Search Results (158)

Hydroelectricity energy harvesting has emerged as a promising, eco-friendly alternative for addressing the growing demand for sustainable energy solutions. In this study, we present a hydroelectricity energy harvester fabricated from shredded waste printing paper (WPP), offering a novel waste-to-energy conversion strategy that requires neither material purification nor complex processing. The device leverages the randomly entangled fiber network of WPP to facilitate capillary-driven moisture diffusion and electric double layer (EDL) formation, thereby enabling efficient electrokinetic energy conversion. The random arrangement of WPP fibers increases the effective EDL area, allowing the waste printing paper generator (WPPG) to achieve an open-circuit voltage of 0.372 V and a short-circuit current of 135 μA at room temperature under optimized electrolyte conditions. This study demonstrates that carbon-black-coated WPP can be effectively upcycled into a high-performance hydroelectricity generator, exhibiting excellent electrical output at ambient conditions. By combining material recycling with efficient energy conversion, this system establishes a practical and sustainable pathway for distributed power generation. Overall, this work not only presents an environmentally responsible approach to device fabrication but also highlights that hydroelectricity energy harvesting using WPPG represents a promising alternative energy route for future applications. Full article

Redox flow batteries (RFBs) are an emerging class of large-scale energy storage devices, yet the commercial benchmark—vanadium redox flow batteries (VRFBs)—is highly constrained by a modest open-circuit potential (1.26 V) while posing an expensive and volatile material procurement costs. This review focuses on recent progress in diversifying redox-active species to overcome these limits, highlighting chemistries that increase overall cell voltage, energy density, and efficiency while maintaining long cycle life and safety. The study dwells deeper into manganese-based systems (e.g., Mn/Ti, Mn/V, Mn/S, M/Zn) that leverage Mn’s high positive potential while addressing Mn(III) disproportionation reactions; iron-based hybrids (Fe/Cr, Fe/Zn, Fe/Pb, Fe/V, Fe/S, Fe/Cd) that exploit the low cost, and its abundance, along with membrane and electrolyte strategies to prevent the potential issue involving crossover; cerium-anchored catholytes (Ce/Pb, V/Ce, Eu/Ce, Ce/S, Ce/Zn) that deliver high operational voltage by implementing an acid-base media, along with selective zeolite membranes; and halide systems (Zn–I, Zn–Br, Sn–Br, polysulfide–bromine/iodide) that combine fast redox kinetics and high solubility with advances such as carbon-coated membranes, bromine complexation, and ambipolar electrolytes. Across these various families of RFBs, the review highlights the modifications made to the flow-fields, membranes, and electrodes by utilizing a zero-gap serpentine flow field, sulfonated poly(ether ether ketone) (SPEEK) membranes, carbon-modified and zeolite separators, electrolyte additives to enhance the voltage (VE%), and thereby energy (EE%) efficiency, while reducing the overall system cost. These modifications to the existing RFB technology offer a promising alternative to traditional approaches, paving the way for improved performance and widespread adoption of RFB technology in large-scale grid-based energy storage solutions. Full article

Micro-photosynthetic power cells (μPSCs), also known as biophotovoltaics (BPVs), represent sustainable and self-regenerating solutions for harvesting electricity from photosynthetic microorganisms. However, their practical deployment has been constrained by low voltage, low current output, and scaling inefficiencies. In this work, we address these limitations through a dual-optimization strategy: (i) systematic quantification of how electrode surface area influences key performance metrics, and (ii) based on our previous work we highlighted the novel hybrid modular array architectures that combine series and parallel connections of μPSCs. Three single μPSCs with electrode areas of 4.84, 19.36, and 100 cm² were fabricated and compared, revealing that while open-circuit voltage remains largely area-independent (850–910 mV), both short-circuit current and maximum power scale with electrode size. Building on these insights, two hybrid array configurations fabricated from six 4.84 cm² μPSCs achieved power outputs of 869.2 μW and 926.4 μW, equivalent to ~82–87% of the output of a large 100 cm² device, while requiring only ~29% electrode area and ~70% less reagent volume. Importantly, these arrays delivered voltages up to 2.4 V, significantly higher than a single large device, enabling easier integration with IoT platforms and ultra-low-power electronics. A meta-analysis of over 40 reported BPV/μPSC systems with different electrode surface areas further validated our findings, showing a consistent inverse relationship between electrode area and power density. Collectively, this study introduces a scalable, resource-efficient strategy for enhancing μPSC performance, providing a novel design paradigm that advances the state of the art in sustainable bioenergy and opens pathways for practical deployment in distributed, low-power and IoT applications. Full article

