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Solid oxide fuel cells (SOFCs) have been recognized as one of the powerful next-generation energy conversion systems in association of the demanding green carbon technology. The electrochemical performance is crucially dependent on the intricate microstructures of porous electrodes, either cathodes or anodes. The composite electrodes should be analyzed in the sophisticated manner by characterizing the microstructural parameters. The current work combines electron microscopy with machine learning, more specifically semantic segmentation. The semantic segmentation was synergistically combined with high volume of electron micrographs enabled by FIB-SEM which has been used for microstructural analyses in SOFCs. The Ni/YSZ anode composites were selected as a model system, by incorporating the third components, i.e., pores featured by porous electrodes. The semantic segmentation-predicted image separation was connected with the conventional linear intercept approach, leading to the automated extraction of microstructural parameters. The work reports an exemplary analysis results based on the machine learning-assisted microstructure characterization in SOFC composite materials, implying that the machine learning-assisted approach becomes an essential tool in coping with high volume of electron microscopy-generated image data.

980 nm VCSEL illumination eliminates the organic additives and densifies the laminated multilayered SOFC NiO–YSZ|NiO–ScCeSZ|ScCeSZ|GDC|GDCscaffold in just 2.42 h compared to >100 h needed for the conventional thermal sintering process.

In this study, experiments were carried out to evaluate the seismic performance of a hybrid prestressed precast concrete (HPPC) system devised to ensure the integrity of precast concrete (PC) beam-column joints by introducing the post-tensioning method. A total of four full-scale HPPC beam-column joints were fabricated, and the presence of a topping slab, a post-tensioned length, and a type of tendon were set as the main variables. The quasi-static cyclic loading tests were performed to analyze the load-story drift ratio response, failure mode, ductility, residual deformation, and energy dissipation capacity of each specimen. The seismic performance of the specimen was then quantitatively evaluated based on the acceptance criteria of the ACI 374 report. The shear strength estimation model for the HPPC beam-column joints was also proposed and the proposed model was verified in detail using the test results.

In the previous studies, the energy-based damage model was developed by simulating cracked pipe fracture behaviour under seismic loading. The multi-axial fracture strain energy density, the parameter of energy-based damage model, was determined by standard tensile test data and monotonic cracked pipe test data. Very low cycle fatigue crack growth was simulated by applying the multi-axial fracture strain energy density under monotonic loading. When the previous energy-based damage model was applied to simulate cracked pipe fracture test under seismic loading, the simulated results were good agreement with experimental data under high load amplitude reverse cyclic loading and displacement controlled large scale cyclic loading. However, the conservative predicted results are shown in pipe test with low load amplitude and different load ratio. In this paper, the energy-based damage model was improved by incorporating the effect of load amplitude and load ratio on multi-axial fracture strain energy density. The cyclic multi-axial fracture strain energy density increased by depending on the load amplitude and load ratios. The improved damage model was applied by pipe fracture test under seismic loading with various load amplitudes and load ratios. The pipe tests were subjected by using circumferential through-wall crack (TWC) and surface crack (SC) pipe specimen. The seismic loading consisted of two load amplitudes and two load ratios. The simulated results were compared with experimental data and validated.

This paper proposes a simple method to predict the fracture toughness of hydrogen-embrittled material using a hydrogen-embrittlement constant. The effect of hydrogen-embrittlement on fracture strain can be quantified via hydrogen-embrittlement constant, and it can be obtained by simulating small punch test in hydrogen. The fracture toughness of hydrogen-embrittled material can be simply estimated by multiplying the constant on fracture toughness of the un-embrittled material. The result of estimation shows good agreement with the published experimental data.

In a post-tensioned precast concrete beam–column connection, the precast beams are post-tensioned to ensure the integrity of the connection and to provide recentring characteristics when the connection is subjected to seismic loads. In this study, the post-tensioned precast concrete beam–column connections were tested under reverse-cyclic loading with the test variables of grout strength at the beam–column interface and prestressing stress of stands. The test results showed that the post-tensioned precast concrete beam–column connections have a seismic performance equivalent to that of a conventional reinforced concrete beam–column connection. When high prestress is applied, however, confinement reinforcement is required at the beam ends to secure sufficient deformation capacity without crushing of concrete. An analysis model for post-tensioned precast concrete beam–column connections is also proposed; the model considers the bond properties of prestressing strands, and provides good agreement with the experimental results.

