Search Results (222)

Preeclampsia (PE) has traditionally been regarded as a pregnancy-limited hypertensive disorder; however, accumulating evidence increasingly positions it as a pivotal early-life vascular stress test that manifests underlying vulnerabilities and accelerates biological aging. Women with a history of PE exhibit a heightened susceptibility to premature-onset multi-systemic diseases, specifically cardiovascular, ovarian, renal, and metabolic decline. This suggests that PE acts as a catalyst for accelerated aging, driven by shared pathophysiological pathways that represent common mechanisms of systemic senescence. This review provides a comprehensive analysis of the epidemiological links and pathogenic drivers underpinning accelerated systemic aging following PE, with a specific focus on the cardiovascular-ovarian axis. Epidemiological data consistently demonstrate that women with prior PE exhibit significantly reduced anti-Müllerian hormone (AMH) levels, translating to an estimated 1.5-year acceleration in reproductive aging. In parallel, PE is associated with a twofold increase in lifetime cardiovascular disease (CVD) risk and the onset of chronic hypertension occurring an average of 7.7 years earlier. However, reconciling the phenotypic heterogeneity of PE and transcending the constraints of non-experimental designs are essential for firmly establishing this accelerated aging paradigm. At the molecular level, PE and ovarian aging converge on shared pathways—including mitochondrial dysfunction, oxidative stress, inflammation, and epigenetic dysregulation—collectively defining a distinct pathogenic ovarian–vascular aging axis. Proposed geroscience-based strategies advocate for refined risk stratification by incorporating molecular aging biomarkers—such as epigenetic clocks and inflammatory profiles—alongside conventional clinical indicators. This integrative framework facilitates the early identification of high-risk aging phenotypes, enabling targeted monitoring and timely interventions to preemptively modulate accelerated aging pathways. Pharmacological approaches within this framework emphasize the judicious repurposing of established agents, such as metformin, statins, and SGLT2 inhibitors, while emerging gerotherapeutics, including senolytics and senomorphics, provide a conceptual foundation for targeting the fundamental biological drivers of senescence. Although these geroprotective strategies, including the repurposing of established agents and the use of senolytics, offer innovative conceptual frameworks for targeting the fundamental drivers of senescence, they remain largely exploratory and require further clinical validation. Such strategies offer novel opportunities to shift the clinical focus from treating isolated comorbidities to modulating the shared molecular substrates of aging, ultimately promoting healthy aging and functional longevity in the elderly female population. Full article

To improve the low deposition rate of atomic layer deposition (ALD), we introduced filtered cathodic vacuum arc (FCVA) technology for the high-rate deposition of Al₂O₃ films. The FCVA-Al₂O₃ process achieved a deposition rate of 15 nm/min, which is approximately an order of magnitude higher than that of conventional ALD. This process does not involve hydrogen, preventing hydrogen ion penetration and thereby ensuring the high stability of the oxide TFT backplane. FCVA-Al₂O₃ films were integrated with inkjet-printed (IJP) organic layers to form a hybrid thin-film encapsulation (TFE) structure for OLEDs. The resulting laminated encapsulation exhibited excellent water vapor barrier properties (WVTR, Water Vapor Transmission Rate of 1.2 × 10⁻⁴ g/m²/day), demonstrating the great potential of FCVA for packaging high-throughput and high-performance flexible electronics. In addition to evaluating barrier properties (surface roughness, residual stress, and WVTR) to assess the suitability of TFE, the impact of FCVA technology was assessed via oxide thin-film transistor (TFT) electrical performance and OLED device reliability tests. The electrical properties of oxide TFTs show no significant degradation post-encapsulation, while OLED performance, despite a slight increase in current efficiency, remains effectively unchanged. Additionally, the lifetime of OLED devices reached 300 h under accelerated aging conditions (85 °C, 85% relative humidity), which is nearly twice that of devices without FCVA-Al₂O₃ encapsulation. Full article

