Search Results (4,573)

The Zeta-Minimizer Theorem provides a fully deductive variational foundation for the hydrogen oxidation reaction (HOR) and oxygen reduction reaction (ORR) in proton exchange membrane fuel cells. Starting from three primitive thermodynamic axioms and the helical geometry of the phase functional, a multi-extent dynamical system is constructed that simultaneously treats the electrochemical reaction coordinates and the adsorption extents of the participating species at the solid–electrolyte interface. The combined Hessian of the phase functional yields a complete spectrum of relaxation rates whose eigenvalues and eigenvectors emerge directly from the solid blackbox constants C_k and the helical partition functions of the reactive species. Adiabatic elimination of the fast surface modes produces an effective single-extent description in which voltage (or overpotential) appears as the conjugate variable, exactly analogous to the role of pressure in the corresponding gas-phase ammonia synthesis framework. The resulting nonlinear rate law is thermodynamically consistent at all conditions, recovers the Butler–Volmer and Tafel forms as well-defined limiting cases, and incorporates the effects of temperature, dilution, and catalyst-specific interface constants without empirical activation energies or adjustable reaction orders. The framework therefore unifies equilibrium, kinetics, and modal dynamics of HOR and ORR within a single variational structure, offering a parameter-light, first-principles alternative to classical empirical electrocatalytic rate expressions while preserving transparent contact with established limiting laws. Full article

Effective management of wastewater treatment plants often requires real-time measurements of Biochemical Oxygen Demand (BOD). Conventional methods for determining Biochemical Oxygen Demand (BOD) are often time-consuming, labor-intensive and prone to inaccuracies. Microbial Fuel Cells (MFCs) have emerged as a viable alternative technology for BOD measurement, offering real-time monitoring capability. However, challenges remain in its validity for testing different types of wastewater. This study developed a cost-effective dual-chamber MFC with graphite felt electrodes and a CMI-7000 membrane, inoculated with a microbial consortia grown from anaerobic sludge at optimal conditions (35 °C, pH 7, 1000 Ω external resistance). After one month of biofilm formation, the MFC produced 600 mV. Voltage outputs were measured at six BOD₅ concentrations (36 to 583 mg/L) in synthetic wastewater, showing a strong linear correlation between BOD₅ concentrations and voltage outputs. The MFC was also tested with five domestic wastewater samples with BOD₅ values ranging between 81 and 405 mg/L. The output voltages were inserted into the derived voltage–BOD correlation to obtain BOD₅ values within 2.5% to 11% of conventional laboratory results. These findings confirm the potential of MFC-based biosensors as an efficient and accurate tool for real-time wastewater monitoring. Full article

Low-damage plasma processing of atomically thin $\mathrm{M}\mathrm{o}{\mathrm{S}}_2$ requires simultaneous control of ion species, energy, and incident angle, yet the discharge mechanisms governing $\mathrm{A}\mathrm{r}/{\mathrm{O}}_2$ plasma and their connection to surface damage remain insufficiently understood. Here, we investigate the effect of the $\mathrm{A}\mathrm{r}/{\mathrm{O}}_2$ mixing ratio on the spatial distributions of charged particles, the plasma potential, and the substrate-incident ion energy and angular distributions in a low-voltage, single-frequency capacitively coupled plasma using a two-dimensional particle-in-cell Monte Carlo collision (PIC-MCC) simulation. At a fixed pressure of 50 mTorr with the $\mathrm{A}\mathrm{r}/{\mathrm{O}}_2$ ratio varied from 9:1 to 2:8, the ion energy and angular distributions were collected at the center and edge of the powered electrode. Increasing the oxygen fraction reduced the electron density while enhancing the ${\mathrm{O}}^{-}$ density and electronegativity, driving an electropositive-to-electronegative transition near 8:2, and shifted the dominant positive ion from $\mathrm{A}{\mathrm{r}}^{+}$ to ${\mathrm{O}}_2^{+}$, with ${\mathrm{O}}^{+}$ remaining minor owing to charge-exchange loss. The plasma potential and ion-energy peaks generally increased with the oxygen fraction but showed nonmonotonic dependence, while radial edge fields tilted and broadened the angular distributions. To link the ion and oxygen-radical fluxes to $\mathrm{M}\mathrm{o}{\mathrm{S}}_2$ processing without assuming uncertain surface-response coefficients, we interpreted the ion and oxygen-radical fluxes through a phenomenological two-channel surface-reaction scheme, introducing a damaging ion fraction and a radical-to-ion flux ratio. Their opposing trends reveal an intrinsic trade-off, indicating that an intermediate ${\mathrm{O}}_2$ fraction offers a more favorable low-damage window than either $\mathrm{A}\mathrm{r}$-rich or strongly oxygen-rich conditions. Full article

