Topologically protected oxygen redox in a layered manganese oxide cathode for sustainable batteries

Manganese could be the element of choice for cathode materials used in large-scale energy storage systems owning to its abundance and low toxicity levels. However, both lithium and sodium ion batteries adopting this electrode chemistry suffer from rapid performance fading, suggesting a major technical barrier that must be overcome. Here we report a P3-type layered manganese oxide cathode Na 0.6 Li 0.2 Mn 0.8 O 2 (NLMO) that delivers a high capacity of 240 mAh g −1 with outstanding cycling stability in a lithium half-cell. Combined experimental and theoretical characterizations reveal a characteristic topological feature that enables the good electrochemical performance. Specifically, the-α-γ-layer stacking provides topological protection for lattice oxygen redox, whereas the reversibility is absent in P2-structured NLMO which takes a-α-β-configuration. The identified new order parameter opens an avenue towards the rational design of reversible Mn-rich cathode materials for sustainable batteries

Extreme Learning Machines [Trends & Controversies]

This special issue includes eight original works that detail the further developments of ELMs in theories, applications, and hardware implementation. In "Representational Learning with ELMs for Big Data," Liyanaarachchi Lekamalage Chamara Kasun, Hongming Zhou, Guang-Bin Huang, and Chi Man Vong propose using the ELM as an auto-encoder for learning feature representations using singular values. In "A Secure and Practical Mechanism for Outsourcing ELMs in Cloud Computing," Jiarun Lin, Jianping Yin, Zhiping Cai, Qiang Liu, Kuan Li, and Victor C.M. Leung propose a method for handling large data applications by outsourcing to the cloud that would dramatically reduce ELM training time. In "ELM-Guided Memetic Computation for Vehicle Routing," Liang Feng, Yew-Soon Ong, and Meng-Hiot Lim consider the ELM as an engine for automating the encapsulation of knowledge memes from past problem-solving experiences. In "ELMVIS: A Nonlinear Visualization Technique Using Random Permutations and ELMs," Anton Akusok, Amaury Lendasse, Rui Nian, and Yoan Miche propose an ELM method for data visualization based on random permutations to map original data and their corresponding visualization points. In "Combining ELMs with Random Projections," Paolo Gastaldo, Rodolfo Zunino, Erik Cambria, and Sergio Decherchi analyze the relationships between ELM feature-mapping schemas and the paradigm of random projections. In "Reduced ELMs for Causal Relation Extraction from Unstructured Text," Xuefeng Yang and Kezhi Mao propose combining ELMs with neuron selection to optimize the neural network architecture and improve the ELM ensemble's computational efficiency. In "A System for Signature Verification Based on Horizontal and Vertical Components in Hand Gestures," Beom-Seok Oh, Jehyoung Jeon, Kar-Ann Toh, Andrew Beng Jin Teoh, and Jaihie Kim propose a novel paradigm for hand signature biometry- for touchless applications without the need for handheld devices. Finally, in "An Adaptive and Iterative Online Sequential ELM-Based Multi-Degree-of-Freedom Gesture Recognition System," Hanchao Yu, Yiqiang Chen, Junfa Liu, and Guang-Bin Huang propose an online sequential ELM-based efficient gesture recognition algorithm for touchless human-machine interaction