Titanium nitride (TiN) thin films grown by atomic layer deposition (ALD) have attracted considerable attention as electrode materials in semiconductor device applications, such as logic transistors...

A single source precursor containing both Hf and Si was employed with oxygen plasma to deposit hafnium silicate thin films using plasma-enhanced atomic layer deposition (PEALD). The self-limiting growth features were observed between 210 °C-280 °C with a constant growth rate of 1.45 A/cycle. The deposited hafnium silicate thin films were amorphous with a composition of Hf0.33Si0.66O2. The mechanism for the surface adsorption of the precursor was investigated by density functional theory (DFT) calculations, which show adsorption reactions of both Hf and Si atoms are viable. Bottom-gated InGaZnO channel thin-film transistors (TFTs) were fabricated to evaluate the electrical characteristics of PEALD hafnium silicate thin films as gate dielectrics. Compared to the conventional SiO2 gate dielectric, TFTs with PEALD hafnium silicate gate dielectric exhibited an improved switching performance, where the turn-on voltage, threshold voltage, saturation mobility, subthreshold slope, and on-off current ratio were −0.456 V, 3.202 V, 15.83 cm2/V·sec, 0.196, and 1.34 × 109, respectively.

Sn(II) dimethylamino-2-methyl-2-propoxy (Sn(dmamp)2) with water and hydrogen sulfide as oxygen and sulfur sources was employed in atomic layer deposition (ALD) of composite tin oxysulfide thin films abbreviated by Sn(O,S) made up of tin oxide (SnO) and tin sulfide (SnS) thin films employed as end members in addition to tin oxide and tin sulfide. Both SnO and SnS thin films demonstrate temperature-independent growth rates per cycle of 0.042nm/cycle and 0.056 nm/cycle, at 100–160 °C and 100–130 °C, respectively. Comparison of two tin-based thin film materials demonstrates dissimilar deposition features depending on the reactivity of the Sn precursors, i.e., Sn(dmamp)2 with anion sources provided by the water and hydrogen sulfide reactants. Density functional theory (DFT) calculations show that surface exchange reaction between *OH and *SH groups determine preference of S incorporation in the Sn(O,S) thin films. The material properties of ALD-based SnO, SnS, and Sn(O,S) thin films were characterized in terms of composition, stoichiometry, crystallinity, band structure, and electronic properties, demonstrating the potential of ALD SnO and SnS as p-type channel materials for transparent electronics.

Metal thin films have been widely used as conductors in semiconductor devices for several decades. However, the resistivity of metal thin films such as Cu and TiN increases substantially (1000%) as they become thinner (10 nm) when using high-density integration to improve device performance. In this study, the resistivities of MAX-phase V

To predict fracture behavior for ductile materials, some ductile fracture simulation methods different from classical approaches have been investigated based on appropriate models of ductile fracture. For the future use of the methods to overcome restrictions of classical approaches, the applicability to the actual components is of concern. In this study, two benchmark problems on the fracture tests supposing actual components were provided to investigate the prediction ability of simulation methods containing parameter decisions. One was the circumferentially through-wall and surface cracked pipes subjected to monotonic bending, and the other was the circumferentially through-wall cracked pipes subjected to cyclic bending. Participants predicted the ductile crack propagation behavior by their own approaches, including finite element method (FEM) employed Gurson–Tvergaard–Needleman (GTN) yielding function with void ratio criterion, are FEM employed GTN yielding function, FEM with fracture strain or energy criterion modified by stress triaxiality, extended FEM with J or ΔJ criterion, FEM with stress triaxiality and plastic strain based ductile crack propagation using FEM, and elastic-plastic peridynamics. Both the deformation and the crack propagation behaviors for monotonic bending were well reproduced, while few participants reproduced those for cyclic bending. To reproduce pipe deformation and fracture behaviors, most of the groups needed parameters that were determined to reproduce pipe deformation and fracture behaviors in benchmark problems themselves and it is still difficult to reproduce them by using parameters only from basic materials tests.

