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The exceptional performance and potential of 2D/3D perovskites for enhancing the efficiency and stability of perovskite solar cells (PSCs) are extensively studied. However, selecting appropriate organic ligands is critical to this process. The incorporation of suitable ligands into the 2D layer can passivate surface defects, regulate energy level alignment, and significantly improve device stability. Conversely, using unsuitable ligands not only fails to achieve the desired functions, but also negatively impacts device efficiency by hindering charge carrier transport and causing energy level mismatch issues. Therefore, exploring the selection of organic ligands based on different requirements is essential. In this review, recent studies are classified based on the chemical structure of organic ligands, analyze their application requirements, and provide suggestions for choosing optimal organic ligands to construct 2D/3D perovskites. Additionally, the feasibility and necessity of utilizing more efficient organic ligands while providing guidance for future research on 2D/3D PSCs are emphasized. [ABSTRACT FROM AUTHOR]

The identification of significant gene biclusters with particular expression patterns and the elucidation of functionally related genes within gene expression data has become a critical concern due to the vast amount of gene expression data generated by RNA sequencing technology. In this paper, a Conserved Gene Expression Module based on Genetic Algorithm (CGEMGA) is proposed. Breast cancer data from the TCGA database is used as the subject of this study. The p-values from Fisher's exact test are used as evaluation metrics to demonstrate the significance of different algorithms, including the Cheng and Church algorithm, CGEM algorithm, etc. In addition, the F-test is used to investigate the difference between our method and the CGEM algorithm. The computational cost of the different algorithms is further investigated by calculating the running time of each algorithm. Finally, the established driver genes and cancer-related pathways are used to validate the process. The results of 10 independent runs demonstrate that CGEMGA has a superior average p-value of 1.54 × 10−4 ± 3.06 × 10−5 compared to all other algorithms. Furthermore, our approach exhibits consistent performance across all methods. The F-test yields a p-value of 0.039, indicating a significant difference between our approach and the CGEM. Computational cost statistics also demonstrate that our approach has a significantly shorter average runtime of 5.22 × 100 ± 1.65 × 10−1 s compared to the other algorithms. Enrichment analysis indicates that the genes in our approach are significantly enriched for driver genes. Our algorithm is fast and robust, efficiently extracting co-expressed genes and associated co-expression condition biclusters from RNA-seq data. [ABSTRACT FROM AUTHOR]