Exposure to ambient particulate matter (PM) 2.5 can impair alveolar macrophages (AMs) functions and induce pathological conditions. This study explores the impact of PM2.5 exposure on AMs, focusing on gene expression changes and functional alterations. Murine AMs cell line MH-S were exposed to PM2.5 at a concentration of 100 ㎍/ml for durations of 3 and 48 hours. After 3 hours of exposure, genes involved in inflammatory and immune responses were activated, including TNF and B-cell receptor signaling pathways. In contrast, 48 hours of exposure significantly altered genes related to cell proliferation, apoptosis, and immune processes, with PI3K-Akt and cytokine-cytokine receptor interaction pathways. PM2.5 initially triggers immune responses; however, prolonged exposure over 48 hours impairs AM functions risk of chronic inflammation and cancer. This research provides foundational data for therapeutic approaches related to PM2.5 exposure.

Enhancing the oxygen surface exchange reaction by increasing the specific surface area of the electrode is a promising structural approach to lowering the operating temperature of solid oxide fuel cells (SOFCs). Nanowire structures, due to their high specific surface area and lower tortuosity of ion and electron conduction pathways, play a vital role in enhancing SOFC electrode performance. In this study, we synthesized La_0.6Sr_0.4CoO_3-𝛿 (LSC) nanowires using a polycarbonate membrane filter as a nanotemplate and applied them to the cathode for intermediate-temperature SOFC fabrication. The fabricated cell exhibited a 10% increase in peak power density at 650℃, achieving 0.506 W·cm^-2, compared to cell using only commercial LSC powder. Furthermore, distribution of relaxation times analysis revealed a 15% reduction in area-specific polarization resistance in the mid-frequency range. These findings demonstrated that the electrode with LSC nanowires fabricated through electrospray deposition can significantly improve electrochemical performance of intermediate-temperature SOFC.

Liquid-spray cleaning has recently been considered an eco-friendly cleaning method in the semiconductor industry because it efficiently cleans contaminated wafers without using any chemicals, relying instead on direct momentum transfer through dropwise impaction. Previous researches are mainly divided into two groups, such as modelling studies predicting the cleaning effect of single-droplet impact and experimental works for measuring particle removal efficiency (PRE) that essentially accompanies multiple droplet impacts. Here, we developed a Monte Carlo model to connect the single-droplet based model to the ensemble effect of multiple droplet impacts in real cleaning experiments, and thereby predict the PREs from the impaction conditions of droplets and the diameters of target particles. Additionally, we developed a two-fluid supersonic nozzle system, capable of spraying 10-60 ㎛ droplets under control of impact velocity, with aims to validate the model predictions of PREs for 15-130 nm contaminant particles on a Si wafer. We confirmed that the model predictions are in agreement with the experimental data within 7% and the cleaning time needs to be controlled for ensuring the efficient removal of particles.