First-generation biofuel feedstocks raise concerns related to food security and land use, whereas second-generation lignocellulosic biomass offers a more sustainable alternative. The floating aquatic weed Eichhornia crassipes has been widely studied for its biofuel potential; however, the rooted species Eichhornia azurea, with similar invasive traits, remains largely underexplored. This work presents a comparative study of the potential of the two species as biofuel feedstock. Samples of both species were collected from two locations, Mwanza Gulf (MG) and Baumann Gulf (BG), and taxonomically identified. Proximate and lignocellulosic composition analyses of leaves, stems, and roots were conducted using ASTM standard methods. For E. azurea, proximate analysis yielded average values of 91% moisture, 72% volatile matter, 19% ash, and 9% carbon for MG samples and 90%, 71%, 14%, and 16%, respectively, for BG samples. For E. crassipes, corresponding values were 91%, 63%, 25%, and 10% (MG) while 92%, 62%, 24%, and 13% (BG). Lignocellulosic composition of E. azurea was recorded to be 19% cellulose, 24% hemicellulose, and 25% lignin for MG and 8%, 31%, and 35% for BG, compared with E. crassipes 6%, 21%, and 30% (MG) and 7%, 24%, and 28% (BG). Hydrolysis results showed that E. azurea released higher reducing sugars (457 mg/g in MG and 405 mg/g in BG) compared with E. crassipes (280 mg/g for MG and 335 mg/g for BG). The higher sugar release translates into greater availability of fermentable sugars, which are critical intermediates for bioethanol and biogas production. Overall, E. azurea outperformed E. crassipes in both locations, with E. azurea from MG exhibiting the most superior fuel characteristics.
Streamwise pressure gradients and turbulence intensity can affect wind turbine performance and wake characteristics. In this work, we investigate the combined effect of a constant pressure gradient and turbulence intensity on the performance and wake of a turbine via wind tunnel experiments. Five different pressure gradients are imposed using linear ramps, and three different surface characteristics are used to alter the turbulence intensity level. The power and thrust coefficients show high sensitivity to the pressure gradient, whereas limited sensitivity to the turbulence intensity is observed. On the other hand, power fluctuations are found to be dependent on the turbulence intensity but not on the pressure gradient. The wake velocity deficit depicts the typical behavior of a faster recovery under a higher turbulence intensity for a certain pressure gradient, where the velocity deficit increases for an adverse pressure gradient and decreases for a favorable one compared to zero pressure gradient for all turbulence intensities. The wake growth rate and near-wake length are characterized as functions of pressure gradient and turbulence intensity. It is, thereby, observed that an increase in turbulence intensity diminishes the effect of pressure gradient on the wake growth rate and near-wake length. Finally, two analytical models for wake velocity deficit under pressure gradient are compared against the experimental data.
Bifacial photovoltaic (PV) technology enhances solar energy conversion by capturing irradiance from both front and rear surfaces, but accurate performance prediction remains challenging due to complex spatial irradiance distribution and electrical mismatch losses. This study proposes a hybrid modeling framework integrating physics-based irradiance calculations with machine learning techniques for cell-level analysis of bifacial PV modules. The physics-based component combines solar geometry with numerical view-factor analysis to evaluate front- and rear-side irradiance under realistic three-dimensional configurations. An artificial neural network accelerates view-factor computation, reducing simulation time by 65% compared to full numerical methods while maintaining mean absolute error below 0.003. A convolutional neural network detects irradiance anomalies from partial shading, soiling, and cell faults with 96.8% classification accuracy. The framework is validated using experimental measurements from a 395 Wp bifacial module (Voc = 46.8 V, Isc = 10.97 A) under diverse environmental conditions throughout 2025. Results show rear-side irradiance prediction errors below 3% and power output errors within 3.1%. Analysis reveals that nonuniform albedo reflection causes mismatch losses up to 5.8% in south-facing 30°-tilted configurations under medium albedo (ρg = 0.5), while vertical east/west installations reduce losses to 1.2%–2.1%. Ground clearance significantly impacts irradiance distribution, with mismatch losses decreasing from 6.8% at d = 0.3 m to 2.1% at d = 2.0 m. The proposed framework enables accurate, computationally efficient cell-level analysis for bifacial PV system optimization and performance prediction.