A vibration energy harvester (VEH) based on the principle of variable magnetic reluctance has been developed to enable wireless and maintenance-free power supply for condition monitoring sensors in vibrating machinery. Conventional battery or wired solutions are often impractical due to limited lifetime and high installation costs, motivating the use of vibration-based energy harvesting. The proposed VEH converts mechanical vibrations into electrical energy through the relative motion of a movable ferromagnetic core within a magnetic circuit. Unlike conventional VEH designs, where the magnet is the moving element, this concept utilizes a movable ferromagnetic core in combination with a stationary pole piece for voltage induction. This configuration enables a compact and easily adjustable proof mass, as neither the coil nor the magnet needs to be moved. The VEH is designed to operate effectively under excitation frequencies between $16 \mathrm{H}\mathrm{z}$ and $50 \mathrm{H}\mathrm{z}$ and acceleration levels from $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $1 \mathrm{g}$) up to $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ (equivalent to $10 \mathrm{g}$). To ensure a reliable power supply, the VEH must deliver a minimum electrical output of $0.1 \mathrm{m}\mathrm{W}$ at the lowest excitation ($1 \mathrm{g}$) while maintaining structural integrity. Additionally, the maximum permissible displacement amplitude of the movable core is limited to $1.15 \mathrm{m}\mathrm{m}$ to avoid mechanical damage and ensure durability over long-term operation. Coupled magnetic-transient and mechanical finite element method (FEM) simulations were conducted to analyze the system’s dynamic behavior and electrical power output across varying excitation frequencies and accelerations. A laboratory prototype was developed and tested under controlled vibration conditions to validate the simulation results. The experimental measurements confirm that the VEH achieves an electrical output of $0.166 \mathrm{m}\mathrm{W}$ at $9.81 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ and $16 \mathrm{H}\mathrm{z}$, while maintaining the maximum allowable displacement amplitude of $1.15 \mathrm{m}\mathrm{m}$, even at $98.1 {\displaystyle \frac{\mathrm{m}}{{\mathrm{s}}^2}}$ ($10 \mathrm{g}$) and $50 \mathrm{H}\mathrm{z}$. The strong agreement between simulation and experimental data demonstrates the reliability of the coupled FEM approach. Overall, the proposed VEH design meets the defined performance targets and provides a robust solution for powering wireless sensor systems under a wide range of vibration conditions. Full article

Standalone photovoltaic systems play an important role in providing reliable renewable energy in remote areas. These systems depend heavily on battery energy storage, especially lithium iron phosphate batteries, which are known for their safety and long cycle life. However, battery degradation remains a major challenge, as high charging currents, temperature variations, and wide state-of-charge fluctuations introduce electro-thermal stress that reduces the useful lifetime of the storage system. To address this issue, this paper presents a Flexible Power Point Tracking (FPPT) strategy supported by a fuzzy-logic-based controller. In this context, battery stress refers to the combined electrochemical and thermal stress induced by high charging currents, elevated operating temperatures, and large state-of-charge (SOC) excursions, which are known to accelerate ageing mechanisms and capacity fade. Based on a review of the existing literature, most FPPT and lifetime-oriented control studies have focused on lithium-ion batteries such as NMC or LCO chemistries, while limited attention has been given to lithium iron phosphate (LiFePO₄) batteries. The goal is to limit battery stress by reducing current peaks, mitigating temperature rise, and smoothing state-of-charge variations, thereby improving battery lifetime without compromising the stability of the standalone PV system. A complete PV–battery model is developed in PLECS and tested using one-year irradiance, temperature, and load data from Perth, Australia. The results show that the FPPT–Fuzzy controller reduces current peaks, stabilises the state of charge, and lowers the thermal impact on the battery when compared with traditional MPPT. As a result, overall degradation decreases and the battery lifetime is extended by approximately 7%. These findings demonstrate that FPPT is a promising method for improving the long-term performance of renewable energy systems based on lithium iron phosphate battery storage. Full article

It goes without saying that when studying the corrosion behaviour of a component or structure, the experimental conditions should reflect the service environment to which the object will be exposed. However, all too frequently, “accelerated” conditions are used, involving applied potentials, elevated temperature, high solute concentrations, excessive strain or strain rates, etc., which complicates the prediction of the in-service behaviour or component lifetime. At best, it is necessary to extrapolate the results of these accelerated laboratory measurements to more realistic conditions, ideally based on a mechanistic understanding of the processes involved. At worst, accelerated laboratory tests may suggest corrosion processes that are not feasible or relevant to the service environment, potentially disqualifying a given material or design from consideration that would otherwise provide acceptable behaviour in service. Examples of the need to properly take into account the service environment and the potential negative consequences of accelerated testing are given for the case of the corrosion behaviour of nuclear waste container materials. For example, the use of bulk solutions to study the corrosion of copper by sulfide in the laboratory involves high sulfide fluxes and leads to localized corrosion and stress corrosion cracking mechanisms that are not possible under actual repository conditions. Similarly, accelerating the effects of γ-irradiation using high absorbed dose rates runs the risk of changing the mechanism of radiation-induced corrosion. Above all, it is imperative to develop a sound mechanistic understanding of the underlying corrosion mechanisms in order to confidently apply the results of short-term laboratory observations to the prediction of the long-term performance of nuclear waste containers. Full article

