Rechargeable magnesium batteries have lately received great attention for large-scale energy storage systems due to their high volumetric capacities, low materials cost, and safe characteristic. However, the bivalency of Mg(2+) ions has made it challenging to find cathode materials operating at high voltages with decent (de)intercalation kinetics. In an effort to overcome this challenge, we adopt an unconventional approach of engaging crystal water in the layered structure of Birnessite MnO2 because the crystal water can effectively screen electrostatic interactions between Mg(2+) ions and the host anions. The crucial role of the crystal water was revealed by directly visualizing its presence and dynamic rearrangement using scanning transmission electron microscopy (STEM). Moreover, the importance of lowering desolvation energy penalty at the cathode-electrolyte interface was elucidated by working with water containing nonaqueous electrolytes. In aqueous electrolytes, the decreased interfacial energy penalty by hydration of Mg(2+) allows Birnessite MnO2 to achieve a large reversible capacity (231.1 mAh g(-1)) at high operating voltage (2.8 V vs Mg/Mg(2+)) with excellent cycle life (62.5% retention after 10000 cycles), unveiling the importance of effective charge shielding in the host and facile Mg(2+) ions transfer through the cathode's interface.

The phase transition of layered manganese oxides to spinel phases is a well-known phenomenon in rechargeable batteries and is the main origin of the capacity fading in these materials. This spontaneous phase transition is associated with the intrinsic properties of manganese, such as its size, preferred crystal positions, and reaction characteristics, and it is therefore very difficult to avoid. The introduction of crystal water by an electrochemical process enables the inverse phase transition from spinel to a layered Birnessite structure. Scanning transmission electron microscopy can be used to directly visualize the rearrangement of lattice atoms, the simultaneous insertion of crystal water, the formation of a transient structure at the phase boundary, and layer-by-layer progression of the phase transition from the edge. This research indicates that crystal water intercalation can reverse phase transformation with thermodynamically favored directionality.

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Faults in electrical facilities may cause severe damages, such as the electrocution of maintenance personnel, which could be fatal, or a power outage. To detect electrical faults safely, electricians disconnect the power or use heavy equipment during the procedure, thereby interrupting the power supply and wasting time and money. Therefore, detecting faults with remote approaches has become important in the sustainable maintenance of electrical facilities. With technological advances, methodologies for machine diagnostics have evolved from manual procedures to vibration-based signal analysis. Although vibration-based prognostics have shown fine results, various limitations remain, such as the necessity of direct contact, inability to detect heat deterioration, contamination with noise signals, and high computation costs. For sustainable and reliable operation, an infrared thermal (IRT) image detection method is proposed in this work. The IRT image technique is used in various engineering fields for diagnosis because of its non-contact, safe, and highly reliable heat detection technology. To explore the possibility of using the IRT image-based fault detection approach, object detection algorithms (Faster R-CNN; Faster Region-based Convolutional Neural Network, YOLOv3; You Only Look Once version 3) are trained using 16,843 IRT images from power distribution facilities. A thermal camera expert from Korea Hydro & Nuclear Power Corporation (KHNP) takes pictures of the facilities regarding various conditions, such as the background of the image, surface status of the objects, and weather conditions. The detected objects are diagnosed through a thermal intensity area analysis (TIAA). The faster R-CNN approach shows better accuracy, with a 63.9% mean average precision (mAP) compared with a 49.4% mAP for YOLOv3. Hence, in this study, the Faster R-CNN model is selected for remote fault detection in electrical facilities.