A high-performance machine learning-assisted gas sensor strategy based on the integration of supervised and unsupervised learning with a gas-sensitive semiconductor metal oxide (SMO) gas sensor array is introduced. A 4-SMO sensor array was chosen as a test sensor system for detecting carbon monoxide (CO) and ethyl alcohol (C2H5OH) mixtures using 15 different combinations. Gas sensing detection/classification was performed with different numbers of gas sensor and machine learning algorithms. K-Means clustering was successfully employed to rationally identify the similarity features of targeted gases among 4 different groups, i.e., matrix gas, two single-component gases, and one two-gas mixture, based on only unlabeled voltage-based gas sensing information. Detailed classification was performed through a multitude of supervised algorithms, i.e., 2-layer artificial neural networks (ANNs), 4-layer deep neural networks (DNNs), 1-dimensional convolutional neural networks (1D CNNs), and 2-dimensional CNNs (2D CNNs). The numerical-based DNNs and image-based CNNs are shown to be excellent approaches for gas detection and classification, as indicated by the highest accuracy and lowest loss indicators. Through the analysis of the influence of the number of sensors on the arrayed gas sensor system, the application of machine learning methodology to an arrayed gas sensor system demonstrates four unique features, i.e., a data augmentation methodology, machine learning approach of combining K-means clustering and neural networks, and a systematic approach to optimized sensor combinations, potentially leading to the practical sensor networks based on chemical sensors. Even two SMO sensor combinations are shown to be highly effective in gas discrimination against diverse gas environments assisted through numeric-based DNNs and image-based 1D CNNs, overcoming the simple clustering proposed through the unsupervised K-means clustering.

We report enhancement of mobility and increase in mobile carrier concentration in zinc oxide (ZnO) nanocrystal (NC) field effect transistors (FETs) through the formation of a homo-junction interface using atomic layer deposition (ALD) passivation. An ultrathin ALD-ZnO passivation film deposited on a ZnO NC film not only increased the FET mobility from 4.6ⅹ10 −6 to 1.4ⅹ10 −4 cm 2 /V but also caused earlier turn-on of the ZnO NC FETs, shifting the threshold voltage from 18.9 to −4.6 V. The enhanced FET mobility and earlier turn-on in the FET are attributed to reduced localized state density on the ZnO NC surface through ALD-ZnO passivation. Passivation of the surface states mitigates carrier depletion in the ZnO NC film through oxygen adsorption on the ZnO surface. We also observed that the presence of saturation of the drain in a high drain-source voltage region depends on the ALD-ZnO passivation and its origin is discussed.

Deep learning and network science are applied in a synergistic manner to address structural crack issues with the aim of providing the characteristic features of crack generation and a quantitative description of crack networks in natural materials. Loess/water mixtures were chosen as a model system due to the facile formation of cracks resulting from water evaporation. Deep learning algorithms are applied to the detection and classification of edges and nodes in cracks forming in the drying stage of the loess/water mixture system. Deep learning is shown to effectively detect and classify cracks in terms of nodes and edges. Based on the guided information on nodes and edges, cracks were subject to a connectivity analysis with network science.The combined deep learning/network science approach is proven to be suitable for understanding crack formation and propagation in both qualitative and quantitative aspects.

In this paper, we present techniques for automatically classifying players and tracking ball movements in basketball game video clips under poor conditions, where the camera angle dynamically shifts and changes. In the core of our system lies Yolo, a realtime object detection system. Given the ground truth boxes collected by our data specialists, Yolo is trained to detect the presence of objects in every video frame. In addition, Yolo uses Darknet that implements convolution neural networks to classify a detected object to a player and to recognize its jersey numbers of specific movements. By identifying players and ball possessions, we can automatically compute ball distributions that are reflected on complex networks. With original Yolo system, player movement can be interrupted, when the players move out of the frame due to camera shift and when players overlap each other on a two-dimensional frame. We have adapted Yolo to keep track of players even under such poor condition by considering contextual information available from the framework preceding and/or succeeding problematic video frames. In addition to the novel movement inference method, we provide a framework for analyzing the pass networks in various perspectives to help the managing staff to reveal critical determinants of team performance and to design better game strategies. We assess the performance of our system in terms of accuracy by making a comparison with the analytical reports generated by human experts.