In this study, structural optimization and trap effect analysis of a 4H-SiC–based p–i–n betavoltaic (BV) cell were performed using Silvaco ATLAS TCAD (version 5.30.0.R) simulations combined with an electron-beam (e-beam) irradiation model. First, the optimum device structure was derived by varying the thickness of the intrinsic layer (i-layer), the thickness of the p-layer, and the doping concentration of the i-layer. Under 17 keV e-beam irradiation, the electron–hole pairs generated in the i-layer were effectively separated and transported by the internal electric field, thereby contributing to the short-circuit current density (J_SC), open-circuit voltage (V_OC), and maximum output power density (P_{out_max}). Subsequently, to investigate the effects of traps, donor- and acceptor-like traps were introduced either individually or simultaneously, and their densities were varied to evaluate the changes in device performance. The simulation results revealed that traps degraded the performance through charge capture and recombination, with acceptor-like traps exhibiting the most pronounced impact. In particular, acceptor-like traps in the i-layer significantly reduced V_OC from 2.47 V to 2.07 V and P_{out_max} from 3.08 μW/cm² to 2.28 μW/cm², demonstrating that the i-layer is the most sensitive region to performance degradation. These findings indicate that effective control of trap states within the i-layer is a critical factor for realizing high-efficiency and high-reliability SiC-based betavoltaic cells. Full article

Triboelectric nanogenerators (TENG) are mechanical energy harvesters characterized by high sensitivity and simple structure and are currently being widely developed for use in human body motion sensing. Among them, fiber-based TENGs (FTENG) are particularly suitable for wearable human motion sensors due to their unique structure, which offers flexibility, high durability, and comfort. However, studies involving doping to further modify the electrical output characteristics of FTENGs are very limited. Here, we propose an innovative approach that combines graphite (GP) doping with fiber-based TENG fabrication, successfully developing a graphite-doped polyester fiber-based TENG (GP@PET-TENG). Proper graphite doping can increase the amount of transferred charge and thus improve the output electrical performance of TENG, but this method has rarely been explored in FTENG. With the incorporation of 3%wt graphite, the open-circuit voltage of the GP@PET-TENG increased from 103.3 V to 202.1 V, and the short-circuit current increased from 60.7 μA to 105.1 μA, compared to the pure polyester fiber based TENG (PET-TENG). The device achieved a maximum output power of 4.15 mW (2.59 W/m²), demonstrates the capability to charge various capacitors, and successfully lit up 200 LEDs. By attaching the GP@PET tribo-layer to human skin, a single-electrode mode TENG can be formed, which captures the subject’s motion signals through skin contact and separation, converting them into voltage outputs. In fist-clenching and wrist-bending tests, motion-induced voltage signals up to 0.6 V were recorded, demonstrating the potential applications in rehabilitation assistance and mechanical control. Full article

In this paper, a method to improve the protection of a four-core power cable of a salt-producing floating platform equipped with an automatic breaker with an independent tripping mechanism is suggested. The use of this automatic breaker in combination with a suggested protection device ensures reliable protection of not only the power cables of the platform against all faults but also the personnel of the platform and animals on the reservoir banks against electric shock in the event of a single-phase ground fault in reservoir water. This would be possible due to a voltage sensor made in the form of a metal ring on the power cable and a relay; one terminal of the relay winding is connected to the voltage sensor by a single-core control cable, and the other to the neutral of a power source on the platform. The typically open contacts of this relay are connected to an electric circuit which includes a power source and a coil for an independent tripping mechanism of the automatic breaker. This design ensures reliable operation of the suggested protection device in the event of a single-phase ground fault in the power cable of the platform when underwater cable insulation is damaged. Full article

