Next-generation Rechargeable Batteries Utilizing Ionic Liquids and Various Charge Carriers

Renewable energy resources and rechargeable batteries are key to establishing a carbon-neutral society. Lithium-ion batteries (LIBs) have been widely used in portable electronic devices for the past 30 years. However, the further spread of large-scale batteries is essential in the household and industrial sectors, which drives the research and development of technologies beyond LIBs. Since ionic liquids are safe and confer unique physicochemical properties, several next-generation batteries utilizing ionic liquid electrolytes have been researched. Sodium-ion and potassium-ion batteries show promise in overcoming the potential problems of LIBs related to the uneven distribution of lithium and cobalt resources. Fluoride-shuttle batteries deliver significantly higher theoretical energy densities compared to current LIBs. Nevertheless, many issues remain unresolved for the practical application of these batteries. This comprehensive paper provides several research topics on next-generation rechargeable batteries utilizing ionic liquids and various charge carriers, unveiling their novelty, the issues to be solved, and future research directions

Amide‐Based Ionic Liquid Electrolytes for Alkali‐Metal‐Ion Rechargeable Batteries

Ionic liquids (ILs) have a wide variety of applications in energy storage and material production. ILs are composed of only cations and anions, without any molecular solvents, and are generally known as “designer liquids (solvents)” because their physicochemical properties can be tuned by the combination of ionic species. In recent several decades, research and development activities of rechargeable batteries have garnered considerable attention because certain groups of ILs exhibit high electrochemical stability and moderate ionic conductivity, rendering them suitable for application in high-voltage batteries. ILs with amide anions are representative electrolytes and are extensively researched by many research groups, including our group. This paper focuses on amide-based ILs as electrolytes for alkali-metal-ion rechargeable batteries, introducing their history, characteristics, and existing challenges to be addressed

A Face-like Structure Detection on Planet and Satellite Surfaces using Image Processing

This paper demonstrates that face-like structures are everywhere, and can be de-tected automatically even with computers. Huge amount of satellite images of the Earth, the Moon, the Mars are explored and many interesting face-like structure are detected. Throughout this fact, we believe that science and technologies can alert people not to easily become an occultist.Comment: 4 page

Deep Reinforcement Learning-Based Channel Allocation for Wireless LANs with Graph Convolutional Networks

Last year, IEEE 802.11 Extremely High Throughput Study Group (EHT Study Group) was established to initiate discussions on new IEEE 802.11 features. Coordinated control methods of the access points (APs) in the wireless local area networks (WLANs) are discussed in EHT Study Group. The present study proposes a deep reinforcement learning-based channel allocation scheme using graph convolutional networks (GCNs). As a deep reinforcement learning method, we use a well-known method double deep Q-network. In densely deployed WLANs, the number of the available topologies of APs is extremely high, and thus we extract the features of the topological structures based on GCNs. We apply GCNs to a contention graph where APs within their carrier sensing ranges are connected to extract the features of carrier sensing relationships. Additionally, to improve the learning speed especially in an early stage of learning, we employ a game theory-based method to collect the training data independently of the neural network model. The simulation results indicate that the proposed method can appropriately control the channels when compared to extant methods