This paper explores the use of passive, stereo sensing for vision-based navigation. The traditional approach uses dense depth algorithms, which can be computationally costly or potentially inaccurate. These drawbacks compound when including the additional computational demands associated to the sensor fusion, collision checking, and path planning modules that interpret the dense depth measurements. These problems can be avoided through the use of the stixel representation, a compact and sparse visual representation for local free-space. When integrated into a Planning in Perception Space based hierarchical navigation framework, stixels permit fast and scalable navigation for different robot geometries. Computational studies quantify the processing performance and demonstrate the favorable scaling properties over comparable dense depth methods. Navigation benchmarking demonstrates more consistent performance across high and low performance compute hardware for PiPS-based stixel navigation versus traditional hierarchical navigation.

Zirconium oxides were deposited using plasma-enhanced atomic layer deposition (PEALD) involving (2-(N-methylamino)1-MethylEthyleneCyclopentadienyl)Bis(DiMethylAmino)Zr (abbreviated as CMEN-Zr) and oxygen plasma as zirconium and oxygen sources. The zirconium oxide thin films demonstrate temperature-independent growth rates per cycle of 0.94 A/cycle at 150–215 °C. The deposited ZrO2 thin films were characterized using numerous analytical tools, i.e., X-ray photoelectron spectroscopy for chemical bonding state and composition, X-ray diffraction for crystallinity, atomic force microscopy for surface morphology, field-emission scanning electron microscopy for cross-sectional analysis, spectroscopic ellipsometry and UV–visible spectrophotometry for optical characterization, capacitance–voltage measurements for dielectric constants and atomic defects, and current–voltage characteristics for electrical information. The insulating features of the crystalline and stoichiometric ZrO2 films were implemented in the anode composites to evaluate the influence of ALD-based nano-features on the electrochemical performance of solid oxide fuel cells, with the main emphasis on anode performance. The presence of nanomaterials on Ni/YSZ anode composites is analyzed to determine the negative effects on electrochemical performance and the degradation of cell performance of solid oxide fuel cells (SOFCs). The artificial design was proven to be effective in controlling the cell performance as long as proper material design was adopted in SOFC electrodes.

In this work, loess-based materials were designed based on a multicomponent composite materials system for ecofriendly natural three-dimensional (3D) printing involving quick lime, gypsum, and water. The 3D printing process was monitored as a function of gypsum content, in terms of mechanical strength and electrical resistance, in the cube-shaped bulk form. After initial optimization, the 3D printing composition was refined to provide improved printability in a 3D printing system. The optimal 3D fabrication allowed for reproducible printing of rectangular columns and cubes. The development of 3D printing materials was scrutinized using a multitude of physicochemical probing tools, including X-ray diffraction for phase identification, impedance spectroscopy to monitor setting behaviors, and mercury intrusion porosimetry to extract the pore structure of loess-based composite materials. Additionally, the setting behavior in the loess-based composite materials was analyzed by investigating the formation of gypsum hydrates induced by chemical reaction between quick lime and water. This setting reaction provides reasonable mechanical strength that is sufficient to print loess-based pastes via 3D printing. Such mechanical strength allows utilization of robotic 3D printing applications that can be used to fabricate ecofriendly structures.

