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Self‐repairing catalysts are useful for achieving alkaline water electrolyzers with long lifetimes under intermittent operation. However, rational methodologies for designing self‐repairing catalysts have not yet been established. Herein, hybrid cobalt hydroxide nanosheets (Co‐ns), with a high deposition (repairing) rate, and β‐FeOOH nanorods (Fe‐nr), with high oxygen evolution reaction (OER) ability, are electrostatically self‐assembled into composite catalysts. This strategy is developed to integrate multifunctionality in self‐repairing catalysts. Positively charged Co‐ns and negatively charged Fe‐nr form uniform composites when dispersed in an electrolyte. These composites are electrochemically deposited on a nickel electrode by electrolysis at 800 mA cm−2. Co‐ns form a conductive mesoporous assembly of CoOOH nanosheets as a support. Fe‐nr are then distributed on the CoOOH nanosheets as active sites for the OER. Because of the high deposition rate of Co‐ns, the amount of Fe‐nr deposited increases 22 times compared to when Fe‐nr is deposited alone, and the OER current density increases 14 times compared to that of Co‐ns alone. The composite self‐repair catalyst shows the highest activity and durability under an accelerated durability test (ADT), and its degradation rate decreases from 84 μV cycle−1 (Fe‐nr only) to 60 μV cycle−1 (composite catalyst) under ADT conditions without repair. [ABSTRACT FROM AUTHOR]

In sample survey analysis, accurate population mean estimation is an important task, but traditional approaches frequently ignore the intricacies of real-world data, leading to biassed results. In order to handle uncertainties, indeterminacies, and ambiguity, this work presents an innovative approach based on neutrosophic statistics. We proposed novel neutrosophic factor type exponential estimators that use auxiliary information to improve population mean precision. The suggested estimators are highly useful for computing results while working with unclear, hazy, and neutrosophic-type data. These estimators produce answers that are interval-form rather than single-valued, which may give our population parameter a better chance of being off. Since we now have an estimated interval with the population mean's unknown value supplied a minimal MSE or highest Percentage Relative Efficiency (PRE), the estimators are more effective. The investigation of alpha constants in the neutrosophic framework, extending from −1 to +1, is at the heart of this research. These constants have a significant impact on how estimates are made and enable flexible accuracy modification. We choose the best neutrosophic values for characterizing constants, emphasizing their importance in obtaining precise estimations. This study further expands its originality by including preexisting estimators into the neutrosophic framework, showcasing its versatility and adaptability. We demonstrate the estimators' superiority to traditional techniques through empirical assessments employing neutrosophic temperature information and simulation assessments. The ensuing interval-based results, influenced by alpha constants, offer concrete insights into the world of uncertainty, enabling more well-informed decision-making. [ABSTRACT FROM AUTHOR]