Chitosan nanoparticles (CNPs) were synthesized in this study to enhance the limited bioactivity and stability of <i>Cordyceps militaris</i> grown on germinated <i>Rhynchosia nulubilis</i> (GRC) and effectively deliver it to target tissues. Under optimized conditions, stable encapsulation of GRC was achieved by setting the chitosan (CHI)-to-tripolyphosphate (TPP) ratio to 4:1 and adjusting the pH of TPP to 2, resulting in a zeta potential of +22.77 mV, which indicated excellent stability. As the concentration of GRC increased, the encapsulation efficiency decreased, whereas the loading efficiency increased. Fourier-transform infrared (FT-IR) spectroscopy revealed shifts in the amide I and II bands of CHI from 1659 and 1578 to 1639 cm⁻¹, indicating hydrogen bonding and successful encapsulation of GRC encapsulated with CNPs (GCN). X-ray diffraction (XRD) examination revealed the transition of the nanoparticles from a crystalline to an amorphous state, further confirming successful encapsulation. In vivo experiments demonstrated that GCN treatment significantly reduced lung injury scores in fine particulate matter (PM_2.5)-exposed mice (<i>p</i> < 0.05) and alleviated lung epithelial barrier damage by restoring the decreased expression of occludin protein (<i>p</i> < 0.05). In addition, GCN decreased the PM_2.5-induced upregulation of <i>MMP-9</i> and <i>COL1A1</i> mRNA expression levels, preventing extracellular matrix (ECM) degradation and collagen accumulation (<i>p</i> < 0.05). GCN exhibited antioxidant effects by reducing the mRNA expression of nitric oxide synthase (<i>iNOS</i>) and enhancing both the protein and mRNA expression of superoxide dismutase (<i>SOD-1</i>) caused by PM_2.5, thereby alleviating oxidative stress (<i>p</i> < 0.05). In A549 cells, GCN significantly reduced PM_2.5-induced reactive oxygen species (ROS) production compared with GRC (<i>p</i> < 0.05), with enhanced intracellular uptake confirmed using fluorescence microscopy (<i>p</i> < 0.05). In conclusion, GCN effectively alleviated PM_2.5-induced lung damage by attenuating oxidative stress, suppressing apoptosis, and preserving the lung epithelial barrier integrity. These results emphasize its potential as a therapeutic candidate for preventing and treating the lung diseases associated with PM_2.5 exposure.

While all-solid-state batteries (ASSBs) have received considerable attention as next-generation energy storage devices, the problems that arise between the cathodes and solid-state electrolytes (SSEs) of ASSBs have been regarded as among the greatest technical issues related to ASSBs, and several studies have been conducted to address these issues. A stable oxide buffer layer coating on the interface between the cathode active material (CAM) and the SSE is known to be effective in suppressing side reactions at the interface, including space charge layer formation and SSE decomposition. To achieve this, it is essential to coat a uniform and conformal buffer layer on the CAM surface. In this study, a powerful technique for conformal coating on a particle, called particle atomic layer deposition (ALD), was introduced to produce the buffer layer coating on CAM particles. The introduction of particle ALD to the energy technology was studied, and the synthesis of ternary materials through particle ALD was demonstrated for the first time. The results show that a buffer layer fabricated by particle ALD is effective in suppressing side reactions and enhancing the performance of ASSBs.

Abstract All‐solid‐state lithium batteries (ASSLBs) are promising energy‐storage devices with high energy density and safety. However, the discharge capacity of ASSLBs that use sulfide solid electrolytes (SEs) and Ni‐rich cathode active materials (CAMs) deteriorates significantly during electrochemical cycling. Interfacial coatings between CAM and sulfide SEs are known to effectively alleviate capacity deterioration. Among the various coating materials, lithium phosphate (LPO) was selected and synthesized via atomic layer deposition (ALD). The crystal phase of the LPO thin film irreversibly changed, and the Li‐ion conductivity deteriorated after heat treatment. Additionally, particle ALD (P‐ALD) of LPO was successfully performed to completely passivate the CAM particles. Torque cells with the LPO‐coated CAMs showed excellent capacity retention, whereas the cells without the LPO coating showed a significantly deteriorated discharge capacity with increased internal resistances. The passivation effect of the LPO coating was confirmed by the cross‐sectional images from a FIB and scanning electron microscopy with energy‐dispersive X‐ray spectroscopy (SEM–EDS) dual system. We believe that the study will help us investigate the effects of side reactions between sulfide SEs and CAMs on the performance and stability of P‐ALD.