Accurate state-of-charge (SOC) estimation is essential for reliable operation of vanadium redox flow batteries (VRFBs), yet many model-based methods treat the open-circuit-voltage (OCV)-SOC relationship as a fixed calibration curve. This study experimentally investigates condition-dependent SOC-OCV behavior using a laboratory-scale VRFB equipped with a bypass OCV cell. The bypass OCV method was validated against an intermittent discharge–rest method, with OCV differences below 5 mV. SOC-OCV characteristics were then examined under different electrolyte flow rates, cycling histories, electrolyte/component refreshing conditions, and a dynamic stress test profile. The results show that the SOC-OCV curve varies with cycling history and flow rate, while electrolyte/component refreshing and dynamic operation further modify the measured OCV response. Fixed-curve-based SOC inversion confirms that calibration mismatch can introduce substantial SOC estimation errors, with a case-specific maximum error of 11.36% observed when the initial post-preconditioning constant-current curve was applied to the cell after 50 cycles under the tested 96 mL min⁻¹ DST condition. These findings highlight the need for adaptive OCV correction and condition-dependent SOC-OCV mapping in practical VRFB SOC estimation. Full article

The developed spectrally selective open-circuit voltage decay (OCVD) system uses pulsed LEDs to probe carrier recombination at varying depths in monocrystalline and multicrystalline silicon solar cells. Full-size c-Si cells exhibit a 15 ms voltage decay under broad-spectrum illumination, while 2 × 2 cm² mini-cells with a 5 mm LED spot reveal localized decays around 300 μs. Low-temperature annealing at 115 °C for 3 min enhances surface passivation, shown by increased decay times under blue (458 nm) excitation. In contrast, infrared (864 nm) excitation indicates minimal improvement in bulk carrier lifetime. This technique effectively differentiates surface from bulk recombination, providing valuable insights for optimizing passivation strategies in silicon solar cells. Full article

Second-life electric vehicle (EV) batteries are increasingly recognized as valuable resources for stationary energy storage. However, the heterogeneous degradation of retired batteries makes reliable and application-oriented reuse decisions challenging. Existing studies primarily focus on battery health estimation or degradation characterization, while limited attention has been given to systematically translating experimentally measured health indicators into practical second-life deployment decisions. To address this gap, this study proposes an experimental multi-metric battery health assessment and decision-support framework for application-oriented screening and reuse pathway allocation of retired EV batteries. A total of 91 s life lithium-ion battery cells were experimentally characterized through standardized laboratory charge–discharge testing. Multiple complementary health indicators, including State of Health (SoH), discharge capacity, round-trip energy efficiency, and voltage–current time-series characteristics, were extracted and statistically analyzed to evaluate residual battery performance and degradation behavior. The experimental results reveal substantial variability among retired batteries, with SoH values ranging from approximately 22% to 96%, while more than half of the tested cells exhibit SoH below 60%. Furthermore, batteries with comparable SoH frequently demonstrate different energy efficiencies, indicating that capacity retention alone is insufficient for reliable second-life battery assessment. Building upon these findings, a transparent rule-based decision-support framework is developed to map experimentally measured battery health indicators to application-oriented reuse pathways, including grid-support systems, residential energy storage, backup applications, and recycling. The proposed framework establishes a practical bridge between laboratory battery characterization and deployment-oriented second-life decision-making, providing an interpretable and experimentally grounded methodology for scalable battery screening and sustainable reuse planning. Full article