This paper explores the integration of solar roof technology with electric vehicles by utilizing Switched Reluctance Motors (SRMs). We are designing a dynamic system model to run an electric vehicle motor at different conditions. This research examines the synergistic benefits of combining photovoltaic sunroof systems with the robust, cost-effective SRM drivetrain architecture. We employ a maximum power point tracking-based photovoltaic array to achieve maximum power while also using a Zeta converter to minimize voltage and current fluctuations, ensuring a smooth power supply. For different atmospheric conditions, such as irradiance and temperature, we consider constant temperature and variable irradiance in this paper. SRMs offer significant advantages for solar-augmented EVs; solar roof systems can extend the driving range by 25%–30% under optimal conditions, while SRMs efficiently handle the intermittent nature of solar power through their inherent torque-speed characteristics. Under no load to full load conditions, the speed should be accelerated to 4000 rpm in 1.5–2.0 s. This research contributes to advancing sustainable transportation solutions by leveraging renewable energy sources directly at the vehicle level while utilizing motors that align with resource-conscious manufacturing principles. By using these profiles, controllers can be designed for gradual acceleration, avoiding mechanical or electrical overstress during startup or abrupt changes in load. The advanced direct instantaneous torque control is capable of reducing the torque ripple by 8.8% at a rated load. With the solar roof connected, our suggested dynamic electrical vehicle model uses a Li-ion battery with a discharge rate of 0.0032%/s.
To overcome the conservativeness and limited scalability of conventional analytical Lyapunov function-based methods, this paper proposes a data-driven framework for region of attraction (ROA) estimation using bounded converse Lyapunov functions and Gaussian process regression (GPR). The bounded converse Lyapunov function maps trajectory-based stability information into a compact numerical range, making it more suitable for regression-based learning. GPR is then employed to construct a non-analytical Lyapunov function model, while an active learning strategy is introduced to improve sample utilization efficiency near the ROA boundary. Furthermore, uncertain parameters are incorporated as input features during model training, enabling approximate minimal ROA estimation under parameter uncertainty. Case studies on a single grid-following voltage source converter connected to an infinite bus system and the IEEE 39-bus system demonstrate that the proposed method significantly improves ROA boundary estimation accuracy compared with conventional approaches and can provide a conservative sampling-based approximation of the minimal ROA under parameter uncertainty.
The continuing growth of wind-turbine size, blade flexibility, offshore deployment, and wind-farm control requirements is increasing the need for aerodynamic models that retain wake physics at a computational cost suitable for engineering design. Blade element momentum methods remain efficient but depend on empirical wake and induction corrections, whereas high-fidelity computational fluid dynamics is often too expensive for long aeroelastic, floating-platform, and multi-turbine simulations. Filament-based free vortex wake (FVW) methods provide an intermediate-fidelity alternative by representing blade-bound and wake vorticity explicitly and recovering induced velocity through the Biot–Savart law. This review summarizes the theoretical foundation and current state of filament-based FVW methods for horizontal-axis wind turbines. The discussion is organized around the modeling workflow: vorticity–velocity formulation, projection of blade and wake vorticity, Lagrangian wake advection, induced-velocity regularization, diffusion and core growth, stretching treatment, circulation convergence, dynamic-stall coupling, computational acceleration, and engineering applications. Particular attention is given to the distinctions among lifting-line, lifting-surface, vortex-lattice, thick-panel, filament, ring, and reduced-wake formulations. By connecting the governing vorticity equations with practical implementation choices and recent applications, the review clarifies the capabilities, limitations, and future development needs of filament-based FVW methods for large, flexible, floating, and interacting wind turbines.
Exploring industrial load resources to participate in demand response (DR) is a crucial approach to enhance the flexibility and renewable energy accommodation capacity of modern power grids. Industrial conveying processes are energy-intensive loads and have significant operational flexibility, making them valuable load resources for DR programs. In this paper, the conveying systems are first aggregated into load clusters to participate in customer directrix load (CDL) based DR programs. The operation feasible domains of a belt conveyor and the conveying load clusters are constructed, respectively. Then, a day-ahead optimal control model for conveying load clusters to participate in a CDL based DR program is built with the formulation of the objective function and the constraints at the single conveyor level, load cluster level, and system level. A cement clinker conveying system is taken as the study case for model verification. In the simulation study, three scenarios for the conveying load clusters participating in a CDL based DR program are compared and analyzed. The third scenario, as a combination of scenario 1 and scenario 2, is considered to be the best option. It minimizes the comprehensive cost of the cement clinker conveying system by driving its load profile to track CDL through responding to CDL based DR and the time-of-use price simultaneously.
