Interface Control of Ferroelectricity in an SrRuO3/BaTiO3/SrRuO3 Capacitor and its Critical Thickness

The atomic‐scale synthesis of artificial oxide heterostructures offers new opportunities to create novel states that do not occur in nature. The main challenge related to synthesizing these structures is obtaining atomically sharp interfaces with designed termination sequences. In this study, it is demonstrated that the oxygen pressure during growth plays an important role in controlling the interfacial terminations of SrRuO₃/BaTiO₃/SrRuO₃ (SRO/BTO/SRO) ferroelectric (FE) capacitors. The SRO/BTO/SRO heterostructures are grown by a pulsed laser deposition method. The top SRO/BTO interface, grown at high (around 150 mTorr), usually exhibits a mixture of RuO₂–BaO and SrO–TiO₂ terminations. By reducing , the authors obtain atomically sharp SRO/BTO top interfaces with uniform SrO–TiO₂ termination. Using capacitor devices with symmetric and uniform interfacial termination, it is demonstrated for the first time that the FE critical thickness can reach the theoretical limit of 3.5 unit cells.     

Fabrication of Millimeter‐Long Carbon Tubular Nanostructures Using the Self‐Rolling Process Inherent in Elastic Protein Layers

Millimeter‐long conducting fibers can be fabricated from carbon nanomaterials via a simple method involving the release of a prestrained protein layer. This study shows how a self‐rolling process initiated by polymerization of a micropatterned layer of fibronectin (FN) results in the production of carbon nanomaterial‐based microtubular fibers. The process begins with deposition of carbon nanotube (CNT) or graphene oxide (GO) particles on the FN layer. Before polymerization, particles are discrete and nonconducting, but after polymerization the carbon materials become entangled to form an interconnected conducting network clad by FN. Selective removal of FN using high‐temperature combustion yields freestanding CNT or reduced GO microtubular fibers. The properties of these fibers are characterized using atomic force microscopy and Raman spectroscopy. The data suggest that this method may provide a ready route to rapid design and fabrication of aligned biohybrid nanomaterials potentially useful for future electronic applications.     

A Fluid Liquid‐Crystal Material with Highly Polar Order

An anomalously large dielectric permittivity of ≈10⁴ is found in the mesophase temperature range (MP phase) wherein high fluidity is observed for a liquid‐crystal compound having a 1,3‐dioxane unit in the mesogenic core (DIO). In this temperature range, no sharp X‐ray diffraction peak is observed at both small and wide Bragg angles, similar to that for a nematic phase; however, an inhomogeneous sandy texture or broken Schlieren one is observed via polarizing optical microscopy, unlike that for a conventional nematic phase. DIO exhibits polarization switching with a large polarization value, i.e., P = 4.4 µC cm^?2, and a parallelogram‐shaped polarization–electric field hysteresis loop in the MP phase. The inhomogeneously aligned DIO in the absence of an electric field adopts a uniform orientation along an applied electric field when field‐induced polarization switching occurs. Furthermore, sufficiently larger second‐harmonic generation is observed for DIO in the MP phase. Second‐harmonic‐generation interferometry clearly shows that the sense of polarization is inverted when the +/? sign of the applied electric field in MP is reversed. These results suggest that a unidirectional, ferroelectric‐like parallel polar arrangement of the molecules is generated along the director in the MP phase.     

Spatial arrangement using deep reinforcement learning to minimise rearrangement in ship block stockyards

As the shipbuilding industry is an engineering-to-order industry, different types of products are manufactured according to customer requests, and each product goes through different processes and workshops. During the shipbuilding process, if the product is not able to go directly to the subsequent process due to physical constraints of workshop, it temporarily waits in a stockyard. Since the waiting process involves unpredictable circumstances, plans regarding time and space cannot be established in advance. Therefore, unnecessary movement often occurs when ship blocks enter or depart from the stockyard. In this study, a reinforcement learning approach was proposed to minimise rearrangement in such circumstances. For this purpose, an environment in which blocks are arranged and rearranged was defined. Rewards based on the simplified rules were logically defined, and simulation was performed for quantitative evaluation using the proposed reinforcement learning algorithm. This algorithm was verified using an example model derived from actual data from a shipyard. The method proposed in this study can be used not only to the arrangement problem of ship block stockyards but also to the various arrangement and allocation problems or logistics problems in the manufacturing industry.     

Sensitive Wearable Temperature Sensor with Seamless Monolithic Integration

Accurate temperature field measurement provides critical information in many scientific problems. Herein, a new paradigm for highly sensitive, flexible, negative temperature coefficient (NTC) thermistor-based artificial skin is reported, with the highest temperature sensing ability reported to date among previously reported NTC thermistors. This artificial skin is achieved through the development of a novel monolithic laser-induced reductive sintering scheme and unique monolithic structures. The unique seamless monolithic structure simultaneously integrates two different components (a metal electrode and metal oxide sensing channel) from the same material at ambient pressure, which cannot be achieved by conventional heterogeneous integration through multiple, complex steps of photolithography or vacuum deposition. In addition to superior performance, electronic skin with high temperature sensitivity can be fabricated on heat-sensitive polymer substrates due to the low-temperature requirements of the process. As a proof of concept, temperature-sensitive artificial skin is tested with conformally attachable physiological temperature sensor arrays in the measurement of the temperatures of exhaled breath for the early detection of pathogenic progression in the respiratory system. The proposed highly sensitive flexible temperature sensor and monolithic selective laser reductive sintering are expected to greatly contribute to the development of essential components in various emerging research fields, including soft robotics and healthcare systems.     

Advanced Materials and Systems for Biodegradable,Transient Electronics

Transient electronics refers to an emerging class of advanced technology, defined by an ability to chemically or physically dissolve, disintegrate, and degrade in actively or passively controlled fashions to leave environmentally and physiologically harmless by-products in environments, particularly in bio-fluids or aqueous solutions. The unusual properties that are opposite to operational modes in conventional electronics for a nearly infinite time frame offer unprecedented opportunities in research areas of eco-friendly electronics, temporary biomedical implants, data-secure hardware systems, and others. This review highlights the developments of transient electronics, including materials, manufacturing strategies, electronic components, and transient kinetics, along with various potential applications.     

Defect-Detection Model for Underground Parking Lots Using Image Object-Detection Method

The demand for defect diagnoses is gradually gaining ground owing to the growing necessity to implement safe inspection methods to ensure the durability and quality of structures. However, conventional manpower-based inspection methods not only incur considerable cost and time, but also cause frequent disputes regarding defects owing to poor inspections. Therefore, the demand for an effective and efficient defect-diagnosis model for concrete structures is imminent, as the reduction in maintenance costs is significant from a long-term perspective. Thus, this paper proposes a deep learning-based image object-identification method to detect the defects of paint peeling, leakage peeling, and leakage traces that mostly occur in underground parking lots made of concrete structures. The deep learning-based object-detection method can replace conventional visual inspection methods. A faster region-based convolutional neural network (R-CNN) model was used with a training dataset of 6,281 images that utilized a region proposal network to objectively localize the regions of interest and detect the surface defects. The defects were classified according to their type, and the learning of each exclusive model was ensured through test sets obtained from real underground parking lots. As a result, average precision scores of 37.76%, 36.42%, and 61.29% were obtained for paint peeling, leakage peeling, and leakage trace defects, respectively. Thus, this study verified the performance of the faster RCNN-based defect-detection algorithm along with its applicability to underground parking lots.