Special Issue: Can Structural Chemistry Point the Way: Exploring the Relevance between Structure and Properties

Feng Pan

Citation:  Feng Pan. Special Issue: Can Structural Chemistry Point the Way: Exploring the Relevance between Structure and Properties[J]. Chinese Journal of Structural Chemistry, 2020, 39(1): 1-1. shu

Special Issue: Can Structural Chemistry Point the Way: Exploring the Relevance between Structure and Properties

English

  • Based upon intermolecular/ionic relationships, the structures of functional materials generally have an enormous impact on their properties. In 2019, John B. Goodenough won Nobel Prize in Chemistry for discovering the excellent electrochemical properties of layer-structured LixCoO2 and olivine-structured LixFePO4 as cathode electrode materials for lithium-ion batteries (LIBs), which have fundamentally changed our society. Therefore, in the year of 2020, we would like to focus on exploring the relationship between material structure and function, which is of great importance for the prediction and design of advanced materials with desirable properties.

    In this issue, some authoritative experts who have long-term experience in material science are invited to share their experiences and opinions on the current and future development of advanced battery materials. They are Dr. Khalil Amine from Argonne National Laboratory, Prof. Yi Cui from Stanford University, Prof. Huakun Liu from University of Wollonggong, Prof. Shi-Gang Sun from Xiamen University and Prof. Qiang Zhang from Tsinghua University. Dr. Amine mainly focuses on recent progress and future prospect on advanced concentration gradient cathode materials. Prof. Yi Cui summarizes the alloying chemistry as well as the structural design strategies of next-generation silicon anode materials. Prof. Huakun Liu summarizes a variety of MnO2 materials with different phases as the hosts for Zn-ion batteries. Prof. Shi-Gang Sun describes the challenges and recent progress of high-capacity Li-rich Mn-based cathodes from a fundamental perspective. Prof. Qiang Zhang introduces an unsupervised machine learning research for discovering lithium ionic conductors by screening and clustering lithium compounds.


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  • 发布日期:  2020-01-01
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