A feasible process for recycling valuable metals from LiNi0.5Co0.2Mn0.3O2 cathode materials of spent Li-ion batteries.

The recycling of cathode materials for spent NCM has always been a major concern for the energy industry. However, among the current processing methods, the general leaching efficiency of Li is between 85% and 93%, with much room for improvement. The recovery of Ni, Co and Mn requires a high cost of secondary purification. In this study, to recycle the NCM cathode material, a route of sulphated reduction roasting - selective Li water leaching - efficiency acid leaching of Ni, Co, Mn - extraction separation - crystallisation was adopted. The results showed that after roasting (a temperature of 800 °C, a reaction time of 90 min, a carbon content of 26%, and a sulphuric acid addition of nH2SO4:nLi = 0.85), Li water leaching efficiency was 98.6%, followed by acid leaching of Ni, Co and Mn at around 99%. Mn, Co were extracted with Di-(2-ethylhexyl) phosphoric acid and 2-Ethylhexyl phosphonic acid mono-2-ethylhexyl ester respectively to obtain Ni, Co, Mn solutions, which eventually were crystallized for manganese sulphate, cobalt sulphate, lithium carbonate and nickel sulphate products, with high purity of 99.40%, 98.95%, 99.10%, and 99.95%. The results of this study improved the leaching efficiency of Li and were closely linked to the actual industrial preparation of Ni, Co and Mn sulphates, providing a feasible and promising basis for spent NCM cathode materials industrial recovery.

A Knowledge Graph Entity Disambiguation Method Based on Entity-Relationship Embedding and Graph Structure Embedding.

The purpose of knowledge graph entity disambiguation is to match the ambiguous entities to the corresponding entities in the knowledge graph. Current entity ambiguity elimination methods usually use the context information of the entity and its attributes to obtain the mention embedding vector, compare it with the candidate entity embedding vector for similarity, and perform entity matching through the similarity. The disadvantage of this type of method is that it ignores the structural characteristics of the knowledge graph where the entity is located, that is, the connection between the entity and the entity, and therefore cannot obtain the global semantic features of the entity. To improve the Precision and Recall of entity disambiguation problems, we propose the EDEGE (Entity Disambiguation based on Entity and Graph Embedding) method, which utilizes the semantic embedding vector of entity relationship and the embedding vector of subgraph structure feature. EDEGE first trains the semantic vector of the entity relationship, then trains the graph structure vector of the subgraph where the entity is located, and balances the weights of these two vectors through the entity similarity function. Finally, the balanced vector is input into the graph neural network, and the matching between the entities is output to achieve entity disambiguation. Extensive experimental results proved the effectiveness of the proposed method. Among them, on the ACE2004 data set, the Precision, Recall, and F1 values of EDEGE are 9.2%, 7%, and 11.2% higher than baseline methods.

Preparation, Characterization, and Performance Analysis of S-Doped Bi2MoO6 Nanosheets.

S-doped Bi2MoO6 nanosheets were successfully synthesized by a simple hydrothermal method. The as-prepared samples were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM), transmission electron microscopy (TEM), N2 adsorption-desorption isotherms, Raman spectroscopy, Fourier transform infrared spectroscopy (FT-IR), elemental mapping spectroscopy, photoluminescence spectra (PL), X-ray photoelectron spectroscopy (XPS), and UV-visible diffused reflectance spectra (UV-vis DRS). The photo-electrochemical performance of the samples was investigated via an electrochemical workstation. The S-doped Bi2MoO6 nanosheets exhibited enhanced photocatalytic activity under visible light irradiation. The photo-degradation rate of Rhodamine B (RhB) by S-doped Bi2MoO6 (1 wt%) reached 97% after 60 min, which was higher than that of the pure Bi2MoO6 and other S-doped products. The degradation rate of the recovered S-doped Bi2MoO6 (1 wt%) was still nearly 90% in the third cycle, indicating an excellent stability of the catalyst. The radical-capture experiments confirmed that superoxide radicals (·O2-) and holes (h+) were the main active substances in the photocatalytic degradation of RhB by S-doped Bi2MoO6.