• Tribology and Lubricants
• /
• v.38 no.4
• /
• pp.136-142
• /
• 2022

This paper investigates the enhanced wear performance of a CrAl coated accident tolerant fuel (ATF) cladding. In the wake of the Fukushima accident, extensive research on ATF with respect to improving the oxidation resistance of cladding materials is ongoing. Since coated Zr claddings can be applied without major changes to the criteria for reactor core design, many researchers are studying coatings for claddings. To improve the quality of the CrAl coating layer, optimization of the manufacturing process is imperative. This study employs arc ion plating to obtain improved CrAl coated claddings using CrAl binary alloy targets through an improved coating method. Surface roughness and adhesion are improved, and droplets are reduced. Furthermore, the coated layer has a dense and fine microstructure. In scratch tests, all the tested CrAl coated claddings exhibit a superior resistance compared to the Zr cladding. In a fretting wear test, the wear volume of the CrAl coated claddings is smaller compared to the Zr cladding. Furthermore, the coated cladding manufactured through the improved process exhibits better wear resistance than other CrAl coated claddings. Based on these results, we suggest that fine microstructure is attributed to a mechanically and microstructurally robust CrAl coating layer, which enhances wear resistance.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
• /
• v.36 no.5
• /
• pp.442-453
• /
• 2023

Tandem or multijunction solar cells (MJSCs) can convert sunlight into electricity with higher efficiency (η) than single junction solar cells (SJSCs) by dividing the solar irradiance over sub-cells having distinct bandgaps. The efficiencies of various common SJSC materials are close to the edge of their theoretical efficiency and hence there is a tremendous growing interest in utilizing the tandem/multijunction technique. Recently, III-V materials integration on a silicon substrate has been broadly investigated in the development of III-V on Si tandem solar cells. Numerous growth techniques such as heteroepitaxial growth, wafer bonding, and mechanical stacking are crucial for better understanding of high-quality III-V epitaxial layers on Si. As the choice of growth method and substrate selection can significantly impact the quality and performance of the resulting tandem cell and the terminal configuration exhibit a vital role in the overall proficiency. Parallel and Series-connected configurations have been studied, each with its advantage and disadvantages depending on the application and cell configuration. The optimization of both growth mechanisms and terminal configurations is necessary to further improve efficiency and lessen the cost of III-V on Si tandem solar cells. In this review article, we present an overview of the growth mechanisms and terminal configurations with the areas of research that are crucial for the commercialization of III-V on Si tandem solar cells.

• Nuclear Engineering and Technology
• /
• v.55 no.8
• /
• pp.3080-3087
• /
• 2023

Thermoelectric (TE) materials working in radioisotope thermoelectric generators are irradiated by neutrons throughout its service; thus, investigating the neutron irradiation stability of TE devices is necessary. Herein, the influence of neutron irradiation with fluences of 4.56 × 10¹⁰ and 1 × 10¹³ n/cm² by pulsed neutron reactor on the electrical and thermal transport properties of n-type Bi₂Te_2.7Se_0.3 and p-type Bi_0.5Sb_1.5Te₃ thermoelectric alloys prepared by cold-pressing and molding is investigated. After neutron irradiation, the properties of thermoelectric materials fluctuate, which is related to the material type and irradiation fluence. Different from p-type thermoelectric materials, neutron irradiation has a positive effect on n-type Bi₂Te_2.7Se_0.3 materials. This result might be due to the increase of carrier mobility and the optimization of electrical conductivity. Afterward, the effects of p-type and n-type TE devices with different treatments on the output performance of TE devices are further discussed. The positive and negative effects caused by irradiation can cancel each other to a certain extent. For TE devices paired with p-type Bi_0.5Sb_1.5Te₃ and n-type Bi₂Te_2.7Se_0.3 thermoelectric legs, the generated power and conversion efficiency are stable after neutron irradiation.

• Journal of Powder Materials
• /
• v.30 no.2
• /
• pp.146-155
• /
• 2023

The Ti-6Al-4V lattice structure is widely used in the aerospace industry owing to its high specific strength, specific stiffness, and energy absorption. The quality, performance, and surface roughness of the additively manufactured parts are significantly dependent on various process parameters. Therefore, it is important to study process parameter optimization for relative density and surface roughness control. Here, the part density and surface roughness are examined according to the hatching space, laser power, and scan rotation during laser-powder bed fusion (LPBF), and the optimal process parameters for LPBF are investigated. It has high density and low surface roughness in the specific process parameter ranges of hatching space (0.06-0.12 mm), laser power (225-325 W), and scan rotation (15°). In addition, to investigate the compressive behavior of the lattice structure, a finite element analysis is performed based on the homogenization method. Finite element analysis using the homogenization method indicates that the number of elements decreases from 437,710 to 27 and the analysis time decreases from 3,360 to 9 s. In addition, to verify the reliability of this method, stress-strain data from the compression test and analysis are compared.

