The ionomer content in the catalyst layer is a crucial design factor that affects the performance of polymer electrolyte membrane fuel cells (PEMFCs). However, the optimal ionomer content can vary based on the surrounding humidity levels. This study systematically evaluated the influence of the ionomer-to-carbon (I/C) ratio (0.00, 0.55, and 0.91) on PEMFC performance under fully humidified (RH 100%) and low-humidity (RH 25%) conditions. Membrane-electrode assemblies (MEAs) were fabricated using a spray coating technique, and their electrochemical properties were analyzed through polarization curves and electrochemical impedance spectroscopy (EIS). Under RH 100%, the MEA with an I/C ratio of 0.55 achieved the highest peak power density of 519.8 mW/cm², indicating a successful balance between proton conductivity and gas transport. Conversely, under RH 25%, the best performance of 203.9 mW/cm² was observed at an I/C ratio of 0.91. This shift is attributed to improved water retention at higher ionomer content, which reduced membrane dehydration and lowered both ohmic and Faradaic resistances. These findings highlight the dual role of the ionomer in facilitating proton transport and managing water balance, emphasizing the necessity of optimizing the I/C ratio according to operating conditions for stable and high-performing PEMFC operation.

A Transformer model to predict the remaining useful life of a fuel cell, which has demonstrated superior performance in analyzing time series data. The dataset was created from long-term performance evaluation experiments conducted in rated power mode, with measurements taken every 10 hours. We preprocessed the raw data using a moving average, allocating 70% for training and 30% for evaluation. The model's performance, evaluated through MAE, MSE, and MAPE, was excellent. The fuel cell's critical voltage, defined as 94.5% of its initial voltage, was measured at 0.719 V. During the experimental run, the actual critical time was 106.6 hours, while the model predicted 106.8 hours, resulting in a 0.19% error. Since the predictions were based on data collected up to 93 hours, the estimated remaining life was 13.8 hours.

Coherent Beam Combining (CBC) is a promising technique for enhancing laser output power by accurately aligning the phase and position of multiple laser beams. The Stochastic Parallel Gradient Descent (SPGD) algorithm is commonly used in CBC systems due to its simplicity and scalability. However, its dependence on fixed control parameters can result in slow convergence rates and diminished control stability. To overcome these challenges, this study introduces an adaptive SPGD algorithm that dynamically adjusts the perturbation amplitude and learning rate based on the real-time value of the objective function. This approach accelerates convergence during the initial stages by increasing control inputs when the objective function value is low, while ensuring stability as the function nears its maximum in later stages. Numerical simulations of 7-channel and 19-channel CBC systems revealed that the adaptive SPGD algorithm reduced average iteration counts by 26.4% and 18.1%, respectively, compared to the basic SPGD. Furthermore, the overall control performance improved, achieving high beam combining efficiency with reduced total computation time. This proposed algorithm serves as a straightforward yet effective enhancement to the conventional SPGD method, improving both convergence speed and stability.

A yttria-stabilized zirconia (YSZ) cathode functional layer (CFL) was fabricated using a co-sputtering process to improve the oxygen reduction reaction (ORR) in solid oxide fuel cells (SOFCs). To optimize the yttria molar percentage and achieve a nano-granular structure with enhanced grain boundary density, the DC sputtering power for the metallic yttrium target was varied at 10, 30, and 50 W. Structural and compositional analyses were performed using scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), atomic force microscopy (AFM), and X-ray diffraction (XRD). The results indicated that a DC power of 30 W resulted in a well-developed grain structure with high grain boundary density and an yttria composition close to the optimal molar percentage of 8-10 mol %. Under these optimized conditions, the SOFC with the co-sputtered YSZ CFL achieved a maximum power output of 9.22 mW/cm² at 450^oC, representing approximately a 43% enhancement compared to the reference cell. This highlights the significant potential of co-sputtering for future low-temperature SOFC applications.