Poly(3,4-ethylenedioxythiophene)–polystyrene sulfonate (PEDOT:PSS) is widely used to fabricate conductive organic coatings for electrodes in electrophysiology. As these devices move toward clinical translation, establishing sterilization methods that preserve their functional properties is essential. Ethylene oxide (EtO) is routinely used for sterilizing heat- and moisture-sensitive medical devices due to its high penetration efficiency and low thermal load. However, the absence of systematic studies evaluating its impact on PEDOT:PSS raises concerns about the compatibility of EtO sterilization with organic electrophysiology interfaces. Here, we report the first comprehensive evaluation of EtO sterilization on PEDOT:PSS electrodes electrochemically deposited onto cortical interfaces designed for intraoperative monitoring and stimulation. EtO exposure induced only minimal changes in surface topography, with no detectable alteration of the electrical or electrochemical performance of the electrodes. Impedance spectroscopy, cyclic voltammetry, and charge-injection capacity measurements all revealed that EtO-treated electrodes retained properties comparable to untreated controls. Moreover, EtO-sterilized PEDOT:PSS coatings demonstrated robust long-term stability under accelerated lifetime testing, exhibiting negligible degradation over extended operation. These findings demonstrate that EtO sterilization is fully compatible with PEDOT:PSS-based bioelectronic interfaces and constitutes a viable pathway toward their safe and effective integration into clinical electrophysiology. This work represents an important step toward translating organic conducting polymer technologies into real-world biomedical applications. Full article

Driven by the dual imperatives of global energy transition and sustainable development goals, lithium-ion batteries, as critical energy storage carriers, have seen the assessment of their lifecycle reliability and durability become a core issue underpinning the sustainable operation of clean energy systems. Grounded in a multidimensional perspective of sustainable development, this study aims to establish a quantifiable and monitorable battery reliability evaluation framework to address the challenges to lifespan and performance sustainability faced by batteries under complex multi-stress coupled operating conditions. Lithium-ion batteries are widely used across various fields, making an accurate assessment of their reliability crucial. In this study, to evaluate the lifespan and reliability of lithium-ion batteries when operating in various coupling stress environments, a multi-stress collaborative accelerated model(MCAM) considering interaction is established. The model takes into account the principal stress effects and the interaction effects. The former is developed based on traditional acceleration models (such as the Arrhenius model), while the latter is constructed through the combination of exponential, power, and logarithmic functions. This study firstly considers the scale parameter of the Weibull distribution as an acceleration effect, and the relationship between characteristic life and stresses is explored through the synergistic action of primary and interaction effects. Subsequently, a multi-stress maximum likelihood estimation method that considers interaction effects is formulated, and the model parameters are estimated using the gradient descent algorithm. Finally, the validity of the proposed model is demonstrated through simulation, and numerical examples on lithium-ion batteries demonstrate that accurate lifetime prediction is enabled by the MCAM, with test duration, cost, and resource consumption significantly reduced. This study not only provides a scientific quantitative tool for advancing the sustainability assessment of battery systems, but also offers methodological support for relevant policy formulation, industry standard optimization, and full lifecycle management, thereby contributing to the synergistic development of energy storage technology across the economic, environmental, and social dimensions of sustainability. Full article

The increasing demand for reliable and efficient power electronic systems in critical applications—such as renewable energy, electric vehicles, and aerospace—has intensified the need to understand and predict failure mechanisms in power devices. This work focuses on the reliability assessment and lifetime modeling of medium-voltage power electronic components under realistic mission profiles. By combining accelerated aging tests, failure analysis, and physics-of-failure modeling, we identify dominant degradation mechanisms such as thermal cycling, partial discharge, and dielectric break-down. A hybrid methodology is proposed, integrating experimental data and simulation to predict the evolution of key parameters (e.g., on-state resistance, threshold voltage) over time. The study also explores the impact of packaging, thermal management, and environmental stresses on device robustness. The results provide valuable insights into the design of more durable power electronic converters and for the implementation of condition monitoring strategies in real-time applications. Full article

