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Given the social demand for self-powering wearable electronics, it is necessary to develop composite materials that exhibit both good flexibility and excellent piezoelectric performances. Intensive research on synthesis methods and devising characterization techniques for piezoelectric nanomaterials in various forms has been conducted. In particular, characterization techniques for piezoelectric nanomaterials require different approaches from those for conventional bulk materials. Atomic force microscopy (AFM)-based characterization techniques work based on the local physical interactions between the AFM tip and sample surfaces, making them an irreplaceable tool for studying the electromechanical properties of piezoelectric nanomaterials. Piezoresponse force microscopy (PFM), conductive AFM (C-AFM), and lateral force microscopy (LFM) are three representative AFM-based techniques used to characterize the piezoelectric and ferroelectric properties of nanomaterials. Coupled with the appearance of diverse novel nanomaterials such nanowires, free-standing nanorods, and electrospun nanofibers, AFM-based characterization techniques are becoming freer from artifacts and the need for quantitative measurements. PFM was initially developed to image the microstructures of piezoelectric materials, and well-calibrated techniques designed to realize quantitative measurements have been applied to nanomaterials. In contrast, C-AFM and LFM were initially used to measure the conductivity of diverse materials and the nanotribology of material surfaces. Over the last decade, they have proved their versatility and can now be used to evaluate the direct piezoelectric effect and the mechanical properties of piezoelectric nanomaterials. In these cases, systematic understanding with regard to the measurement principles is required for accurate measurements and analyses. In the present review article, we discuss earlier work in which AFM-based electromechanical characterization techniques were applied to nanomaterials to evaluate piezoelectric and ferroelectric properties. Also discussed is the importance of gaining a comprehensive understanding of the resulting signals. (Received 17 May, 2022; Accepted 7 June, 2022)

keyword : atomic force microscopy, piezoresponse force microscopy, conductive atomic force microscopy, lateral force microscopy, piezoelectric, ferroelectric

The thermoplastic forming (TPF) process of metallic glass (MG) is a unique and powerful method that cannot be performed using conventional crystalline alloys. Because the mechanical and thermal properties of MGs are more favorable with smaller sample sizes, TPF is particularly useful for microscale and nanoscale part molding and micro-patterning. One of the promising commercial MG applications that can take full advantage of these characteristics is hologram patterning. Holograms can be used to identify unique brands, using characteristics with patterns that are difficult to replicate. Their excellent aesthetic qualities can also greatly contribute to increased product value. In this study, we developed and performed a TPF process for actual holographic imprinting with Mg-based MGs, and further investigated the TPF processing window, covering a wide range of temperature and process time conditions through thermal analysis, with ultra-fast heating rates ranging from 100 to 25000 K/s using Flash-DSC. The results of this study serve as a practical guide for identifying the full range of TPF processing windows including conventional and ultrafast heating conditions for micro-scale and nanoscale molding of various MGs. Moreover, a methodology is proposed to identify the general TPF processing window (η<10⁸ Pa·s) and the ideal TPF processing window (η<10⁴ Pa·s) by estimating the viscosity (η) of the supercooled liquid. Accordingly, this study is expected to be utilized to optimize the TPF process of MGs and promote the commercialization of related industries. (Received 17 June, 2022; Accepted 27 June, 2022)

keyword : metallic glass, thermoplastic forming, hologram imprinting, flash-DSC, thermoplastic forming window

This study investigated the microstructure and mechanical properties of Mg-1Gd-1Y-1Zn (at.%) alloys containing designed amounts of Ce or La. The Mg₅(Gd,Zn) phase formed in the as-cast Mg-Gd-Y-Zn-Ce/La alloys and disappeared after a homogenization treatment at 500℃ for 24 h. The addition of Ce and La resulted in the formation of Ce(Mg,Zn)₁₂ and La(Mg,Zn)₁₂ phases, respectively. Except for that, the Ce or La addition had no significant effect on the morphology, volume fraction, and type of the long-period stacking ordered (LPSO) phases in the Mg-Gd-Y-Zn alloy. The grain size decreased with increasing microalloying content because the heavy Ce and La atoms impeded atomic migration across the boundaries. The solute drag effect led to the formation of the rare earth texture in the extruded Mg-Gd-Y-Zn-Ce/La alloys, whose extent decreased with increasing microalloying content. The mechanical strength was improved by the addition of Ce or La at the sacrifice of ductility. In particular, La exhibited a stronger reinforcement ability than Ce when it was added to the Mg-Gd-Y-Zn alloys. Among the investigated chemical compositions, the Mg-1Gd-1Y-1Zn-0.3La alloy exhibited the highest strength because it had the finest grains, the highest volume fraction of the second phase, and the weakest texture intensity. Furthermore, the alloys showed an unusual yield asymmetry due to the difference in the deformation mode of the LPSO phase. (Received 12 April, 2022; Accepted 20 June, 2022)