Increasing energy demands and climate challenges require innovative approaches to renewable energy integration, particularly in remote regions where grid extension is economically unfeasible. Traditional microgrid planning methods suffer from poor renewable forecasting integration and static component sizing that cannot adapt to changing conditions. This study presents a unified framework that integrates probabilistic wind forecasting with intelligent microgrid planning and control. The framework combines three core innovations: a hybrid convolutional neural network-long short-term memory ensemble forecasting model with uncertainty quantification, multi-objective optimization using evolutionary algorithms with dynamic component resizing, and a Deep Reinforcement Learning-based Energy Management System. Unlike existing approaches that treat forecasting and control as isolated components, this framework propagates uncertainty from prediction through infrastructure planning to real-time operational decisions. Validation through a real-world case study in Sujawal, Pakistan, demonstrates significant performance improvements where the system cuts levelized cost of energy (LCOE) by 25%, boosts reliability by 60%, reduces diesel use by 41%, and maintains 97.6% grid stability. The probabilistic forecasting enables uncertainty-aware decision-making, while adaptive sizing allows infrastructure to evolve with changing conditions. This research advances the integration of forecasting and smart grid control by providing a mathematical foundation for resilient, data-driven renewable energy planning. The modular architecture ensures generalizability across diverse geographical contexts and renewable sources. The framework offers a viable alternative to grid extension in developing economies, demonstrating that forecast-informed microgrids can achieve superior economic and environmental performance while maintaining high reliability standards.
This review provides a comprehensive overview of the progress in semitransparent solar panels, a critical technology for building-integrated photovoltaics (BIPV), energy-generating windows, and agrivoltaics. The paper traces the evolution and outlines the state-of-the-art of core technologies, including dye-sensitized solar cells (DSSCs), organic photovoltaics (OPVs), perovskite solar cells (PSCs), luminescent solar concentrators (LSCs), colloidal quantum dots, transparent crystalline silicon (c-Si), and ultrathin Cu(In,Ga)Se2 (CIGS) devices. A central theme is the inherent tradeoff between power conversion efficiency (PCE) and average visible transmittance (AVT), which defines the application space for each technology. Significant progress has been achieved across multiple semitransparent photovoltaic platforms, although reported values must be interpreted in the context of transparency level, device area, and measurement conditions. In opaque reference devices, PSCs and tandem architectures have surpassed 26% and 33.7% PCE, respectively, illustrating the maturity ceiling of the material platform. In contrast, semitransparent PSCs typically operate at lower PCE but over a wide AVT range depending on absorber thickness and electrode design. OPVs offer esthetic versatility with tunable, vibrant colors achieved through microcavity engineering, reaching over 15% PCE at moderate transparency (AVT ≈ 46%). LSCs prioritize transparency, achieving AVT values of up to 92% through ultraviolet absorption and edge emission, albeit with lower PCEs around 1.4%. Innovations in materials and device architecture are explored in detail. These include the development of metal-free dyes and tandem structures in DSSCs; non-fullerene acceptors and scalable peel-off patterning for OPVs; advanced passivation strategies and strain regulation for PSC stability; and lead-free I–III–VI quantum dots to address toxicity concerns. A notable advancement is the development of transparent c-Si solar cells using micropore arrays and all-back-contact architectures, which enable color-neutral modules with 14.7% PCE at 20% AVT. Similarly, ultrathin CIGS devices have achieved over 10% PCE with ∼ 12% AVT through optical management strategies. The review also examines practical applications through case studies, including the performance of BIPV systems in real-world conditions, the use of spectrally selective films in agrivoltaics to simultaneously support plant growth and generate electricity, and the design of hybrid photovoltaic–thermal windows that produce both electricity and heat. Overall, this work encapsulates the dynamic progress of semitransparent photovoltaics, charting their transition from laboratory-scale concepts to commercially promising solutions poised to transform architectural and agricultural surfaces into active power-generating assets.
