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.

To reduce the use of fossil fuels, the adoption of battery electric vehicles (BEVs) using lithium-ion batteries has been increasing in internal combustion engine alternatives. Accordingly, significant efforts have been made to improve the manufacturing process of lithium-ion batteries within electric vehicles. In particular, the cutting process of lithium-ion batteries has been actively discussed as it is closely related to battery performance. Laser-based cutting enables a more precise and sustainable manufacturing process. The laser ablation threshold has been investigated in many studies to achieve high-precision laser processing. While laser parameters and ambient conditions have been examined to determine the laser ablation threshold, studies focusing on the effect of relative humidity remain insufficient. Thus, this study investigated the laser ablation threshold of aluminum foil under varying relative humidity conditions. First, a laser interaction chamber was fabricated to control the relative humidity during experiments. A scanning electron microscope (SEM) was then used to observe laser ablation craters and analyze the threshold. The variation of the laser ablation threshold with relative humidity revealed changes in the interaction between the laser and aluminum foil depending on the humidity level.

In this study, we aim to develop a self-humidifying polymer electrolyte membrane fuel cell (PEMFC) by depositing platinum (Pt) on a membrane using sputtering. After we coated it with a Nafion^® ionomer solution. This is considered a solution that can prevent membrane degradation in low humidity conditions. By introducing this self-humidifying concept, we can expect improved performance compared to conventional PEMFCs. By managing the water content of Nafion^®, we aim to improve both the stability and performance of the PEMFCs. This research contributes to the development of more efficient and reliable PEMFC systems, showing promise for advances in this field.

Printed electronics is a technology which is used for manufacturing flexible electronic devices dubbed as next-generation electronics such as wearable applications. To commercialize them, it is important to guarantee their electrical performance under various environmental conditions such as temperature and humidity. Moreover, flexible electronic devices usually undergo mechanical deformations such as bending and twisting, hence, it is necessary to observe the electrical performance of flexible devices under mechanical deformation considering both temperature and humidity. The effects of temperature and humidity on flexible printed electrodes, as an example of the simplest flexible electronics, under static deformation of bending and twisting are studied. Electrodes that do not deform are also strongly affected by temperature and humidity, and the increase in resistances of the electrodes with deformation is highest when twisting. The magnitude of static deformation does not affect the conductivity. The effect of line width is important for the twisting deformation. To commercialize printed electronics devices, the effects of temperature and humidity should be considered, with further consideration of the effects of mechanical deformation on the design of the devices.

The objective of this study is to investigate a novel temperature and humidity prediction algorithm for smart greenhouse based on the machine learning method. The smart greenhouse is known to increase farm production by automatically controlling temperature and humidity and other factors. However, maintaining constant inside temperature and humidity in the conventional smart greenhouse system is still a problem because of the multiple time delay elements. To solve the problems, prediction control scheme is required. But, since the system is highly nonlinear with the lack of sensory data, predicting accurate temperature and humidity is very challenging. In this paper, the multi-dimensional Long Short-Term Memory networks (LSTMs) is being applied to deal with the unstructured greenhouse environmental data. The designed LSTMs learning model is trained with the 27 dimensional data which comprises of all the greenhouse control parameter and environmental sensory data. The prediction performance was evaluated using the short, mid and long term experiments. Also, the comparison with the conventional recurrent neural networks (RNNs) based prediction algorithm was done using the experimental results and later on discussions.