Radiation hardness tests were conducted on a silicon detector and preamplifier to develop a reactor coolant leakage monitoring system, which detects high-energy beta particles from 16N in the primary reactor coolant of nuclear power plants. Monte Carlo simulations were used to calculate the doses that would be absorbed on components over a 60-year design lifetime. The components were exposed to a 60Co gamma source with an activity of 2.4 kCi for 54 hours. The absorbed doses accumulated during the test were determined to be 0.99 kGy for the detector and 1.37 kGy for the preamplifier. During the test, the alpha count from a check source in the high-channel range disappeared in a high-dose-rate environment, and the gross gamma count decreased as the accumulated dose increased. The performance degradation of the detector and preamplifier was evaluated by comparing the alpha signals and background noise before and after irradiation. The energy resolution of the alpha signals in the high-channel range exhibited slight changes, whereas the electronic noise in the low-channel range increased by approximately 773 % for the irradiated detector and 17 % for the irradiated pre-amp. A method employing various low-level discriminator channels is proposed to miti-gate noise effects in the monitoring system.

In modern manufacturing after the Fourth Industrial Revolution, fast and accurate production is essential, and it is important to maintain and improve process reliability and productivity. Beyond the traditional conservative maintenance method, it is important to monitor the condition of facilities and take preventive measures. This study aims to analyze vibration data of manufacturing equipment and predict rotor defects using deep learning technology. An anomaly detection model that distinguishes between normal and abnormal conditions of the equipment is trained, and proactive problem prediction through internal and external data (sensor data, operation logs, etc.) is emphasized. Vibration data is converted into two-dimensional image data through Short Time Fourier Transform (STFT), and feature extraction is performed through Convolutional Neural Network (CNN) algorithm. Then, an Autoencoder (AE) based time series anomaly detection technique is used to make efficient and accurate predictions. In order to strengthen the competitiveness of the manufacturing industry, a deep learning-based rotor defect prediction model was developed, which is expected to improve the stability and productivity of the manufacturing process.

Chitosan nanoparticles (CNPs) were synthesized in this study to enhance the limited bioactivity and stability of <i>Cordyceps militaris</i> grown on germinated <i>Rhynchosia nulubilis</i> (GRC) and effectively deliver it to target tissues. Under optimized conditions, stable encapsulation of GRC was achieved by setting the chitosan (CHI)-to-tripolyphosphate (TPP) ratio to 4:1 and adjusting the pH of TPP to 2, resulting in a zeta potential of +22.77 mV, which indicated excellent stability. As the concentration of GRC increased, the encapsulation efficiency decreased, whereas the loading efficiency increased. Fourier-transform infrared (FT-IR) spectroscopy revealed shifts in the amide I and II bands of CHI from 1659 and 1578 to 1639 cm⁻¹, indicating hydrogen bonding and successful encapsulation of GRC encapsulated with CNPs (GCN). X-ray diffraction (XRD) examination revealed the transition of the nanoparticles from a crystalline to an amorphous state, further confirming successful encapsulation. In vivo experiments demonstrated that GCN treatment significantly reduced lung injury scores in fine particulate matter (PM_2.5)-exposed mice (<i>p</i> < 0.05) and alleviated lung epithelial barrier damage by restoring the decreased expression of occludin protein (<i>p</i> < 0.05). In addition, GCN decreased the PM_2.5-induced upregulation of <i>MMP-9</i> and <i>COL1A1</i> mRNA expression levels, preventing extracellular matrix (ECM) degradation and collagen accumulation (<i>p</i> < 0.05). GCN exhibited antioxidant effects by reducing the mRNA expression of nitric oxide synthase (<i>iNOS</i>) and enhancing both the protein and mRNA expression of superoxide dismutase (<i>SOD-1</i>) caused by PM_2.5, thereby alleviating oxidative stress (<i>p</i> < 0.05). In A549 cells, GCN significantly reduced PM_2.5-induced reactive oxygen species (ROS) production compared with GRC (<i>p</i> < 0.05), with enhanced intracellular uptake confirmed using fluorescence microscopy (<i>p</i> < 0.05). In conclusion, GCN effectively alleviated PM_2.5-induced lung damage by attenuating oxidative stress, suppressing apoptosis, and preserving the lung epithelial barrier integrity. These results emphasize its potential as a therapeutic candidate for preventing and treating the lung diseases associated with PM_2.5 exposure.