In this study, to investigate how oxygen vacancy impacts the photocatalytic performance of LaNiO₃, undoped and Sr-doped LaNiO_3−σ nanoparticles are successfully prepared by the sol-gel method. The X-ray diffractometer (XRD) results show both two samples belong to the R-3c space group of the rhombohedral system. According to the conservation of valence and the X-ray photoelectron spectroscopy (XPS) results, it is demonstrated that Sr-doping can introduce more oxygen vacancy into LaNiO_3−σ. According to photocatalytic experiments of the degradation of methyl orange (MO) solution, La_0.875Sr_0.125NiO_3−σ shows higher photocatalytic performance than undoped LaNiO_3−σ. First-principle calculation results show that the introduction of oxygen vacancy and Sr-doping can lead to the narrowing of the band gap width of LaNiO₃.

Hematological morphology examinations, essential for diagnosing blood disorders, increasingly utilize deep learning. Blood cell classification, determined by combinations of cell attributes, is complicated by the complex relationships and subtle differences among the attributes, resulting in significant time and cost penalties. This study introduces the Partial Label Learning for Blood Cell Classification (P4BC) strategy, a method that trains neural networks using the blood cell attribute labeling data of weak annotations. Using morphological knowledge, we predefined candidate label sets for the blood cell attributes to blend this knowledge with deep learning. This improves the model’s prediction accuracy and interpretability in classifying attributes. This method effectively combines morphological knowledge with deep learning, an approach we refer to as knowledge alignment. It results in an 8.66% increase in attribute recognition accuracy and a 1.09% improvement in matching predictions to the candidate label sets, compared to the original method. These results confirm our method’s ability to grasp the characteristic information of blood cell attributes, enhancing the model interpretability and achieving knowledge alignment between hematological morphology and deep learning. Our algorithm ensures attribute classification accuracy and shows excellent cell category classification, highlighting its wide application potential and practical value in blood cell category classification.