structure while delivering output performance comparable to that of conventional external magnetic-circuit designs, thereby validating the feasibility of the proposed approach. Full article

Traditional polymer-dispersed liquid crystal (PDLC) faces limitations in smart dimming applications due to high driving voltage and poor high-temperature stability. In this study, a high-birefringence liquid crystal (QYPDLC-901) was used to prepare PDLC films with liquid crystal contents ranging from 72 wt% to 80 wt%, achieved through synergistic regulation of a low-functional acrylic polymer system and a low-intensity curing process. The effects of liquid crystal content, cell gap, and temperature on electro-optical properties were systematically investigated. Optimal performance was obtained at a liquid crystal content of 77 wt%, with a low threshold voltage of 2.9 V, saturation voltage of 7 V, fast response (rise time 4.2 ms, decay time 47 ms), and a favorable balance between high on-state and low off-state transmittance. Microstructural analysis revealed that the superior performance results from uniform droplet dispersion and low interfacial energy. Furthermore, the PDLC exhibited excellent switching stability from 23 °C to 90 °C, maintaining a maximum transmittance of 93% at 90 °C, with increases of only 0.4 V in threshold voltage and 0.1 V in saturation voltage. This study provides an experimental basis for designing smart dimming devices suitable for low-voltage driving and extreme environments. Full article

This study presents an experimental investigation into the repair and seismic performance enhancement of earthquake-damaged reinforced concrete (RC) frame structures using high-strength cement mortar and viscous dampers. A 1/4-scale, four-story RC frame model—designed according to a seismic fortification intensity of 8 degrees (corresponding to 0.2 g PGA in China’s seismic code)—was subjected to shaking table tests under increasing levels of artificial seismic excitation. Following the first round of loading, the damaged structure was repaired using high-strength mortar infill, and 12 viscous dampers were installed for seismic upgrade. The second round of identical seismic loading was applied to evaluate the effectiveness of the repair strategy. Comparative analysis of structural responses before and after repair reveals that the combination of high-strength mortar and viscous dampers improved damping capacity. The initial natural frequencies of the repaired structure increased by 6% (X) and 24% (Y), and damping ratios rose—reaching 12.75% and 10.78% under rare ground motions (1.34 g). Peak acceleration and inter-story drift ratio (IDR) were effectively reduced under moderate seismic levels, although some increase in IDR was observed at higher intensities, all drift values remained within the seismic code limits. The viscous dampers significantly altered the inter-story deformation mechanism, reducing the deformation concentration factor (DCF) of the frame structure and resulting in a more uniform distribution of story drifts. In addition, the energy dissipation capacity of the dampers increased progressively with the intensity of seismic excitation. The results validate the feasibility and efficiency of integrating viscous dampers with high-strength mortar for seismic repair and retrofitting of RC frame structure. Full article

This research introduces a novel methodology that combines Building Information Modelling (BIM) and Economic Multi-Criteria Decision-Making (EMCDM) with Neural Networks to optimize hybrid renewable energy systems in small communities. Its core aim is to improve sustainability, technical performance, and financial vokiability through integrated modelling and decision-making. The approach is applied to a hydropower site, evaluating five Scenarios (IDs 1–5) under a Community and Industry model. Financial benchmarks include a 10% Minimum Required Return and a 7-year payback period. ID3—hydropower, solar, and wind—proves most effective, with ANPV of €10,905 (wet) and €4501 (dry), and ROI of 155%/64%. Its ROIA/MRA Index peaks at 539%, and Payback/N ratios remain within acceptable limits (55%/96%). LCOE stays stable in average conditions (0.042–0.046 €/kWh), rising in dry years (0.07–0.10 €/kWh). Profitability differences primarily stem from demand and curtailment, rather than production costs. The NARX neural network reliably models SS% values from renewable inputs with low error across scenarios. The integrated BIM–EMCDM framework ensures transparent, sustainable, and risk-balanced energy system decisions for long-term autonomy. Full article

Hydraulic rock drill exhibits outstanding attributes of high power and high frequency, but there are some issues including unclear mechanisms governing impact dynamic behaviors and inaccurate evaluation of impact performance. In this study, a dynamic test platform for the hydraulic rock drill was established by employing the terminal velocity method, utilizing a high-frequency non-contact laser displacement sensor to precisely capture the transient kinematics of the impact piston. The quantitative results indicate that as the input pressure rises from 10 MPa to 23 MPa, the impact frequency increases from 50 Hz to 76.9 Hz, and the impact energy increases from 89.9 J to 275 J. A hydraulic rock drill AMESim simulation model incorporating the impact system, collision medium and buffer system was developed and validated. This reveals the operating mechanism of impact piston driven by the equivalent pressure difference between the front and rear chambers. And the stroke reversal interval governs the duration between the deceleration onset and collision of the impact piston. As a result, both excessively large and small stroke reversal intervals will lower the impact power. The 12 mm stroke reversal interval has been identified as the optimal setting for maximizing impact power, at which the impact power reaches 17,561.3 W, which presents an increase of 4.70% and 3.12% compared to the intervals of 7 mm and 17 mm, respectively. This study contributes a reliable theoretical basis and direct data support to the performance evaluation and optimized design of hydraulic shock systems. Full article