To enhance the performance and economic benefits of independent energy storage power stations participating in the electricity market, a hybrid energy storage system (HESS) combining lithium batteries (LiB) and flywheel energy storage (FES) is developed, and a stochastic optimization-based method for operation and capacity configuration of HESS is proposed. First, an operation strategy is established for energy storage participation in the electricity spot market, aiming to maximize the daily net revenue of LiB. In the frequency regulation market, a model predictive control (MPC)-based power allocation strategy for the LiB-FES HESS is proposed to improve the performance in responding to automatic generation control signals. Furthermore, considering the uncertainty of frequency regulation bid winning, a stochastic bid-winning model for power stations is established. A day-ahead bidding strategy based on stochastic optimization is developed to balance daily revenue and risks. Finally, with the goal of maximizing the annualized revenue of the HESS over the project planning horizon, an annualized revenue model covering the entire lifecycle is established. Optimization is achieved through joint iterative optimization of capacity configuration and operation strategies. Case study results show that, compared to the commonly used first-order filtering method, the proposed MPC strategy increases the daily frequency regulation performance index Kp by 15.6% and daily net revenue by 15.9% under fixed energy storage capacity. The stochastic capacity optimization bidding strategy increases the average daily net revenue by 6.8% compared to the fixed capacity approach. Overall, the proposed configuration method enhances the annualized net revenue of the power station by 27.9% compared to conventional engineering methods.
Existing studies on in-cylinder water injection (WI) are primarily focused on parameters such as water-to-fuel ratio (W/F) and injection timing. However, systematic and quantitative analysis on WI plume and in-cylinder flow field interaction, mixing process, and NOx formation mechanisms is lacking. Moreover, such studies are even more limited in biodiesel direct-injection compression ignition engines. To address this research gap, a three-dimensional numerical model was established and validated. The steady-state operating conditions in this study were a W/F of 0.6, an injection pressure of 30 MPa, and an engine speed of 1600 rpm. The structure of the in-cylinder flow field was adjusted by varying the injection position (40/50/60 mm) and injection angle (15°/30°/45°). The effects on water mist evaporation, homogeneity index, combustion phasing, and emissions were analyzed. A parameter map suitable for engineering design was derived. The results indicate that an outward shift of the injector position combined with a larger injection angle reduces the low-velocity core in the central recirculation zone, enhances water evaporation, and improves mixture homogeneity, as reflected by the higher HI. Consequently, the ignition delay (CA0-10) was extended to 23.01 °CA, the combustion duration (CA10-90) was shortened to 8.88 °CA, the peak temperature was reduced by up to 127.98 K, and NOx emissions were reduced by up to 48.45%. Comprehensive multi-indicator analysis of evaporation rate, wall film formation, combustion phase characteristics, and NOx emissions indicates that the optimal nozzle arrangement is 60-45. Additionally, an injection position of 50–60 mm and an injection angle of 30°–45° are recommended.
The ever-decreasing availability of fossil fuels and their adverse environmental impacts have become major global concerns, as most countries, including Algeria, rely heavily on fossil fuels for energy production. Moreover, fluctuations in fossil fuel prices significantly influence the economic growth of these countries. Given the abundance of renewable energy sources, their integration into electricity generation has become increasingly important. In this context, this study aims to supply the electricity demand of a travelers' service station located in the Ouargla region of Algeria by proposing an optimal design and conducting a techno-enviro-economic feasibility analysis of an off-grid photovoltaic (PV)–diesel–battery hybrid energy system (HES). All system configurations were simulated using the hybrid optimization model for electric renewables software. Furthermore, a comparative analysis was performed among the resulting configurations to identify the most suitable system from economic, technical, and environmental perspectives. The optimal configuration was selected based on the total net present cost (TNPC), cost of energy (COE), and pollutant gas emissions (PGEs). The results indicate that the PV–diesel–battery HES is the most cost-effective configuration, with a TNPC of $777 852.77 and a COE of $0.130/kWh. In addition, this configuration is environmentally friendly, achieving a renewable fraction of 35.5% while producing only 82 090.29 kg/yr of total PGE.
