Inorganics

Divalent europium complexes have attracted significant attention in various fields due to the unique electronic configuration of the Eu(II) ion. Given the high sensitivity of the 5d → 4f emission of Eu(II) ions to the ligand field, it is crucial to explore the relationship between ligands and this emission in Eu(II) complexes. However, the heavy-atom effects on the 5d → 4f emission of Eu(II) complexes coordinated with non-metal elements in the same group remain unclear. In this study, five mononuclear Eu(II)-chalcogenide complexes, Eu[H₃B·EPh-κE,H,H]₂(DME)₂ (E = S for 1 and Se for 2; DME = 1,2-Dimethoxyethane) and Eu[EPh]₂(18-C-6) (E = S for 3, Se for 4, and Te for 5; 18-C-6 = 1,4,7,10,13,16-Hexaoxacyclooctadecane), were synthesized via reduction of diphenyl disulfide chalcogenide analogs with Eu(BH₄)₂(THF)₂ or NaH. The structures of these complexes were investigated by single-crystal X-ray diffraction, and their properties were characterized by thermogravimetric analysis and photophysical property tests. Complexes 1 and 2 are isomorphous and show similar yellowish-green luminescence, while complexes 3–5 have similar structures but crystallize in different space groups with bluish-green luminescence. This research reveals the influence of chalcogenide ligands on the 5d → 4f emission of Eu(II) complexes, providing a theoretical basis and new research ideas for the application of Eu(II) complexes in various fields, including luminescent materials, cryogenic refrigerants, and magnetic materials.

To address the low energy conversion efficiency and weak mechanical strength of In₂O₃ thermoelectric materials for Aiye Processing Equipment, this study systematically investigated the regulatory effects and mechanisms of Ce doping on In₂O₃’s thermoelectric and mechanical properties via experiments. In₂O₃ samples with varying Ce contents were prepared, and property-microstructure correlations were analyzed through electrical/thermal transport tests, Vickers hardness measurements, and crystal structure characterization. Results show Ce doping synergistically optimizes In₂O₃ properties through multiple mechanisms. For thermoelectric performance, Ce⁴⁺ regulates carrier concentration and mobility, enhancing electrical conductivity and power factor. Meanwhile, lattice distortion from Ce-In atomic size differences strengthens phonon scattering, reducing lattice and total thermal conductivity. These effects boost the maximum ZT from 0.055 (pure In₂O₃) to 0.328 at 973 K obtained by x = 0.0065, improving energy conversion efficiency significantly. For mechanical properties, Ce doping enhances Vickers hardness and plastic deformation resistance via solid solution strengthening (lattice distortion hinders dislocations), microstructure densification (reducing vacancies/pores), Ce-O bond strengthening, and defect pinning. This study confirms Ce doping as an effective strategy for simultaneous optimization of In₂O₃’s thermoelectric and mechanical properties, providing experimental/theoretical support for oxide thermoelectric material development and valuable references for their medium-low temperature energy recovery applications.

X-ray absorption near-edge structure (XANES) spectra are employed to characterise the coordination numbers of metallic elements within materials. However, conventional XANES analysis methods frequently rely on preconceived assumptions regarding the analysed samples, which may not fully satisfy the requirements of scientific research and industrial applications. To mitigate such reliance, a novel approach based on the Gated Adaptive Network for Deep Automated Learning of Features (GANDALF) is proposed. To effectively extract multi-scale information from the XANES spectrum, the spectrum was segmented into multiple scales. Each segment was fitted using a pseudo-Voigt function, with the absorption edge position. The GANDALF algorithm, a table-based deep learning approach, was employed to model the coordination environment of absorbing elements. The proposed method was validated using a previously published open-access dataset. For vanadium-containing samples, the model achieved R² values of 0.7837 on test sets with non-integer coordination numbers, whereas the random forest model only achieved 0.6328. Furthermore, our results highlight the significant importance of the post-edge peak when predicting coordination numbers using the full spectrum.