Battery cell balancing is crucial in series-connected lithium-ion packs to maximize usable capacity, ensure safe operation, and prolong cycle life. This paper presents a comprehensive study and a novel adaptive duty-cycled hybrid balancing system that combines passive bleed resistors and an active switched-capacitor (SC) balancer, specifically designed for a 4-cell series-connected battery pack. This work also explored open circuit voltage (OCV)-driven adaptive pulse-frequency modulation (PFM) active balancing to achieve higher efficiency and better balancing speed based on different system requirements. Finally, this paper compares passive, active (SC-based), and adaptive duty-cycled hybrid balancing strategies in detail, including theoretical modeling of energy transfer and efficiency for each method. Simulation showed that the adaptive hybrid balancer speeds state-of-charge (SoC) equalization by 16.24% compared to active-only balancing while maintaining an efficiency of 97.71% with minimal thermal stress. The simulation result also showed that adaptive active balancing was able to achieve a high efficiency of 99.86% and provided an additional design degree of freedom for different applications. The results indicate that the adaptive hybrid balancer offered an excellent trade-off between balancing speed, efficiency, and implementation simplicity for 4-cell Li-ion packs, making it highly suitable for applications such as high-voltage portable chargers. Full article

Plant microbial fuel cells (PMFCs) represent an eco-friendly solution for generating clean energy by converting biological processes into electricity. This work presents the first integration of tin sulfide (SnS)-coated 304 stainless steel (SS304) electrodes into Aloe vera-based PMFCs for enhanced energy harvesting. SnS thin films were obtained via chemical bath deposition and screen printing, followed by thermal treatment. X-ray diffraction (XRD) revealed a crystal size of 15 nm, while scanning electron microscopy (SEM) confirmed film thicknesses ranging from 3 to 13.75 µm. Over a 17-week period, SnS-coated SS304 electrodes demonstrated stable performance, with open circuit voltages of 0.6–0.7 V and current densities between 30 and 92 mA/m², significantly improving power generation compared to uncoated electrodes. Polarization analysis indicated an internal resistance of 150 Ω and a power output of 5.8 mW/m². Notably, the system successfully charged a 15 F supercapacitor with 8.8 J of stored energy, demonstrating a practical proof-of-concept for powering low-power IoT devices and advancing PMFC applications beyond power generation. Microbial biofilm formation, observed via SEM, contributed to enhanced electron transfer and system stability. These findings highlight the potential of PMFCs as a scalable, cost-effective, and sustainable energy solution suitable for industrial and commercial applications, contributing to the transition toward greener energy systems. These incremental advances demonstrate the potential of combining low-cost electrode materials and energy storage systems for future scalable and sustainable bioenergy solutions. Full article

To establish a reliable thermoelectric module evaluation, a Standard Reference Thermoelectric Module (SRTEM) was developed based on stability. Open-circuit voltage (V_oc) was selected as the key calibration parameter due to its consistent response to temperature differences (ΔT). The SRTEM consists of eight p–n thermoelectric couples composed of metallic thermoelectric materials—Ni₉₀Cr₁₀ (chromel), Cu₅₅Ni₄₅ (constantan), Fe₆₄Ni₃₆ (invar), and pure Fe—selected based on their thermoelectric properties, structural compatibility, and contact resistance. Among the tested combinations, the chromel–constantan pair exhibited the highest V_oc of 55 mV at ΔT = 150 K. To increase V_oc and expand the usable calibration range, leg-shape modification and substrate replacement were investigated. Module simulation revealed that replacing the rectangular-leg geometry with a double-hourglass (2H/G) structure could increase V_oc by 20.2%. Furthermore, measurement of single-leg modules with substrates attached confirmed a 16.0% improvement in V_oc for the 2H/G shape over the rectangular shape, consistent with the predicted enhancement due to increased thermal resistance. In addition, replacing the alumina substrate with a higher thermal conductivity material, such as AlN, increased ΔT across the legs and yielded a further 9.1% improvement in V_oc. These results demonstrate the potential of the proposed SRTEM as a calibration standard for consistent thermoelectric module measurements. Full article