The integration of liquid pistons into compressed air energy storage systems for achieving isothermal or quasi-isothermal compression has emerged as a significant research focus. However, during the compression process, liquid pistons dissolve considerable amounts of gas, adversely affecting system efficiency and stability. To explore the impact of gas dissolution on liquid piston compressed air energy storage systems, this paper constructs an air dissolution model based on chemical potential equilibrium theory. This model is subsequently coupled with the liquid piston compressed air system model, unveiling the patterns of air dissolution within the system. Additionally, the paper examines the effects of initial temperature and pressure on air dissolution. The findings indicate that in a closed variable space, the amount of gas dissolved increases with rising pressure and decreases with higher temperatures. During compression, air dissolution reduces system efficiency from 89.25% to 81.68%. While the initial temperature has minimal impact on air dissolution, the initial pressure significantly influences gas dissolution. With an initial pressure of 2 MPa and a compression ratio of 7, 30.42% of the air dissolves into the liquid piston.
As the core component of the organic Rankine cycle, the variable nozzle radial-inflow turbine can achieve superior off-design performance by adjusting its nozzle angle. However, traditional methods treat turbine design and working fluid selection independently, requiring significant computational resources and time. In this study, a turbine optimization model is developed by integrating its design model into the thermo-economic model of the organic Rankine cycle. Furthermore, an encoding method is proposed to transform the working fluid into a decision variable, enabling simultaneous determination of the optimal working fluid and aerodynamic parameters of the turbine through multi-objective optimization. Additionally, the off-design performance of the optimized turbine under different nozzle angles and operating conditions is evaluated using computational fluid dynamics and the entropy production method. The results indicate that R600 is the optimal working fluid for the turbine, with an optimal rotational speed of 25 254 rpm, a nozzle outlet angle of 18.92°, and a nozzle inlet radius of 0.1289 m. Under these optimal conditions, the system achieves a net power output of 358 kW, with a thermal efficiency of 12.12%, a levelized energy cost of 0.0328 $/kWh, and a turbine dimension factor of 3.85 × 10−7 m/W. Under off-design conditions, flow losses in the turbine primarily originate from the rotor, with passage vortices near the suction side of the blades playing a dominant role. The turbine achieves a maximum efficiency of 81.5% at a nozzle angle of 26° and an outlet pressure of 0.2 MPa. The off-design performance of the turbine can be significantly improved by selecting a suitable nozzle exit angle according to the operating conditions.
Community integrated energy systems (CIES) are a promising option for integrating distributed renewable energy in residential communities, yet the factors shaping residents' willingness to participate remain underexamined. This study develops an extended Technology Acceptance Model that incorporates psychological, social, institutional, and economic factors and tests the proposed framework using survey data from 589 respondents via structural equation modeling. The results show that attitude toward use is the strongest predictor of residents' behavioral intention, followed by government involvement and perceived cost. Technology trust, resident innovativeness, perceived ease of use, and subjective norm also significantly influence intention, whereas attitude toward renewable energy, perceived usefulness, and environmental concern play comparatively weaker roles. By identifying the main behavioral drivers of CIES participation, this study links household-level adoption behavior to community energy planning and provides evidence to inform policies that can accelerate distributed low-carbon transitions and the development of sustainable community energy systems.
To address transient grid impacts and high operating costs caused by high-frequency, high-power charging of new-energy locomotives in locomotive depot microgrids, this paper innovatively proposes a timetable-driven spatiotemporal-energy decoupling collaborative scheduling method. First, a locomotive spatiotemporal state correlation matrix and a discrete energy mapping model are established, reconstructing the rigid traffic load into a virtual energy storage system with great large-capacity temporal translation potential. Second, a multi-objective optimal scheduling model is constructed to minimize microgrid comprehensive operation and maintenance costs and main grid interactive power fluctuation, considering locomotive smooth ramping and battery safe operation boundaries. For the high-dimensional, discontinuous mixed-integer nonlinear programming problem, an improved multi-objective particle swarm optimization–mantis shrimp optimization algorithm integrated with physical constraint masking is proposed, which reduces dimension via the underlying temporal feasible region filtering mechanism and achieves efficient coordination between global exploration and local exploitation. Multi-scenario simulations show the strategy guides locomotive clusters to form refined temporal peak-shifting. Compared with unordered charging, system comprehensive operation and maintenance costs decrease by 59.6%, main grid interactive power fluctuation by 85.2%, achieving high economic benefits, new-energy local consumption rate and grid-connection friendliness while ensuring transportation rigid demand.
