Lithium-ion batteries with LiCoO₂ (LCO) cathodes are widely used in various electronic devices, resulting in a large amount of spent LCO (SLCO). Therefore, there is an urgent need for an efficient technique for recycling SLCO. However, due to the presence of cobalt oxide with a spinel phase on the surface of highly-degraded LCO, the strong electrostatic repulsion from the transition metal octahedron poses a high Li replenishment barrier, making the regeneration of highly-degraded LCO a challenge. Herein, we propose a structural transformation strategy for reconstructing Li replenishment channels to aid the direct regeneration of highly-degraded LCO. In this approach, ball milling is employed to disrupt the inherent structure of highly-degraded LCO, thereby releasing the internal stress and converting the surface spinel phase into a homogeneous amorphous structure, which promotes Li insertion and regeneration. The regenerated LCO (RLCO) exhibits an outstanding discharge capacity of 179.10 mAh·g⁻¹ in the voltage range of 3.0–4.5 V at 0.5 C. The proposed strategy is an effective regeneration approach for highly-degraded LCO, thereby facilitating the efficient recycling of spent lithium-ion battery cathode materials.

Transition metal dichalcogenides (TMDs) have been regarded as promising cathodes for aqueous zinc-ion batteries (AZIBs) but suffer from sluggish reaction kinetics due to their poor conductivity and the strong electrostatic interaction between Zn-ion and cathode materials. Herein, a well-defined structure with MoSSe nanosheets vertically anchored on graphene is used as the cathode for AZIBs. The dissolution of Se into MoS₂ lattice together with heterointerface design via developing C–O–Mo bonds improves the inherent conductivity, enlarges interlayer spacing, and generates abundant anionic vacancies. As a result, the Zn²⁺ intercalation/deintercalation process is greatly improved, which is confirmed by theoretical modeling and ex-situ experimental results. Remarkably, the assembled AZIBs exhibit high-rate capability (124.2 mAh·g⁻¹ at 5 A·g⁻¹) and long cycling life (83% capacity retention after 1,200 cycles at 2 A·g⁻¹). Moreover, the assembled quasi-solid-state Zn-ion batteries demonstrate a stable cycling performance over 100 cycles and high capacity retention over 94% after 2,500 bending cycles. This study provides a new strategy to unlock the electrochemical activity of TMDs via interface design and atomic engineering, which can also be applied to other TMDs for multivalent batteries.

It remains challenging to effectively estimate the remaining capacity of the secondary lithium-ion batteries that have been widely adopted for consumer electronics, energy storage, and electric vehicles. Herein, by integrating regular real-time current short pulse tests with data-driven Gaussian process regression algorithm, an efficient battery estimation has been successfully developed and validated for batteries with capacity ranging from 100% of the state of health (SOH) to below 50%, reaching an average accuracy as high as 95%. Interestingly, the proposed pulse test strategy for battery capacity measurement could reduce test time by more than 80% compared with regular long charge/discharge tests. The short-term features of the current pulse test were selected for an optimal training process. Data at different voltage stages and state of charge (SOC) are collected and explored to find the most suitable estimation model. In particular, we explore the validity of five different machine-learning methods for estimating capacity driven by pulse features, whereas Gaussian process regression with Matern kernel performs the best, providing guidance for future exploration. The new strategy of combining short pulse tests with machine-learning algorithms could further open window for efficiently forecasting lithium-ion battery remaining capacity.