Efficiently predicting the lifespan of lithium iron phosphate (LFP) batteries early in their operational life is critical to accelerating the development of energy storage systems while reducing testing time, cost, and resource consumption. Traditional degradation models rely on full-cycle testing to estimate long-term performance, which is both time- and resource-intensive. This study proposes a novel semi-empirical degradation model that leverages a small fraction of early-cycle data with just 5% to accurately forecast full-lifetime performance with high accuracy, with less than 1.5% mean absolute percentage error. The model integrates fundamental degradation physics with data-driven calibration, using an improved musical chairs algorithm modified with chaotic map dynamics to optimize model parameters efficiently. Trained and validated on a diverse dataset of 27 LFP cells cycled under varying depths of discharge, current rates, and temperatures, the proposed method demonstrates superior convergence speed, robustness across LFP operating conditions, and predictive accuracy compared to traditional approaches. These results provide a scalable framework for rapid battery evaluation and deployment, supporting advances in electric mobility and grid-scale storage. Full article

Lithium iron phosphate (LFP) batteries are widely deployed in electric vehicles and large-scale energy storage systems due to their low cost, high safety, and excellent cycling stability. However, long-term operation introduces aging phenomena that critically limit performance and lifetime. In this study, multi-condition calendar and cycle aging tests were performed to elucidate the effects of storage state of charge (SOC), temperature, pressure, and cycling protocols on degradation. The results show that calendar aging is strongly governed by SOC and temperature, with higher SOC and elevated temperature accelerating capacity fade via enhanced SEI growth, while pressure exerts a negligible influence. Notably, under different SOC storage conditions, when the batteries are aged to the same state of health (SOH), the largest increase in Direct Current Resistance (DCR) is observed for the batteries stored at 50% SOC. For cycle aging, degradation is dominated by charging rate, SOC window, and temperature, whereas discharge rate and pressure have only minor effects. High-rate charging at low temperature induces lithium plating and rapid capacity loss, while wider or higher SOC cycling ranges significantly accelerate fade and stress accumulation. Overall, this work systematically identifies the key operational factors shaping LFP battery degradation, clarifies their underlying mechanisms, and provides theoretical guidance for optimizing usage strategies to enhance durability under complex operating conditions. Full article

This work investigates bias-temperature instability (BTI) in 1700 V 4H-SiC MOSFETs under realistic 1 MHz switching conditions with simultaneous gate and drain stress. Threshold-voltage measurements reveal that the degradation does not follow the classical Reaction–Diffusion behavior typically assumed for silicon devices. Instead, the power-law exponent n shows a clear increase at the largest negative gate bias (−10 V), indicating a field-driven trap-generation mechanism. Temperature-dependent stress tests further show a negative activation energy (−0.466 eV), consistent with degradation accelerating at lower temperatures due to suppressed detrapping. The results demonstrate that conventional silicon BTI models cannot be directly applied to SiC technologies and that fixed-n lifetime extrapolation leads to significant errors. A bias-dependent, field-driven framework for estimating time-to-failure is proposed, offering more accurate and practical reliability prediction for high-power SiC converter applications. Full article