In response to the shortcomings of traditional green and low-carbon economic industrial clustering path selection in data processing efficiency, path optimization, industrial collaboration, and policy-making, this paper innovatively applies automated data preprocessing, graph neural network (GNN) modeling, reinforcement learning (RL) dynamic optimization, and multi-objective optimization methods to solve problems, providing scientific and efficient decision-making support for achieving the “carbon peaking and carbon neutrality” goals. Renewable energy integration and sustainable energy transition form the core context of this investigation. First, multi-source data are collected and preprocessed. Then, K-means clustering and principal component analysis are used to optimize industrial cluster site selection. Model prediction capabilities are improved through recursive feature elimination and expert knowledge. Next, an enterprise relationship graph is constructed, and the GNN is applied to identify key nodes and collaboration patterns in the industrial chain. The RL is simultaneously adopted to simulate the dynamic development of the industrial chain and learn the optimal strategy. In addition, regression models and time-series analysis (long short-term memory) are used to predict the impact of policies on carbon emissions and future trends, and to establish a risk warning mechanism. Finally, a multi-objective optimization algorithm is used to balance environmental and economic benefits, and the enterprise layout is optimized to reduce transportation costs and carbon emissions. Experimental results show that the proposed machine learning framework improves data preprocessing efficiency by 85%, reduces cluster error by 60%, shortens path optimization time by 84%, and achieves a 66.7% reduction in carbon emissions within the simulation environment, demonstrating a measurable advance in the rigor and computational tractability of path selection for green, low-carbon economic industrial clustering.
To address the issues of high operational costs and considerable grid load changes in photovoltaic (PV)-integrated energy storage charging stations, this research provides a multi-timescale optimization scheduling method based on time-varying boundary constraints. The method employs a feature mode decomposition-long short-term memory-Bootstrap interval prediction strategy to boost forecasting accuracy for electric vehicle (EV) charging loads and PV generation, attaining a 95.74% coverage rate. The scheduling framework implements a bi-layer optimization model: the upper layer maximizes the load margin index for grid stability and minimizes scheduling deviation between layers, while the lower layer optimizes individual EV charging strategies by minimizing operational costs, user target residual energy, and scheduling deviations. The innovation comes in introducing dynamic time-varying boundary restrictions informed by prediction intervals, time-of-use power pricing, and PV feed-in tariffs, enabling adaptive energy flow optimization that adapts to forecast uncertainty in real-time. Comprehensive simulations using IBM ILOG CPLEX Optimization Studio (CPLEX) demonstrate that, compared with the single-timescale scheduling scenario, the proposed multi-timescale strategy reduces the total daily operation cost by 17.56% and decreases the root mean square error of grid load fluctuations by 32.4%, while ensuring that all EV users maintain sufficient state-of-charge above 0.5, thereby validating its effectiveness in improving both economic efficiency and grid stability.
Combustion of fossil fuels harms the environment due to the release of nitrous oxide, carbon monoxide, nitrogen oxide, sulfur dioxide, and other pollutants that cause global warming. To address these issues, hydrogen is a clean and sustainable energy source alternative to fossil fuels. Hydrogen has been produced in different ways, but the biologically produced hydrogen is eco-friendly and cost-effective. Biohydrogen was classified into dark fermentation and light fermentation. Most hydrogen generation processes employ bacteria, which can significantly increase hydrogen production. This review critically examines recent advances in bacterial biohydrogen production, focusing on metabolic pathways and fermentation processes. It shows different microbes and genetically modified strains to improve hydrogen yield, as well as the combination of processes and artificial intelligence to optimize the processes. It also covers the potential of commercialization, the level of technology readiness (TRL), and future prospects for viable and sustainable hydrogen production.
The growing need for sustainable and decentralized energy solutions has sped up the development of hybrid energy systems that mix energy harvesting and storage technologies. Triboelectric nanogenerators (TENGs) are becoming promising devices for converting ambient mechanical energy into electrical energy. Meanwhile, hydrogen energy provides a clean and high-density medium for energy storage. This review looks at how TENGs can be integrated with hydrogen production and storage systems, focusing on material innovations, device designs, and system-level connection strategies. We examine the processes behind TENG-driven electrolysis, self-powered hydrogen generation, and hybrid energy storage setups. We also analyze key challenges, including low output current, system efficiency, and scalability. Finally, we outline future directions such as improving catalysts, creating hybrid nanostructures, and developing smart energy systems to support the practical use of TENG–hydrogen hybrid technologies.