• Korean Journal of Materials Research
• /
• v.33 no.5
• /
• pp.175-188
• /
• 2023

Water electrolysis holds great potential as a method for producing renewable hydrogen fuel at large-scale, and to replace the fossil fuels responsible for greenhouse gases emissions and global climate change. To reduce the cost of hydrogen and make it competitive against fossil fuels, the efficiency of green hydrogen production should be maximized. This requires superior electrocatalysts to reduce the reaction energy barriers. The development of catalytic materials has mostly relied on empirical, trial-and-error methods because of the complicated, multidimensional, and dynamic nature of catalysis, requiring significant time and effort to find optimized multicomponent catalysts under a variety of reaction conditions. The ultimate goal for all researchers in the materials science and engineering field is the rational and efficient design of materials with desired performance. Discovering and understanding new catalysts with desired properties is at the heart of materials science research. This process can benefit from machine learning (ML), given the complex nature of catalytic reactions and vast range of candidate materials. This review summarizes recent achievements in catalysts discovery for the hydrogen evolution reaction (HER) and oxygen evolution reaction (OER). The basic concepts of ML algorithms and practical guides for materials scientists are also demonstrated. The challenges and strategies of applying ML are discussed, which should be collaboratively addressed by materials scientists and ML communities. The ultimate integration of ML in catalyst development is expected to accelerate the design, discovery, optimization, and interpretation of superior electrocatalysts, to realize a carbon-free ecosystem based on green hydrogen.

• The Journal of the Acoustical Society of Korea
• /
• v.42 no.4
• /
• pp.305-312
• /
• 2023

Physics-Informed Neural Network (PINN) is used to invert bubble size distributions from attenuation losses. By considering a linear system for the bubble population inversion, Adaptive Learned Iterative Shrinkage Thresholding Algorithm (Ada-LISTA), which has been solved linear systems in image processing, is used as a neural network architecture in PINN. Furthermore, a regularization based on the linear system is added to a loss function of PINN and it makes a PINN have better generalization by a solution satisfying the bubble physics. To evaluate an uncertainty of bubble estimation, deep ensemble is adopted. 20 Ada-LISTAs with different initial values are trained using the same training dataset. During test with attenuation losses different from those in the training dataset, the bubble size distribution and corresponding uncertainty are indicated by average and variance of 20 estimations, respectively. Deep ensemble Ada-LISTA demonstrate superior performance in inverting bubble size distributions than the conventional convex optimization solver of CVX.

• Journal of the Korea Society of Computer and Information
• /
• v.28 no.4
• /
• pp.113-120
• /
• 2023

Smart farms are steadily increasing in research to minimize labor, energy, and quantity put into crops as IoT technology and artificial intelligence technology are combined. However, research on efficiently managing crop growth information in smart farms has been insufficient to date. In this paper, we propose a management technique that can efficiently monitor crop growth information by applying autonomous sensors to smart farms. The proposed technique focuses on collecting crop growth information through autonomous sensors and then recycling the growth information to crop cultivation. In particular, the proposed technique allocates crop growth information to one slot and then weights each crop to perform load balancing, minimizing interference between crop growth information. In addition, when processing crop growth information in four stages (sensing detection stage, sensing transmission stage, application processing stage, data management stage, etc.), the proposed technique computerizes important crop management points in real time, so an immediate warning system works outside of the management criteria. As a result of the performance evaluation, the accuracy of the autonomous sensor was improved by 22.9% on average compared to the existing technique, and the efficiency was improved by 16.4% on average compared to the existing technique.

• Membrane Journal
• /
• v.33 no.6
• /
• pp.344-351
• /
• 2023

The membrane separation process for carbon dioxide capture from hydrogen reformer exhaust gas has been developed. Using a commercial membrane module, a multi-stage process was developed to achieve 90% of carbon dioxide purity and 90% of recovery rate for ternary mixed gas. Even if a membrane module with being well-known properties such as material selectivity and permeability, the process performance of purity and recovery widely varies depending on the stage-cut, the pressure at feed and permeate side. In this study, we verify the limits of capture efficiency at single-stage membrane process under various operating conditions and optimized the two-stage recovery process to simultaneously achieve high purity and recovery rate.

• Journal of the Microelectronics and Packaging Society
• /
• v.30 no.4
• /
• pp.69-78
• /
• 2023

With the rapid growth of artificial intelligence, the demand for semiconductors is enormously increasing everywhere. To ensure the manufacturing quality and quantity simultaneously, the importance of automatic defect detection during the packaging process has been re-visited by adapting various deep learning-based methodologies into automatic packaging defect inspection. Deep learning (DL) models require a large amount of data for training, but due to the nature of the semiconductor industry where security is important, sharing and labeling of relevant data is challenging, making it difficult for model training. In this study, we propose a new framework for securing sufficient data for DL models with fewer computing resources through a divide-and-conquer approach. The proposed method divides high-resolution images into pre-defined sub-regions and assigns conditional labels to each region, then trains individual sub-regions and boundaries with boundary loss inducing the globally coherent and seamless images. Afterwards, full-size image is reconstructed by combining divided sub-regions. The experimental results show that the images obtained through this research have high efficiency, consistency, quality, and generality.

• Clean Technology
• /
• v.29 no.4
• /
• pp.321-326
• /
• 2023

Recently, there has been a growing interest in clean hydrogen energy that does not emit carbon dioxide during combustion due to the increasing focus on carbon neutral. Research related to hydrogen production continues, and in this study, we applied waste-derived synthesis gas to the water-gas shift reaction to simultaneously treat waste and produce high-purity hydrogen. To enhance catalytic activity in the high-temperature water-gas shift (HT-WGS) reaction, magnesium was used as a support material alongside cerium. Cu-CeO₂-MgO catalysts were synthesized, with copper acting as the active component for the HT-WGS reaction. A study on the catalytic activity based on the preparation method was conducted, and the Cu-CeO₂-MgO catalyst prepared by impregnation method exhibited the highest activity in the HT-WGS reaction. The observed superior performance of the Cu-CeO₂-MgO catalyst prepared through the impregnation method can be attributed to its significantly higher oxygen storage capacity and amount of active Cu species.