keyword : Mg-Gd-Y-Zn, magnesium, microalloying, LPSO, rare earth

Transparent and conductive ZnO 50 nm/Au 8 nm/ZnO 50 nm tri-layer films were deposited on polyimide films by radio frequency (RF) and direct current (DC) magnetron sputtering at room temperature, and then the effect of electron irradiation on the crystallization, electrical resistivity and optical properties of the films was considered with X-ray diffraction, UV-visible spectrometer, Atomic force microscope and Hall measurement system. All the films were deposited at a fixed sputtering power, Ar gas flow rate, and distance between target and substrate, while the post-deposition electron irradiation energy was varied from 300 to 900 eV. The electron irradiated films exhibited a flatter surface than the as deposited films that were not electron irradiated, and the XRD patterns also revealed that the electron irradiated films had larger grain sizes than that of as deposited films. The films electron irradiated at 900 eV also showed a higher visible transmittance of 79.8% and a lower sheet resistance of 56.0 Ω/□. Post-deposition films electron irradiated at 900 eV showed a higher figure of merit of 1.86×10^-3 Ω^-1 than that of the as deposited film of 1.29×10^-3 Ω^-1. The optical band gap was also enhanced by electron irradiation. The films electron irradiated at 900 eV showed a higher optical band gap of 4.07 eV. (Received 15 April, 2022; Accepted 17 June, 2022)

keyword : ZnO, Au, XRD, AFM, Figure of merit

The gas tungsten arc (GTA) weldability of high-Mn (HMn) steel and austenitic stainless steel (STS) 316L using dissimilar metal welds (DMWs) was investigated, to fabricate a fuel tank for an LNG-fueled ship. Three types of welding fillers, HMn steel, STS 309LMo and Inconel 625, were applied to the DMWs. The weldability of the DMWs was examined by investigating mechanical properties at 25 and -196℃, followed by microstructural evolution and weld deformation. The GTA welding process was performed with a heat input range of 0.72-1.89 kJ/mm. All of the fillers employed in the study produced reasonable mechanical properties and low levels of precipitation, such as M₇C₃ and Fe₃C. However, the DN specimen using the Inconel 625 filler exhibited reduced ductility in the bending test. This was attributed to Nb-carbide precipitations at dendrite boundaries at the root weld. The DM specimen using HMn filler experienced severe thermal deformation because it had a relatively higher coefficient of thermal expansion than the other fillers during heating by arc and solidification. Therefore, STS 309LMo and HMn wires were determined to be the appropriate fillers for the DMW of HMn steel and STS 316L for cryogenic applications, and the HMn wire requires attention to weld deformation. (Received 18 April, 2022; Accepted 21 June, 2022)

keyword : high manganese steel, stainless steel 316L, inconel 625, dissimilar metal welding, cryogenic property, weld deformation

With the development of additive manufacturing technology, many types of materials are being utilized, and methods of printing the materials and characteristics of their hydration properties are being studied in the fields of construction and biotechnology. Tricalcium silicate (C3S), which is used as a cement material or biomaterial, is a representative hydraulic material. In previous research, scaffolds were printed via fused deposition modeling and the deformation properties during the hydration process of the printed scaffold were investigated. C3S, like ceramic materials, requires post-processing such as curing after printing, and volumetric deformation occurs during this process. Deformation information is very important to ensure the numerical value of the final product, as well as to suppress the possibility of deformation. In this study, silica, hydroxyapatite (HA), and alumina were mixed with three types of fillers to print a C3S support, which was then cured through a two-step process. In this process, HA and silica, which have good hydrophilicity, exhibited high strength due to the suppression of scaffold deformation. It was confirmed that the smaller the particle size, the more effective it was in obtaining a stable hydrated print. (Received 10 May, 2022; Accepted 7 July, 2022)

keyword : 3D printing, additive manufacturing, sintering-free, hydration reaction, tricalcium silicate(C3S)

