Effects of environmental factors on corrosion behavior of aluminum

Aluminum, used as a material for heat exchangers in air conditioners, often has problems of leakage of refrigerant on the Al surface due to corrosion. The problems originate from pitting corrosion of the Al in an external environment. To understand corrosion problems, it is necessary to study the corrosion behavior of Al in various environments. In this study, the effects of environmental factors on the corrosion behavior of Al were studied by the surface analysis and electrochemical testing in 3.5 wt% NaCl solutions, with changes of dissolved oxygen, temperature, and concentration of Cl⁻ and S ions. Among the external environmental factors, the presence of oxygen and the increase of Cl⁻ ion concentration do not significantly affect the corrosion potential of Al, leading to an increase of only 1.1 and 6 times, respectively. There was a significant decrease in the corrosion resistance of Al, approximately 40 and 800 times, respectively, with the increase of concentration of S and temperature.     

Korean automatic spacing using pretrained transformer encoder and analysis

Automatic spacing in Korean is used to correct spacing units in a given input sentence. The demand for automatic spacing has been increasing owing to frequent incorrect spacing in recent media, such as the Internet and mobile networks. Therefore, herein, we propose a transformer encoder that reads a sentence bidirectionally and can be pretrained using an out-of-task corpus. Notably, our model exhibited the highest character accuracy (98.42%) among the existing automatic spacing models for Korean. We experimentally validated the effectiveness of bidirectional encoding and pretraining for automatic spacing in Korean. Moreover, we conclude that pretraining is more important than fine-tuning and data size.     

Sub‐5 nm Dendrimer Directed Self‐Assembly with Large‐Area Uniform Alignment by Graphoepitaxy

Directed self‐assembly (DSA) using soft materials is an important method for producing periodic nanostructures because it is a simple, cost‐effective process for fabricating high‐resolution patterns. Most of the previously reported DSA methods exploit the self‐assembly of block copolymers, which generates a wide range of nanostructures. In this study, cylinders obtained from supramolecular dendrimer films with a high resolution (<5 nm) exhibit planar ordering over a macroscopic area via guiding topographical templates with a high aspect ratio (>10) and high spatial resolution (≈20 nm) of guiding line patterns. Theoretical and experimental studies reveal that this property is related to geometrical anchoring on the meniscus region and physical surface anchoring on the sidewall. Furthermore, this DSA of dendrimer cylinders is demonstrated by the non‐regular geometry of the patterned template. The macroscopic planar alignment of the dendrimer nanostructure reveals an extremely small feature size (≈4.7 nm) on the wafer scale (>16 cm²). This study is expected to open avenues for the production of a large family of supramolecular dendrimers with different phases and feature dimensions oriented by the DSA approach.     

Environmental Enrichment Enhances Cav 2.1 Channel-Mediated Presynaptic Plasticity in Hypoxic–Ischemic Encephalopathy

Hypoxic–ischemic encephalopathy (HIE) is a devastating neonatal brain condition caused by lack of oxygen and limited blood flow. Environmental enrichment (EE) is a classic paradigm with a complex stimulation of physical, cognitive, and social components. EE can exert neuroplasticity and neuroprotective effects in immature brains. However, the exact mechanism of EE on the chronic condition of HIE remains unclear. HIE was induced by a permanent ligation of the right carotid artery, followed by an 8% O₂ hypoxic condition for 1 h. At 6 weeks of age, HIE mice were randomly assigned to either standard cages or EE cages. In the behavioral assessments, EE mice showed significantly improved motor performances in rotarod tests, ladder walking tests, and hanging wire tests, compared with HIE control mice. EE mice also significantly enhanced cognitive performances in Y-maze tests. Particularly, EE mice showed a significant increase in Ca_v 2.1 (P/Q type) and presynaptic proteins by molecular assessments, and a significant increase of Ca_v 2.1 in histological assessments of the cerebral cortex and hippocampus. These results indicate that EE can upregulate the expression of the Ca_v 2.1 channel and presynaptic proteins related to the synaptic vesicle cycle and neurotransmitter release, which may be responsible for motor and cognitive improvements in HIE.     

