Summary Yttrium iron garnet (YIG) is a potential matrix for immobilizing high-level radioactive waste (HLW) due to its high actinide solubility and chemical flexibility. In this study, Ce was employed as a surrogate of Pu. A series of Y 3- x Ce x Fe 5 O 12 (0 ≤ x ≤ 1) samples were prepared by solid-state sintering at 1400°C to investigate the solubility, microstructure, and chemical durability of Ce-doped YIG. The X-ray diffraction (XRD) and scanning electron microscopy (SEM) results showed that the solubility of the Ce in a single garnet was 32 mol% at synthesis temperature of 1400°C. The SEM analysis indicated the YIG ceramic grain growth and compactness as Ce concentrations increased. The XPS analysis indicated that the amount of Ce 4+ out of the total Ce increased as the CeO 2 doping increased, and the maximum value was more than 75.9% in the sample synthesis at 1400°C. Static leaching experiments were also conducted on the single Ce-doped garnet synthesized at 1400°C. The normalized LR Ce and LR Y release rates were approximately 10 −4 and 10 −5 g m −2 d −1 after 42 days, respectively. The chemical durability was comparable to other ceramic matrices (such as pyrochlore and zircon, among others). These results demonstrated that YIG ceramics are suitable for immobilizing HLW, such as Pu or U. Highlights The solubility of Ce in single-phase YIG was 32 mol% at 1400°C. Grain size and compactness increased Ce content. The concentration of Ce 4+ in total Ce of sample could be over 75.9%. The obtained YIG ceramics exhibit excellent chemical stability. Novelty Statement Y 3-x Ce x Fe 5 O 12 ceramics were prepared by solid-state sintering and the solubility, microstructure, and chemical durability were investigated. The solubility of Ce was 32 mol% at 1400°C. The obtained YIG ceramics exhibit excellent chemical stability.
Rationale Because of its powerful analytical ability, ion mobility spectrometry (IMS) plays an important role in the field of mass spectrometry. However, one of the main defects of IMS is its low structural resolution, which leads to the phenomenon of peak overlap in the analysis of compounds with similar mass charge ratio. Methods A multiobjective dynamic teaching-learning-based optimization (MDTLBO) method was proposed to separate IMS overlapping peaks. This method prevents local optimization and identifies peak model coefficients efficiently. In addition, the position information of particles largely reflects the half-peak width of IMS, which makes single peaks difficult to appear and coefficient identification easier. Results The performance comparison of MDTLBO with other deconvolution methods (genetic algorithm, improved particle swarm optimization algorithm, and dynamic inertia weight particle swarm optimization algorithm) shows that the maximum deconvolution error of MDTLBO is only 0.7%, which is much lower than that for the other three methods. In addition, robustness is a performance index that reflects the advantages and disadvantages of the algorithm. Conclusion MBTLBO is more robust than other algorithms for separating overlapping peaks. The algorithm can separate the heavily overlapped mobility peaks, produce better analysis results, and improve the resolution of IMS.
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