This paper presents a microwave sensing platform based on a 32-element dipole array designed for near-field dielectric contrast mapping. The sensor utilizes an $8\times 8$ tightly coupled dipole array (TCDA) topology, where pairs of dipoles form unit cells that exploit electromagnetic coupling variations. A 32-way equal power divider network ensures uniform excitation across the aperture. Operating at 830 MHz, the dipole array exhibits high absorption (>90%), which enhances near-field intensity and sensitivity to surface perturbations. Experimental validation with dielectric samples, saline liquids of varying concentrations (${ϵ}_r\approx$ 70–78), and biological tissues demonstrates the array’s capability to map spatial variations in electromagnetic properties through rectified DC voltage shifts. When compared to a state-of-the-art multi-port Vector Network Analyzer (VNA) configurations, the proposed architecture offers a robust, low-complexity, proof-of-concept alternative by eliminating complex RF routing networks and multi-port switches. Full article

Precise prediction of voltage degradation is critical for the prognostics and health management of proton exchange membrane fuel cells (PEMFCs). The performance degradation of PEMFCs is governed by a complex coupling of multiple physicochemical mechanisms, including catalyst layer and proton exchange membrane degradation. Crucially, these internal degradation processes evolve across highly heterogeneous time scales, ranging from transient high-frequency fluctuations to low-frequency and long-term irreversible performance fade. Conventional predictive models, which typically rely on single-scale architectures or fixed receptive fields, are inherently ill-equipped to simultaneously decouple and capture these cross-scale temporal dynamics. To tackle this challenge, this paper innovatively proposes a multi-scale deep learning framework that integrates a multi-scale degradation trend perception module, a long short-term memory (LSTM)-based encoder–decoder architecture, and a multi-head attention mechanism. One-dimensional convolutional layers with different kernel sizes are employed to simultaneously extract local temporal features at multiple granularities, followed by the LSTM encoder–decoder to model long-range temporal dependencies, while the cross-attention mechanism dynamically allocates attention across the encoded context at each autoregressive decoding step. Experimental outcomes indicate that the proposed model realizes excellent predictive accuracy across five evaluation indices in comparison with standard baselines. In particular, the mean absolute percentage error (MAPE) reaches 1.6696%, and the maximum absolute percentage error (Max-APE) is strictly bounded within 5%, substantiating the reliability of the proposed framework for high precision and long-horizon health prognostics for PEMFCs. Full article

High renewable penetration and large-scale green hydrogen production are accelerating the formation of the new-type power system (NTPS), in which electrical dispatch, electrolysis, hydrogen storage, fuel-cell reconversion, and flexible demand must be coordinated under nonlinear network physics and uncertain renewable, load, and hydrogen-demand trajectories. This study develops a physics-informed distributionally robust multi-agent reinforcement learning (PI-DRO-MARL) framework for coordinated NTPS operation with integrated electricity–hydrogen coupling. The operational objective is to minimize worst-case expected operating cost, including generation and grid-exchange cost, electrolysis and hydrogen-delivery cost, storage degradation, renewable curtailment, and load- or hydrogen-shedding penalties, while satisfying AC power-flow balance, voltage limits, line-loading limits, ramping limits, battery state-of-charge constraints, hydrogen-storage dynamics, and electrolysis/fuel-cell conversion constraints. The framework embeds physics-informed residuals and projection operators into a centralized-training decentralized-execution architecture; represents renewable, electrical-load, hydrogen-demand, and price uncertainty through statistically calibrated Wasserstein ambiguity sets; and trains agents with robust value estimation and feasibility-aware action correction. Validation is conducted on a modified IEEE 33-bus distribution network coupled with a 12-node hydrogen system, with additional scalability checks on modified IEEE 69-bus and IEEE 123-node reference systems. Across ten random seeds, the primary case shows an operating cost of USD 8850 with a 95% confidence interval of USD 8770–8940, a mean constraint-violation rate of 0.37%, and a shifted-scenario cost increase of 12.6%, outperforming deterministic optimization, stochastic programming, standard reinforcement learning (RL), proximal policy optimization (PPO), soft actor–critic (SAC), multi-agent deep deterministic policy gradient (MADDPG), constrained RL, safe RL, and robust RL baselines. Ablation, Wasserstein-radius, time-step, and stress-test analyses further show that distributional robustness, physics-informed projection, and multi-agent coordination provide distinct and complementary benefits. The results support PI-DRO-MARL as a simulation-validated architecture for real-time, uncertainty-aware NTPS dispatch, while field deployment still requires digital-twin calibration, hardware-in-the-loop testing, and site-specific operational validation. Full article

