Novel ZnMn(2)O(4) ball-in-ball hollow microspheres are fabricated by a facile two-step method involving the solution synthesis of ZnMn-glycolate hollow microspheres and subsequent thermal annealing in air. When evaluated as an anode material for lithium-ion batteries, these ZnMn(2)O(4) ball-in-ball hollow microspheres show significantly enhanced electrochemical performance with high capacity, excellent cycling stability and good rate capability.

In this work, we have successfully grown single-crystalline nanoneedle arrays of NiCo 2 O 4 on conductive substrates such as Ni foam and Ti foil through a simple solution method together with a post-annealing treatment. Remarkably, the NiCo 2 O 4 -Ni foam binder-free electrode exhibits greatly improved electrochemical performance with very high capacitance and excellent cycling stability.

The activities of different types of PtRu catalysts for methanol oxidation are compared. Materials used were:  UHV-cleaned PtRu alloys, UHV-evaporated Ru onto Pt(111) as well as adsorbed Ru on Pt(111) prepared with and without additional reduction by hydrogen. Differences in the catalytic activity are observed to depend on the preparation procedure of the catalysts. The dependence of the respective catalytic activities upon the surface composition is reported. UHV−STM data for Pt(111)/Ru show the formation of two- and three-dimensional structures depending on surface coverage. A molecular insight on the electrochemical reaction is given via in situ infrared spectroscopy. Analysis of the data indicates that the most probable rate-determining step is the reaction of adsorbed CO with Ru oxide.

Accurate health estimation and lifetime prediction of lithium-ion batteries are crucial for durable electric vehicles. Early detection of inadequate performance facilitates timely maintenance of battery systems. This reduces operational costs and prevents accidents and malfunctions. Recent advancements in "Big Data" analytics and related statistical/computational tools raised interest in data-driven battery health estimation.Here, we will review these in view of their feasibility and cost-effectiveness in dealing with battery health in real-world applications. We categorise these methods according to their underlying models/algorithms and discuss their advantages and limitations. In the final section we focus on challenges of real-time battery health management and discuss potential next-generation techniques. We are confident that this review will inform commercial technology choices and academic research agendas alike, thus boosting progress in datadriven battery health estimation and prediction on all technology readiness levels.