To manufacture composites with required properties, it is extremely important to select an appropriate filler. Carbon-based nano materials such as carbon black, graphene and carbon nanotube (CNT) have been extensively investigated as reinforcing and conducting fillers. Because of their high aspect ratio coupled with superior physical properties, 1-dimensional CNTs are ideal as filler materials to impart electrical conductivity to insulating polymers, while enhancing mechanical strength. In this study, we investigated the piezo-resistive and mechanical properties of composites consisting of two types of CNT classified as multi-walled CNT (MWNT) or single-walled CNT (SWNT) depending on the number of walls. Since MWNT and SWNT have different physical properties such as specific surface area and aspect ratio, this can affect the composite’s performance. To more effectively evaluate the effect of MWNTs and SWNTs in composites, we used thermoplastic polyurethane (TPU) as a matrix, which is an insulating stretchable elastomer. Morphological and mechanical/electrical characterizations were conducted to determine differences in the MWNT and SWNT composites. In addition, we conducted dynamic strain sensing tests on each type of CNT composites to compare the sensitivity as a strain sensor. Differences in piezo-resistive behaviors were attributed to the loss of electrical contact points during stretching. These results can serve as a useful design guideline for the wider use of CNT composites. (Received 13 April, 2022; Accepted 21 June, 2022)

keyword : carbon nanotube, composite, mechanical property, electrical properties

This study investigates the effects of precompression and subsequent annealing on the tensile deformation behavior of a rolled AZ31 Mg alloy at room temperature using digital image correlation (DIC). When the as-rolled sample (AR sample) is subjected to precompresssion along the rolling direction (RD) and transverse direction (TD), the sample’s texture changes from the typical normal direction (ND)-oriented basal texture to the RD- and TD-oriented basal textures, respectively, because of the lattice reorientation by {10-12} twinning. During tension along the RD, the AR sample and the sample precompressed along the TD and subsequently annealed at 250 ℃ (TDCA sample) accomodate the tensile strain via dislocation slip, resulting in high yield strengths and slip-dominant strain-hardening behaviors. In contrast, the sample precompressed along the RD and subsequently annealed at 250 ℃ (RDCA sample) exhibits a low yield strength and twinningdominant strain-hardening behavior, owing to the vigorous activation of {10-12} twinning during tension. DIC results reveal that in the AR sample, noticeable strain localization occurs at an early stage of tensile deformation due to the difficulty of accommodating strain along the thickness direction. In the RDCA sample, strain distribution is relatively homogeneous via {10-12} twinning, but the rapid strain hardening caused by abundant {10-12} twins causes premature crack initiation. Because the basal planes of most grains of the TDCA sample are aligned parallel to the thickness direction, the thickness strain is effectively accommodated via prismatic slip, resulting in the highest tensile elongation among the three samples. (Received 23 May, 2022; Accepted 6 July, 2022)

keyword : magnesium alloy, tension, dislocation slip, twinning, digital image correlation (DIC)

We investigated various numerical methods including a physical-based empirical equation, linear regression, shallow neural network, and deep learning approaches, to compare their accuracy for predicting the hardness of tempered martensite in low alloy steels. The physical-based empirical equation, which had been previously proposed with experimental data, was labelled and used in the present study. While it had a smaller number of coefficients, the prediction accuracy of the physical-based empirical equation was almost similar to that of the regression model based on the response surface method. The prediction accuracy of the machine learning models clearly improved as the number of layers increased and became more complicated in structure before the model began to overfit. The key point we found was that a single layered neural network model with optimized hyperparameters resulted in similar or better hardness prediction performance compared to deep learning models with a more complex architecture. We also analyzed 18 research papers from the literature which used neural network models to predict the hardness of steels. Only two recent papers adopted a convolutional neural network, as a kind of deep learning model, in a new attempt to predict hardness. The other 16 papers from 1998 to 2021 commonly chose shallow neural network models because a more complicated model is less effective than a simple model for regression problems with well-labeled experimental data in materials science and engineering. (Received 3 June, 2022; Accepted 8 July, 2022)

keyword : prediction accuracy, model complexity, machine learning, model regression, tempered martensite hardness