The dynamics and transient response of fuel cell systems are critical aspects, especially in commercial applications where an immediate response to fluctuating power demands is required. This study presents experimental results obtained from evaluating the dynamic behavior of a 1.2 kW Ballard Nexa fuel cell, subjected to various operational disturbances, including startups, shutdowns, step load increases, irregular and constant loading, and system purging operations. The variables analyzed include voltage, current, and temperature, both in individual cells and in the entire system. The results indicate that the temperature exhibits an attenuated response with an arc-like evolution, but with an upward trend correlated with the increase in the demanded current. Meanwhile, when multiple load steps are applied, the system exhibits rapid responses in both cell and stack voltages, with transient overshoot and undershoot peaks whose magnitudes increase proportionally with the applied current. Regarding the purge system, tests show that its activation improves operational efficiency, resulting in approximately 0.3 V of voltage increase per operation. Furthermore, it is observed that the purge frequency increases with higher external load levels, suggesting a direct interaction between energy demand and active waste gas management in the system. Full article

Cell balancing is a critical requirement for ensuring the safety and longevity of supercapacitor (SC)-based energy storage systems (ESSs). While conventional series-parallel active balancing methods predominantly utilize a fixed 50% duty ratio, this static control mechanism fundamentally limits the energy transfer rate, particularly under severe initial voltage imbalances and non-ideal cell variations. To accelerate the equalization process without incurring the cost, volume, and complexity penalties of hardware modifications, this paper proposes a dynamic duty modulation (DDM) technique. The proposed DDM algorithm adaptively adjusts the switching duty ratios in real-time based on the instantaneous voltage deviations of individual cells. A discrete-time mathematical model is derived to evaluate the convergence behavior, analytically proving that the dynamic approach requires significantly fewer switching cycles than the conventional fixed-duty method. The mathematical superiority and practical feasibility of the proposed technique are strictly validated through comprehensive PSIM simulations. Quantitative simulation results confirm that the proposed DDM reduced the balancing time by over 48% and proportionally decreased the total number of switching cycles compared to the conventional method, providing an enhanced dynamic performance suitable for advanced battery management systems (BMSs). Full article

High lithium and manganese oxides (HLMs), also known as lithium- and manganese-rich oxides (LMR), are an alternative to the state-of-the-art (SoA) cathode materials for Li-ion battery cells due to their high specific capacity, working potential, and potential elimination of cobalt from their composition. Nevertheless, they are claimed to undergo accelerated capacity and potential fade. In this work, an extensive electrochemical characterization is conducted while revisiting the most relevant literature on HLM. The classical galvanostatic cycling is used to conduct differential voltage and incremental capacity analyses, while impedance spectroscopy and galvanostatic intermittent titration techniques are applied to complement this test. The results are complemented with online electrochemical mass spectrometry and postmortem characterization. Loss of anode active material is identified as the main degradation mechanism, aggravated by potential slippage. Moreover, the hypotheses on degradation mechanisms are further confirmed by changing the voltage cutoffs of the cells, limiting the Li₂MnO₃ activation. The results are benchmarked with SoA LiNi_0.8Mn_0.1Co_0.1O₂-based cells with a promising balance for HLM in some cases. This work serves as a guide to assist in the interpretation (and avoid misinterpretation) of the results with Li-ion batteries consisting of HLM electrodes. Full article