Photovoltaic (PV) systems are increasingly integral to global energy solutions, but their long-term reliability is challenged by various operational faults. In this article, we propose an advanced hybrid diagnostic framework combining a Deep Belief Network (DBN) for feature pattern extraction and a Light Gradient Boosting Machine (LightGBM) for classification to detect and diagnose PV panel faults. The proposed model is trained and validated on the QASP PV Fault Detection Dataset, a real-time SCADA-based dataset collected from 255 W panels at the Quaid-e-Azam Solar 100 MW Power Plant (QASP), Pakistan’s largest solar facility. The dataset encompasses seven classes: Healthy, Open Circuit, Photovoltaic Ground (PVG), Partial Shading, Busbar, Soiling, and Hotspot Faults. The DBN captures complex non-linear relationships in SCADA parameters such as DC voltage, DC current, irradiance, inverter power, module temperature, and performance ratio, while LightGBM ensures high accuracy in classifying fault types. The proposed model is trained and evaluated on a real-world SCADA-based dataset comprising 139,295 samples, with a 70:30 split for training and testing, ensuring robust generalization across diverse PV fault conditions. Experimental results demonstrate the robustness and generalization capabilities of the proposed hybrid (DBN–LightGBM) model, outperforming conventional machine learning methods and showing an accuracy of 98.21% classification accuracy, 98.0% macro-F1 score, and significantly reduced training time compared to Transformer and CNN-LSTM baselines. This study contributes to a reliable and scalable AI-driven solution for real-time PV fault monitoring, offering practical implications for large-scale solar plant maintenance and operational efficiency. Full article

Accurate estimation of the State of Charge (SOC) of lithium-ion batteries under complex operating conditions remains challenging, as the SOC signal combines a global linear (quasi-linear) trend with localized dynamic fluctuations driven by polarization, ion diffusion, temperature gradients, and load transients. In practice, open-circuit-voltage (OCV) approaches are affected by hysteresis and parameter drift, while high-fidelity electrochemical models require extensive parameterization and significant computational resources that hinder their real-time deployment in battery management systems (BMS). Purely data-driven methods capture temporal patterns but may under-represent abrupt local fluctuations and blur the distinction between trend and fluctuation, leading to biased SOC tracking when operating conditions change. To address these issues, LF-Net is proposed. The architecture decomposes battery time series into long-term trend and local fluctuation components. A linear branch models the quasi-linear SOC evolution. Multi-scale convolutional and differential branches enhance sensitivity to transient dynamics. An adaptive Fusion Module aggregates the representations, improving interpretability and stability, and keeps the parameter budget small for embedded hardware. Our experimental results demonstrate that the proposed model achieves a mean absolute error (MAE) of 0.0085 and a root-mean-square error (RMSE) of 0.0099 at 40 °C, surpassing mainstream models and confirming the method’s efficacy. Full article

This work proposes a methodology for the automated sizing of transistors in analog integrated circuits, based on a modular and hierarchical representation of the circuit. The methodology combines structured design techniques and systematic design flow to generate a hierarchy of simplified macromodels that define their specifications locally and are interconnected with other macromodels or transistor-level primitive blocks. These primitive blocks can be described using symbolic models or pre-characterized data from look-up tables (LUTs). The symbolic representation of the system is obtained using Modified Nodal Analysis (MNA), and the exploration of each block is performed using local design spaces constrained by top-level specifications. The methodology is validated through the design of low dropout voltage regulators (LDOs) for DC-DC integrated power systems using open-source tools and three process design kits: Sky130A, GF180MCU, and IHP-SG13G2. Results show that the methodology allows the exploration of several topologies and technologies, demonstrating its versatility and modularity, which are key aspects in analog design. Full article

