21:T43f,As a potential oxygen evolution reaction (OER) catalyst, Co-based perovskites have received intensive attention. However, Sr readily accumulates on their surface, and makes them inert toward the OER. Herein, we propose a simple but versatile electrochemical reduction method to reconstruct the active surface of Co-based perovskites within a few seconds. By this method, Sr rapidly precipitates from Co-based perovskites, accompanied by the introduction of Sr and oxygen vacancies. After reconstruction, the electrochemical active surface areas of Co-based perovskites greatly increase, and the OER overpotential of the optimized SrNb_0.1Co_0.7Fe_0.2O_3-δ (ER-SNCF-20s) reaches 278 mV at 10 mA cm^-2. This can be explained by the decrease of overpotentials at the rate-determining step. Using ER-SNCF-20s, the splitting voltage of alkaline natural seawater can reach 1.56 V at 10 mA cm^-2, and remains steady for 300 h. This effort offers a feasible method for reconstructing the active surface of Co-based perovskites.22:T51b,Single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool in genomics research, enabling the analysis of gene expression at the individual cell level. However, scRNA-seq data often suffer from a high rate of dropouts, where certain genes fail to be detected in specific cells due to technical limitations. This missing data can introduce biases and hinder downstream analysis. To overcome this challenge, the development of effective imputation methods has become crucial in the field of scRNA-seq data analysis. Here, we propose an imputation method based on robust and non-negative matrix factorization (scRNMF). Instead of other matrix factorization algorithms, scRNMF integrates two loss functions: L2 loss and C-loss. The L2 loss function is highly sensitive to outliers, which can introduce substantial errors. We utilize the C-loss function when dealing with zero values in the raw data. The primary advantage of the C-loss function is that it imposes a smaller punishment for larger errors, which results in more robust factorization when handling outliers. Various datasets of different sizes and zero rates are used to evaluate the performance of scRNMF against other state-of-the-art methods. Our method demonstrates its power and stability as a tool for imputation of scRNA-seq data.

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