Emerging Trends in Advanced Materials and Technologies for Sustainable Energy Storage

Dear Colleagues,

This multidisciplinary Topic focuses on the latest advances in energy storage technologies, with a specific emphasis on high energy density and high power density, safety, recycling, and the utilization of advanced in situ characterization tools and data-driven approaches. As the demand for efficient and sustainable energy storage solutions continues to grow, it is crucial to explore advancements in energy storage technologies and develop strategies to address safety concerns and enable effective recycling processes.

The multidisciplinary Topic encompasses a wide range of materials, chemistries, and interfaces for lithium-ion batteries (LiBs), lithium metal batteries (LMBs), hybrid supercapacitors, and alternative battery systems such as sodium (Na), potassium (K), aluminum (Al), magnesium (Mg), and other ion- or element-based batteries. These technologies offer the potential for higher energy and power densities, enabling the development of more efficient and powerful energy storage systems.

Additionally, the multidisciplinary Topic highlights the importance of recycling in the context of energy storage technologies. With the growing number of batteries reaching the end of their life cycles, the development of effective and sustainable recycling processes is critical to minimize environmental impact and recover valuable materials. Contributions discussing recycling strategies, methods for materials recovery, and life cycle assessments are encouraged.

Furthermore, the multidisciplinary Topic promotes the use of advanced in situ characterization tools and data-driven approaches to enhance the understanding and advancement of next-generation energy storage systems. By leveraging real-time and high-resolution characterization techniques, researchers can gain valuable insights into battery materials, interfaces, and electrochemical processes. Data-driven approaches, including machine learning and computational modeling, can aid in the design and optimization of energy storage materials and devices.

Prof. Dr. Hongtao Sun
Dr. Jian Zhu
Prof. Dr. Junfei Liang
Topic Editors