Integrated circuits are the core of a cyber-physical system, where tens of billions of components are integrated into a tiny silicon chip to conduct complex functions. To maximize utilities, the design and manufacturing life cycle of integrated circuits rely on numerous untrustworthy third parties, forming a global supply chain model. At the same time, this model produces unpredictable and catastrophic issues, threatening the security of individuals and countries. As for guaranteeing the security of ultra-highly integrated chips, detecting slight abnormalities caused by malicious behavior in the current and voltage is challenging, as is achieving computability within a reasonable time and obtaining a golden reference chip; however, artificial intelligence can make everything possible. For the first time, this paper presents a systematic review of artificial-intelligence-based integrated circuit security approaches, focusing on the latest attack and defense strategies. First, the security threats of integrated circuits are analyzed. For one of several key threats to integrated circuits, hardware Trojans, existing attack models are divided into several categories and discussed in detail. Then, for summarizing and comparing the numerous existing artificial-intelligence-based defense strategies, traditional and advanced artificial-intelligence-based approaches are listed. Finally, open issues on artificial-intelligence-based integrated circuit security are discussed from three perspectives: in-depth exploration of hardware Trojans, combination of artificial intelligence, and security strategies involving the entire life cycle. Based on the rapid development of artificial intelligence and the initial successful combination with integrated circuit security, this paper offers a glimpse into their intriguing intersection, aiming to draw greater attention to these issues. Full article

We investigated a novel natural dye-sensitized solar cell (DSSC) utilizing gadolinium ruthenate pyrochlore oxide Gd₂Ru₂O₇ (GRO) as a photoanode and compared its performance to the TiO₂-Gd₂Ru₂O₇ (TGRO) combined-layer configuration. The films were fabricated using the spin-coating technique, resulting in spherical grains with an estimated mean diameter of 0.2 µm, as observed via scanning electron microscopy (SEM). This innovative photoactive gadolinium ruthenate pyrochlore oxide demonstrated strong absorption in the visible range and excellent dye adhesion after just one hour of exposure to natural dye. X-ray diffraction confirmed the presence of the pyrochlore phase, where Raman spectroscopy identified various vibration modes characteristic of the pyrochlore structure. Incorporating Gd₂Ru₂O₇ as the photoanode significantly enhanced the overall efficiency of the DSSCs. The device configuration FTO/compact-layer/Gd₂Ru₂O₇/Hibiscus-sabdariffa/electrolyte(I⁻/I₃⁻)/Pt achieved a high efficiency of 9.65%, an open-circuit voltage (Voc) of approximately 3.82 V, and a current density of 4.35 mA/cm² for an active surface area of 0.38 cm². A mesoporous TiO₂-based DSSC was fabricated under the same conditions for comparison. Using impedance spectroscopy and cyclic voltammetry measurements, we provided evidence of the mechanism of conductivity and the charge carrier’s contribution or defect contributions in the DSSC cells to explain the obtained Voc value. Through cyclic voltammetry measurements, we highlight the redox activities of hibiscus dye and electrolyte (I⁻/I₃⁻), which confirmed electrochemical processes in addition to a photovoltaic response. The high and unusual obtained Voc value was also attributed to the presence in the photoanode of active dipoles, the layer thickness, dye concentration, and the nature of the electrolyte. Full article

This article presents a hybrid fault diagnosis framework for DC–DC converters in photovoltaic (PV) systems, combining digital twin (DT) modelling and detection with machine learning anomaly classification. The proposed method addresses both hardware faults such as open and short circuits in insulated-gate bipolar transistors (IGBTs) and diodes and sensor-level false data injection attacks (FDIAs). A five-dimensional DT architecture is employed, where a virtual entity implemented using FMI-compliant FMUs interacts with a real-time emulated physical plant. Fault detection is performed by comparing the real-time system behaviour with DT predictions, using dynamic thresholds based on power, voltage, and current sensors errors. Once a discrepancy is flagged, a second step classifier processes normalized time-series windows to identify the specific fault type. Synthetic training data are generated using emulation models under normal and faulty conditions, and feature vectors are constructed using a compact, interpretable set of statistical and spectral descriptors. The model was validated using OPAL-RT Hardware in the Loop emulations. The results show high classification accuracy, robustness to environmental fluctuations, and transferability across system configurations. The framework also demonstrates compatibility with low-cost deployment hardware, confirming its practical applicability for fault diagnosis in real-world PV systems. Full article