Thermal treatment effect on the magnetoelectric properties of (Ni0.5Zn0.5)Fe2O4/Pt/Pb(Zr0.3Ti0.7)O3 heterostructured thin films

Magnetoelectric (ME) property modulation in heterostructured (Ni_0.5Zn_0.5)Fe₂O₄/Pt/Pb(Zr_0.3Ti_0.7)O₃ (NZFO/Pt/PZT) thin films on platinized Si substrate by thermal annealing condition variation was studied. In an attempt to prevent interfacial reaction between NZFO and PZT layers during high temperature annealing, thin Pt layer was deposited which can serve as inter-diffusion barrier as well as electrode. The ferroelectric, magnetic, and ME properties of the heterostructured film were noticeably modulated due to microstructural evolution and clamping relaxation developed during thermal annealing process. Room temperature ME voltage coefficient of the heterostructured thin films was enhanced with increasing annealing temperature and reached to 29 mV/cm·Oe when annealed at 650 °C.     

Aqueous dispersion of 1D van der Waals Mo6S3I6 crystal using biocompatible tri-block copolymer

To achieve the stable dispersion of 1D van der Waals crystal Mo₆S₃I₆ in aqueous media, the tri-block copolymer (Poloxamer) is used as dispersant. The head group of Poloxamer, hydrophobic polypropylene oxide parts can be adsorbed to Mo₆S₃I₆ surface by hydrophobic interaction and the tail group with hydrophilic polyethylene oxide exposed to the outside of the Mo₆S₃I₆ is soluble in water and can form sufficient steric hindrance, resulting in stable aqueous dispersion in nm scale. The excellent biocompatibility of aqueous dispersed nm scale 1D Mo₆S₃I₆ was demonstrated by effective proliferation of C2C12 cells.     

Machine Learning-Based Two-Stage Data Selection Scheme for Long-Term Influenza Forecasting

One popular strategy to reduce the enormous number of illnesses and deaths from a seasonal influenza pandemic is to obtain the influenza vaccine on time. Usually, vaccine production preparation must be done at least six months in advance, and accurate long-term influenza forecasting is essential for this. Although diverse machine learning models have been proposed for influenza forecasting, they focus on short-term forecasting, and their performance is too dependent on input variables. For a country’s long-term influenza forecasting, typical surveillance data are known to be more effective than diverse external data on the Internet. We propose a two-stage data selection scheme for worldwide surveillance data to construct a long-term forecasting model for influenza in the target country. In the first stage, using a simple forecasting model based on the country’s surveillance data, we measured the change in performance by adding surveillance data from other countries, shifted by up to 52 weeks. In the second stage, for each set of surveillance data sorted by accuracy, we incrementally added data as input if the data have a positive effect on the performance of the forecasting model in the first stage. Using the selected surveillance data, we trained a new long-term forecasting model for influenza and perform influenza forecasting for the target country. We conducted extensive experiments using six machine learning models for the three target countries to verify the effectiveness of the proposed method. We report some of the results.     

Efficient fault diagnosis method of PEMFC thermal management system for various current densities

The temperature of a fuel cell has a considerable impact on the saturation of a membrane, electrochemical reaction speed, and durability. So thermal management is considered one of the critical issues in polymer electrolyte membrane fuel cells. Therefore, the reliability of the thermal management system is also crucial for the performance and durability of a fuel cell system. In this work, a methodology for component-level fault diagnosis of polymer electrolyte membrane fuel cell thermal management system for various current densities is proposed. Specifically, this study suggests fault diagnosis using limited data, based on an experimental approach. Normal and five component-level fault states are diagnosed with a support vector machine model using temperature, pressure, and fan control signal data. The effects of training data at different operating current densities on fault diagnosis are analyzed. The effects of data preprocessing method are investigated, and the cause of misdiagnosis is analyzed. On this basis, diagnosis results show that the proposed methodology can realize efficient component-level fault diagnosis using limited data. The diagnosis accuracy is over 92% when the residual basis scaling method is used, and data at the highest operating current density is used to train the support vector machine.