Electroporation outcomes are governed by the local electric field distribution and transmembrane voltage, both of which may be altered by nanoscale elements positioned near the cell membrane. In this study, we developed a two-dimensional finite-element electromagnetic model to investigate the effect of a membrane-proximal silica-coated superparamagnetic iron oxide nanoparticle cluster during a trapezoidal electroporation pulse. The model couples electric and magnetic field components with a membrane electroporation formulation based on Smoluchowski-type pore-density dynamics. Simulations were performed with and without a nanoparticle positioned 5 nm from the membrane, considering different cytosol and extracellular medium conductivities. The results show that the nanoparticle induces a highly localized perturbation of the electric field, whose magnitude depends on the sampling region and conductivity contrast. Transmembrane voltage is modestly and transiently modulated during pulse rise time, whereas the effect is limited during the pulse plateau. Pore-density analysis further indicates that the nanoparticle does not induce a generalized increase in electroporation-related parameters and may locally reduce pore density near the nanoparticle–membrane interface. Overall, the model identifies transient and conductivity-dependent nanoscale field redistribution caused by membrane-proximal silica-coated magnetic nanoparticles, while highlighting the need for three-dimensional modeling and experimental validation before inferring electroporation enhancement. Full article

The growing deployment of electric vehicles requires battery management systems that maintain cell uniformity while reducing hardware complexity and improving energy efficiency. Many cell-balancing methods rely on converter-based architectures and remain validated only through simulation. This study experimentally validates a previously published adaptive recombination strategy using a prototype with second-life Panasonic NCR18650PF lithium-ion cells. The system employs dynamic series-parallel reconfiguration, relay-based switching, isolated voltage monitoring, and adaptive control to redistribute energy without dedicated balancing converters. Six test cases were evaluated under resting, charging, and discharging conditions using simultaneous and sequential schemes. Complete balancing was achieved in all test cases within the measurement resolution of the prototype. The experiments reproduced the main balancing mechanisms predicted by simulation, particularly under resting and discharging conditions, while also revealing practical deviations under charging operation. These deviations indicate that real current-sharing behavior, cell aging, contact resistance, wiring losses, and measurement constraints can influence recombination performance in ways not fully captured by ideal simulation models. The study therefore provides first-stage hardware evidence for the feasibility of adaptive recombination balancing and identifies key implementation requirements for future real-time, safety-rated, and scalable BMS development. This research contributes to SDG 7 by supporting improved lithium-ion battery utilization and energy efficiency for sustainable electric mobility. Full article

To address the bottleneck that traditional building blinds struggle with, namely synergistically achieve shading control and energy recovery, a vertical mid-pane photovoltaic (PV) louver based on a self-powered feedback mechanism was designed. This system utilizes the voltage difference generated by differential light exposure on photovoltaic thin-film cells to drive a motor, realizing zero-energy automatic tracking of the solar azimuth and dynamic adjustment of component angles. By establishing a mathematical model for sunlight-tracking power generation and combining it with COMSOL multiphysics simulation, the coupling effects of the PV louver angle, operating conditions, and solar terms on photoelectric performance were thoroughly analyzed. The research results indicate that when the PV louver angle increases from 60° to 150°, the power generation significantly improves by 80%. Compared with the non-tracking mode, the all-day power generation efficiency gain of the vertical tracking center-mounted PV louver can reach up to 19.68%. Driven by the seasonal evolution of the solar elevation angle, the direct radiation irradiance during the tracking period across four typical solar terms exhibits a distribution pattern characterized as “higher in winter, lower in summer, and intermediate in spring and autumn.” These findings provide a technical pathway integrating dynamic shading, passive photothermal regulation, and clean power generation for south-facing facades in hot summer and cold winter zones, offering significant reference value for enhancing the energy autonomy and low-carbon level of building envelopes. Full article