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In this paper, in order to improve the mechanical properties of high-entropy fluorite oxides (HEFOs), five equimolar multicomponent oxides were synthesized by solid-phase reaction method by using element Y replacing one of the five cations in (Ce0.2Zr0.2La0.2Sm0.2Nd0.2)O2-δ. The substitution of La, Sm or Nd in (Ce0.2Zr0.2La0.2Sm0.2Nd0.2)O2-δ with Y element are able to form single-phase HEFOs at 1600 °C, while Ce4+ and Zr4+ replaced by Y3+ contains a large number of secondary phase. The former three components are almost dense and smooth, and all the elements are uniformly distributed with no obvious segregation and agglomeration. The Ce3+ concentration of Y-substituted Sm HEFOs is calculated to be 13.7%, yielding a nominal composition of (Ce0.2Zr0.2Y0.2Sm0.2Nd0.2)O1.6863. In addition, the HEFOs have the best mechanical properties with Vickers hardnesses, flexural strengths and fracture toughnesses of 15.12 GPa, 101.1 MPa and 7.88 MPa-mm1/2, respectively, which have potential applications in high-performance structural materials.

Document classification is an important area in Natural Language Processing (NLP). Because a huge amount of scientific papers have been published at an accelerating rate, it is beneficial to carry out intelligent paper classifications, especially fine-grained classification for researchers. However, a public scientific paper dataset for fine-grained classification is still lacking, so the existing document classification methods have not been put to the test. To fill this vacancy, we designed and collected the PaperNet-Dataset that consists of multi-modal data (texts and figures). PaperNet 1.0 version contains hierarchical categories of papers in the fields of computer vision (CV) and NLP, 2 coarse-grained and 20 fine-grained (7 in CV and 13 in NLP). We ran current mainstream models on the PaperNet-Dataset, along with a multi-modal method that we propose. Interestingly, none of these methods reaches an accuracy of 80% in fine-grained classification, showing plenty of room for improvement. We hope that PaperNet-Dataset will inspire more work in this challenging area.