Atomic Layer Deposition (ALD) has emerged as a promising technique for fabricating thin films that enhance the performance of solid oxide fuel cells and solid oxide electrolysis cells. ALD allows for precise control over film thickness and composition at the atomic level, resulting in uniform and dense thin films. These characteristics enable the deposition of thin, homogeneous layers of various materials onto the porous electrode surfaces of solid oxide cells, thereby increasing electrochemical activity and reducing activation losses. Additionally, thin-film electrolytes produced through ALD can achieve high ionic conductivity and low ohmic losses, facilitating a reduction in the operating temperature of solid oxide cells. This review summarizes recent research trends in applying ALD technology to the fuel electrode, air electrode, and electrolyte of solid oxide cells and discusses design strategies aimed at improving efficiency and long-term stability.

Solid Oxide Fuel Cells (SOFCs) are energy conversion devices known for their significantly higher power density compared to other fuel cell types. However, their high operating temperatures pose challenges related to thermal stability. To address this, research is focusing on Low-Temperature SOFCs (LT-SOFCs), which function at lower temperatures and exhibit enhanced electrochemical performance. While various electrode materials are utilized in SOFCs, platinum (Pt) stands out for its excellent electronic conductivity and catalytic activity. Unfortunately, at the operating temperatures of SOFCs, Pt tends to agglomerate, leading to a rapid reduction in the triple phase boundary (TPB) and a subsequent decline in electrochemical reactions. In this study, LT-SOFCs were fabricated with a Praseodymium Oxide (PrOx) capping layer applied to a porous Pt cathode using sputtering, with various thicknesses achieved by adjusting the deposition time. The electrochemical performance of the LT-SOFCs was measured at 500^oC. Additionally, the degradation behavior of the LT-SOFCs was assessed by applying a constant voltage of 0.5 V for 48 hours. Scanning Electron Microscopy (SEM) analysis was also conducted on the PrOx capping layer thin films under the same operating conditions.

This study presents a performance evaluation platform for sputtered thin-film electrodes used in biogas-driven, low-temperature solid oxide fuel cells (SOFCs). The design considerations include electrolyte material composition and thickness, anode material composition and thickness, anode fuel composition, and cathode composition and thickness, all derived from a review of existing literature. For the electrolyte, we propose a thickness of 100 μm for the main electrolyte made of gadolinium-doped ceria (GDC) and 0.1 μm for the auxiliary electrolyte made of scandia-stabilized zirconia. In terms of anode fabrication, we suggest a material composition of Ru/Ni-Cu-GDC, with thicknesses of 1 μm for Ni-Cu-GDC and a few nanometers for Ru in the nanoporous anode. For the anode fuel supply, we recommend mole ratios of 45% to 75% CH₄ and 25% to 55% CO₂ to assess the impact of biogas composition on power performance. Lastly, for the cathode, we propose a material composition of Pt-Ti-samarium-doped ceria with a thickness of 100 nm for the nanoporous structure.

This paper presents a method for estimating the fatigue life of crossed roller bearings (XRBs). XRBs feature a single row of rollers arranged alternately at right angles, making them ideal for applications that require high precision and a compact design. In rolling-element bearings, fatigue life is a crucial design parameter for ensuring long-term reliability and performance. However, existing fatigue life estimation models for XRBs in the literature are limited to basic rating life, with no models available for reference rating life. To address this gap, we developed a comprehensive fatigue life prediction model specifically for XRBs. We formulated a corresponding dynamic load rating to align with the values provided by bearing manufacturers and calibrated an unknown adjustment factor for XRBs using a commercial program. Additionally, a parametric study was conducted to investigate the impact of varying diametral clearance, external loads, roller dimensions, and roller profile parameters on the fatigue life of XRBs.

Sustainable Aviation Fuel (SAF) is crucial for achieving carbon neutrality in the aviation sector. Among various production methods, Fischer–Tropsch (FT) synthesis using eco-friendly syngas has garnered significant attention. Two primary routes for producing syngas for FT synthesis—Dry Reforming of Methane (DRM) and Water Electrolysis combined with Reverse Water Gas Shift (WE&RWGS)—are actively being studied. As upstream processes, these routes are evaluated for their potential to provide low-carbon syngas for FT synthesis. However, comprehensive comparisons between these two pathways are limited, despite their importance for future technology planning and decision-making. In this study, we conduct a comparative evaluation of DRM- and WE&RWGS-based SAF production systems using virtual process design, along with life cycle assessment (LCA) and techno-economic analysis (TEA), to assess their environmental and economic viability as future technologies. LCA results indicate that the DRM-based route has more than four times lower environmental impact compared to the WE&RWGS-based system. The majority of the environmental burden arises from feedstock supply (CH₄ and CO₂) and energy inputs. TEA results suggest that while the base case scenario demonstrates limited economic feasibility, future scenarios that incorporate economies of scale and policy incentives show promise for long-term economic viability.