Machine learning was applied to classify the device characteristics of indium gallium zinc oxide (IGZO) thin-film transistors (TFTs). A K-means approach was employed for initial clustering of IGZO transfer curves into three of four grades (high, medium-high, medium, and low) of TFT performance according to qualitative features. A 2-layered artificial neural network (ANN) and 4-layered deep neural network (DNN) were used to extract mobility, threshold voltage, on/off current ratio, and sub-threshold slope device parameters from high-grade and medium-high-grade oxide TFTs. Ground-truth device parameters were calculated using in-house codes based on a rules-based approach consistent with the definitions employed to train the ANN and DNN. The DNN-predicted parameters were in closer agreement with manual and macro-based calculations than were those obtained from the ANN. Synergistic integration of K-means clustering and DNN effectively extracted TFT device parameters encountered in processing high volumes of data in industrial and academic domains of the microelectronics field.

In recent years, a variety of strengthening methods have been developed to improve the seismic performance of reinforced concrete (RC) frame structures with non-seismic details. In this regard, this study proposes a new type of seismic strengthening method that compresses prefabricated precast concrete (PC) walls from the outside of a building. In order to verify the proposed method, a RC frame structure strengthened with precast walls was fabricated, and cyclic loading tests were performed. The results showed that specimens strengthened using the proposed method exhibited further improvements in strength, stiffness and energy dissipation capacity, compared to RC frame structures with non-seismic details. In addition, a nonlinear analysis method, capable of considering the flexural compression and shear behaviors of the walls, was suggested to analytically evaluate the structural behavior of the frame structures strengthened by the proposed method. Using this, an analysis model for frame structures strengthened with precast walls was proposed. Through the proposed model, the analysis and test results were compared in relation to stiffness, strength, and energy dissipation capacity. Then, the failure mode of the column was evaluated based on the pushover analysis. In addition, this study proposed a simplified analysis model that considered the placement of longitudinal reinforcements in shear walls.

비부착 긴장재가 적용된 프리캐스트콘크리트(precast concrete, PC) 보-기둥 접합부는 PC 구조의 내진성능 및 셀프센터링 효과를 증진시킬 수 있다. 이 연구에서는 프리스트레스 크기를 변수로 한 PC 보-기둥 접합부 실험체를 제작하여 반복하중실험을 수행하였다. 비부착긴장재가 적용된 PC 보-기둥 접합부는 RC(reinforced concrete) 접합부에 비하여 핀칭효과가 적게 나타났으며, 보 단부의 압괴에 의해 다소 이른 시점에서 하중이 저하된 것을 제외하고는 ACI 374 Report에서 제시하는 접합부 내진성능 허용기준을 모두 만족하였다. 또한, 이 연구에서는 두 가지 모델링 방법을 이용하여 실험체의 유한요소해석을 수행하였으며, 해석결과로부터 보-기둥요소 및 트러스요소를 이용한 모델링 방법이 보다 적합한 것으로 나타났다.

Nickel(II) 1-dimethylamino-2-methyl-2-butoxide (Ni(dmamb)2) with water and hydrogen sulfide as oxygen and sulfur sources was employed in atomic layer deposition (ALD) of nickel oxide (NiO) and nickel sulfide (NiS) thin films. Both NiO and NiS thin films demonstrate temperature-independent growth rates per cycle of 0.128 nm/cycle and 0.0765 nm/cycle, at 130–150 °C and 80–160 °C, respectively. Comparison of two nickel-based thin film materials demonstrates dissimilar deposition features depending on the reactivity of the Ni precursor, i.e., Ni(dmamb)2 with anion sources provided by the water and hydrogen sulfide reactants. Difference in reactivity observed for NiO and NiS ALD processes is further investigated by density functional theory (DFT) simulations of surface reactions, which indicated that H2S demonstrate higher reactivity with surface-adsorbed Ni precursor than H2O. The material properties of ALD NiO and NiS thin films including stoichiometry, crystallinity, band structure, and electronic properties were analyzed by multiple experimental techniques, showing potential of ALD NiS as electrode or catalyst for energy-oriented devices.

This study investigated the pull-out resistance of superelastic shape memory alloy (SMA) short fibers in mortar with consideration of various end-anchorages that provide different anchoring actions. For the purpose, four types of SMA fibers were prepared using NiTi SMA wires with a diameter of 1.0 mm and the following four end shapes: straight (ST), L-shaped (LS), N-shaped (NS), and spearhead-shaped (SH). The straight-ended fiber was a reference with no working on the end, and the fiber with the spearhead-shaped end was crimped to make the end part flat. The fibers with L- and N-shaped ends were bent with single or double bending. The results showed that only the spearhead-shaped fibers showed self-centering behavior because of the superelasticity of the SMA after slip occurred. This paper discusses the reasons that the ST, LS, and NS fibers do not show self-centering behavior and proposes a concept to induce superelastic behavior in SMA fibers in mortar or concrete.