Electrical insulation systems (EISs) are the principal reliability bottleneck of modern electrical machines (EMs). Among the many stresses acting on insulation, thermal stress is the most pervasive because it accelerates chemical reactions that progressively erode dielectric and mechanical integrity, ultimately dictating service life. As EMs migrate into compact, high-power-density platforms—automotive, aerospace, and industrial drives—designers need lifetime models that are not merely explanatory but actionable, linking operating temperatures and missions to quantified ageing and risk. This review article traces the evolution of thermal-ageing modelling from fundamental chemistry to a practical design tool. The historical empirical lineage of Arrhenius equation, Arrhenius–Dakin model, and Montsinger model is first revisited, clarifying their assumptions, parameter definitions, and the construction of thermal endurance curves. A discussion then follows on extensions that address deviations from first-order kinetics and demonstrate how variable temperature histories can be incorporated through cumulative damage formulations suitable for duty-cycle analysis. Since models are required to be anchored in data, accelerated thermal ageing (ATA) practices on representative specimens are outlined, alongside a description of the Weibull post-processing for deriving percentile lifetimes aligned with design targets. Building upon these foundations, the Physics-of-Failure (PoF) approach is introduced as a reliability-oriented design (ROD) methodology, in which validated lifetime models guide material selection and geometry optimisation while supporting prognostics and health management during operation. The emerging trend towards a hybrid PoF–AI approach is also discussed, which integrates artificial intelligence to identify nonlinear degradation patterns and drifting parameter relationships beyond the reach of empirical models, with physical constraints ensuring that predictions remain consistent with known ageing mechanisms. Such integration enables the learning process to adapt to operational variability and coupled stress effects, thereby improving both the accuracy and physical interpretability of lifetime estimation. The review aims to provide a concise view of models, tests, and workflows that convert thermal-ageing knowledge into robust, design-time decisions. By linking empirical and physics-based insights with modern data-driven learning, these developments support proactive maintenance, sustainable asset management, and extended operational lifetimes for next-generation EMs. Full article

The capacitor is a key component in power electronic transmission systems. The decrease in capacitance and increase in equivalent series resistance (ESR) serve as critical parameters for characterizing the aging state of capacitors. To address this, this paper proposes a convolutional neural network-long short-term memory (CNN-LSTM) model for predicting the aging state and remaining useful life (RUL) of capacitors. First, the parameter dispersion characteristics of capacitance change rate and ESR are analyzed. A CNN-LSTM hybrid model is constructed, along with a prediction framework for aging state evaluation and RUL estimation. Second, an accelerated aging test platform for aluminum electrolytic capacitors is built, and eight sets of capacitor aging experiments are conducted. Finally, the effectiveness of the proposed method is validated. Comparative results show that the CNN-LSTM model achieves higher accuracy in aging parameter evaluation compared to the traditional LSTM model and yields smaller errors in RUL prediction than the conventional Arrhenius lifetime model. Full article

The aluminum casting alloy AlSi7Mg0.6 (A357) is extensively used in the automotive industry due to its favorable balance of mechanical properties, castability, lightweight characteristics, and corrosion resistance. Castings made from this alloy are often subjected to harsh service environments, where surface degradation and microstructural variability can significantly impact fatigue performance. This study investigates the combined effects of surface corrosion damage and higher Fe content on the fatigue life of the AlSi7Mg0.6 alloy, using a rotating bending fatigue test under simultaneous corrosion exposure in a 3.5 wt. % NaCl solution. The effect of corrosion and Fe content on fatigue life was then investigated and analyzed using Wöhler curves and scanning electron microscopy (SEM). The results demonstrate that the corrosion-fatigue interaction accelerated the kinetics of the fatigue process, while the fracture mechanism and crack initiation places are not fundamentally altered compared to alloys in the state without corrosion damage. A comparison of the fatigue lifetime of samples in an air environment and a corrosive environment shows that the corrosive environment (3.5% NaCl) reduces the fatigue lifetime of alloys without T6 by an average of 7.5 MPa and alloys after T6 by 6 MPa. The results are probably due to the penetration of chloride ions into casting defects located on the surface of the samples. Surface pits formed during corrosion act as stress concentrators, increasing the likelihood of stress-induced failure. Microstructural feature morphology, especially Fe-rich intermetallic phases, influences crack propagation mechanisms. Full article

Uncertainty from heterogeneous degradation paths, limited experimental samples, and exogenous perturbations often complicates accelerated lifetime modeling and prediction. To confront these intertwined challenges, a Wiener process-based robust framework is developed. The proposed approach incorporates random-effect structures to capture unit-to-unit variability, adopts interval-based inference to reflect sampling limitations, and employs a hybrid estimator, combining Huber-type loss with a Metropolis–Hastings algorithm, to suppress the influence of external disturbances. In addition, a quantitative stress–parameter linkage is established under the accelerated factor principle, supporting consistent transfer from accelerated testing to normal conditions. Validation on contact stress relaxation data of connectors confirms that this methodology achieves more stable parameter inference and improves the reliability of lifetime predictions. Full article