Carbon capture and storage is a vital strategy for mitigating rising atmospheric carbon dioxide, and metal–organic frameworks (MOFs) have gained attention as promising sorbents. Numerous simulations have examined factors governing CO₂ capture in MOFs—such as diffusion in MOF-74 under varying temperatures and process modeling of MOF-5—but most were limited to specific structures or conditions, hindering a systematic understanding of diffusion across diverse MOFs. Conventional computational methods also face constraints: density functional theory mainly provides static energy evaluations, while molecular dynamics relies on fixed force fields with poor transferability and an inability to describe reactive events. To overcome these limitations, this study employs molecular dynamics simulations driven by neural network potentials to evaluate CO₂ diffusivity in 17 types of MOFs. Results reveal significant variation in transport behavior, with zeolitic-imidazolate framework-3 showing the highest diffusivity and MOF-74 the lowest—an approximately 19-fold difference. These findings highlight the capability of neural-network-based molecular dynamics to deliver consistent and quantitative assessments of CO₂ transport in MOFs, providing a reliable framework for the rational design of next-generation capture materials.

Secondary batteries are crucial for eco-friendly systems, but existing technologies struggle with energy density and safety issues. This study aims to develop a next-generation battery utilizing quasi-solid electrolytes (QSE), which combine the advantages of both liquid and solid electrolytes. However, QSEs often lack the mechanical strength necessary to prevent lithium dendrite growth. To address this challenge, two strategies were proposed and experimentally validated. The first strategy involves creating a QSE-separator composite (QSE-PI) by integrating QSE with a polyimide (PI) separator. Among the various options, PI with a thickness greater than 20 μm and a pore size of 2-5 μm exhibited superior electrolyte absorption and dendrite suppression. This configuration allowed for rapid lithium plating/stripping, high ionic conductivity (1.7 × 10^-3 S cm^-1), and excellent Coulombic efficiency (99.94%).The second strategy incorporates silica (SiO₂) as a ceramic filler in the QSE-PI to enhance mechanical strength and ion transport. The addition of SiO₂ disrupted polymer crystallinity, increased the amorphous regions, and effectively suppressed dendrite formation. Notably, SiO₂ particles larger than 10 μm improved cycle stability, with the composite maintaining performance for over 50 cycles, compared to only 30 cycles for the version without filler.

Sensing the internal temperature of lithium-ion batteries is particularly useful for reliable battery operation as both electrochemistry and mass transport are dictated by local temperature. In this article, we review in operando techniques to monitor the internal temperature of lithium-ion batteries during charging and discharging. We categorize existing techniques into two groups: invasive and non-invasive approaches. Invasive techniques include optical fibers, thermocouples, and resistance temperature detectors as a thermometer. Non-invasive methods cover the temperature estimation techniques, namely electrochemical impedance spectroscopy as well as X-ray thermometry. For both approaches, we review working principle of thermometry, pros and cons of each thermometry, and recent studies to tackle relevant technical challenges. This review provides useful information for internal temperature measurements, offering chances for thermally reliable battery operation.

Commercial exoskeletons currently utilize multiple sensors, including inertial measurement units, electromyography sensors, and torque/force sensors, to detect human motion. While these sensors improve motion recognition by leveraging their unique strengths, they can also lead to discomfort due to direct skin contact, added weight, and complex wiring. In this paper, we propose a simplified motion recognition method that relies solely on encoders embedded in the motors. Our approach aims to accurately classify various movements by learning their distinctive features through a deep learning model. Specifically, we employ a convolutional neural network algorithm optimized for motion classification. Experimental results show that our model can effectively differentiate between movements such as standing, lifting, level walking, and inclined walking, achieving a test accuracy of 98.76%. Additionally, by implementing a sliding window maximum algorithm that tracks three consecutive classifications, we achieved a real-time motion recognition accuracy of 97.48% with a response time of 0.25 seconds. This approach provides a cost-effective and simplified solution for lower limb motion recognition, with potential applications in rehabilitation-focused exoskeletons.