In modern high-rise reinforced concrete buildings, high strength concrete (HSC) has been typically used for column members, while normal strength concrete (NSC) has been generally used for floor slabs. This study proposed a new analytic approach that can reasonably estimate the effective compressive strengths of corner and exterior column members intersected by lower strength concrete slabs. The proposed model was theoretically derived based on strain distributions and constraint conditions at an interface between column and slab members. In addition, the compressive strength test results of isolated, exterior, and corner columns intersected by lower strength concrete slabs reported in the existing literature were compared to the effective compressive strengths estimated by the proposed model. The proposed model provided good accuracy on the effective compressive strengths of the column members intersected by slabs cast with lower concrete compressive strengths. It was also shown that the proposed model successfully reflects the effects of the aspect ratio between the slab thickness and the column width on the effective compressive strengths of the test specimens and their failure modes that changed significantly according to the column–slab compressive strength ratio.

Amorphous In-Ga-Zn-O experiences device instability in thin-film transistors (TFTs) due to the vulnerability of oxide channel materials under ambient conditions. Nitrogenation of In-Ga-Zn-O TFTs by partial oxygen substation of nitrogen was performed to enhance stability. The nitrogenated In-Ga-Zn-O thin films were deposited under plasma conditions involving nitrogen gas molecules. The TFT stability was tested under various conditions of temperature, illumination, time, and sweeping. Despite the minor weakness at elevated operation temperature, the nitrogenated In-Ga-Zn-O TFTs exhibited superior tolerance against sweeping operations in TFT devices, illumination, and time-dependent degradation. The improved stability is attributed to the incorporation of nitrogen into In-Ga-Zn-O via oxygen vacancy and its corresponding interface trap improvement.

This study investigated the effects of superelastic shape memory alloy (SMA) fibers end-shape on pullout resistance through hysteretic pullout testing. Superelastic NiTi SMA wire of 1.0 mm diameter was employed in manufacturing short fibers. SMAs were fabricated with four end-shapes: 1) prismatic and straight end, 2) L-shaped end, 3) N-shaped end, and 4) crimped end with a spearhead. The embedded length of a fiber into the mortar matrix having compressive strength of 50.0 MPa was 18.0 mm, except for the N-shaped end fiber for which the embedded length was 21.0 mm. The pullout test was conducted with displacement control to obtain hysteretic pullout behavior by four cycling loadings. The results showed that crimped-end fibers considerably magnified the pullout strength, and the deformation was recovered by their superelastic behavior. As N-shaped end fibers could not show flag-shaped behavior, another test was performed through which fibers were annealed to induce superelasticity, so, a perfect strain recovery was obtained. Results showed that additional annealing of the N-shaped fibers after the manufacturing improved superelastic behavior at the bended part.

Complementary hybridization of ZnO thin films with aluminum tetraphenylporphyrin (Al(III)TPP), zinc tetraphenylporphyrin (Zn(II)TPP) and H2TPP (tetraphenylporphyrin) was adopted for tuning the hybrid thin film transistor (TFT) performance and improving their flexibility. After the hybridization with the organic layers, the chemical and structural features of ZnO thin films were well-preserved as compared with those of solely ZnO thin films. The existence of organic layers was monitored by X-ray photoelectron spectroscopy depth profiling. We fabricated the TFT based on ZnO/organic layers, resulting in the on-off ratio and threshold voltage of the devices manipulated by selecting the organic layers. These results can be understood by the performance tuning mechanisms related with the electron charge transfer induced by a work function difference. Remarkably, a significant improvement of the flexibility in the hybrid films was achieved without any significant loss in optical transmittance, which will be high demand in transparent and flexible electronics.