RESUMEN
For recovering Ni, Co, and Mn from lithium-ion batteries, traditional chemical precipitation methods demonstrate low selectivity and significantly contribute to environmental pollution. This study proposes a separation recovery technique for transition metals, specifically Ni, Co, and Mn, from spent LIBs, involving "acid dissolution" and "multistage oxidation precipitation". More than 98% of transition metals can be extracted from spent LIBs using a low acid concentration (0.5 M) without reducing agents. The feasibility of separating different metals via multistage oxidation precipitation, based on their different electrode potentials for oxidizing Me2+ (Me = Mn/Co/Ni), was confirmed. The combination of oxidizing agent S2O82- and the precipitant OH- was universally applied to the fractional precipitation of Mn, Co, and Ni respectively. About 99% of Mn, 97.06% Co, and 96.62% Ni could be precipitated sequentially by changing the concentrations of S2O82- and the pH value of solution. XRD, XPS, XRF, ICP-MS and other methods were employed to elucidate the mechanism behind the multistage oxidation precipitation of target metal compounds, exploiting the differential electrode potentials for oxidizing Me2+ ions. This technique surpasses traditional solvent extraction in cost-effectiveness and selectivity, showing promise for large-scale industrial applications in recovering Mn, Co, and Ni.
RESUMEN
The recovery of spent lithium-ion batteries by traditional acid leaching is limited by serious pollution, complicated technology, and the low purity of Li2CO3. To address the problems of the traditional acid leaching process and increasing demand for decarbonization, a technique for the selective carbonation leaching of Li and the recovery of battery-grade Li2CO3 by a simple concentration precipitation process without acids or bases was developed. The coupling of CO2 and reducing agents could effectively promote the precipitation of MCO3 (M=Ni/Co/Mn) and the selective leaching of Li by decreasing the reducing capability needed for transition metals and decreasing the pH of the solution. The optimal selective leaching process of Li was obtained under 1â MPa CO2 with 20â g/L Na2S2O3 at an L/S ratio of 30â mL/g for 1.5â h. FT-IR, XRD, ICP-MS and other methods were used to reveal the multiphase interfacial reaction mechanism of the carbonation reduction of layered cathode materials, which indicated that the reducing agent Na2S2O3 could promote lattice distortion of the cathode materials and effective separation of Li. In summary, a green and economical method for the selective recovery of battery-grade Li2CO3 using a one-step method of CO2 carbonation recovery in a near-neutral environment was proposed.
RESUMEN
Single-nucleotide polymorphisms (SNPs), as disease-related biogenetic markers, are crucial in elucidating complex disease susceptibility and pathogenesis. Due to computational inefficiency, it is difficult to identify high-dimensional SNP interactions efficiently using combinatorial search methods, so the spherical evolutionary multi-objective (SEMO) algorithm for detecting multi-locus SNP interactions was proposed. The algorithm uses a spherical search factor and a feedback mechanism of excellent individual history memory to enhance the balance between search and acquisition. Moreover, a multi-objective fitness function based on the decomposition idea was used to evaluate the associations by combining two functions, K2-Score and LR-Score, as an objective function for the algorithm's evolutionary iterations. The performance evaluation of SEMO was compared with six state-of-the-art algorithms on a simulated dataset. The results showed that SEMO outperforms the comparative methods by detecting SNP interactions quickly and accurately with a shorter average run time. The SEMO algorithm was applied to the Wellcome Trust Case Control Consortium (WTCCC) breast cancer dataset and detected two- and three-point SNP interactions that were significantly associated with breast cancer, confirming the effectiveness of the algorithm. New combinations of SNPs associated with breast cancer were also identified, which will provide a new way to detect SNP interactions quickly and accurately.
