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A long-term durability test for 3910 h was carried out on a low Pt-loading fuel cell stack by simulating the dynamic driving cycles. The performance of a specific membrane electrode assembly (MEA) in the stack before and after the test was analyzed, revealing the high voltage decay rate of the appliance. Various electrochemical and physical characterization techniques were used to analyze the degradation mechanism of the MEA via region-based segmentation technique. The results show that the degradation of MEA performance is mainly due to the agglomeration and loss of Pt nanoparticles in the cathode catalyst. In particular, the growth of catalyst particles occurs at the inlet and outlet of the cathode. A high loss rate of Pt particles at the cathode inlet causes their aggregation at the boundary between the catalyst layer and the membrane where the loss of Pt is initiated. Finally, the migration of the unsupported Pt particles occurs due to the gravity toward the underlying cathode micro porous layer. This study had important implications for promoting the development and commercial application of cost-efficient and long-life MEAs.

In modern times, financial institutions are the core carrier of efficient operation of financial markets. With the continuous development of financial models such as IoT (Internet of Things) finance, commercial banks have made many attempts in the integration and innovation of finance and logistics, but they have also increased the types and complexity of risks they face while improving financing efficiency. It has the characteristics of great destructiveness, strong infectivity, and high complexity. The establishment of a perfect emergency security optimization management for early warning of bank liquidity risk is an important part of timely detection and effective management of liquidity risk. In order to enable decision makers to accurately use the effective disposal of similar liquidity risk emergencies as a reference for decision-making, this paper studies the generation method of emergency security plan for bank liquidity risk using big data analytic-based case reasoning. Firstly, analyze the characteristics of various types of bank liquidity emergencies a, and identify the key risk points, and form the accident case database. Secondly, carry out the interval division according to the different numerical distribution characteristics of the indicators, and calculate the repeatability of the involved stages. Finally, calculate the comprehensive similarity to obtain the emergency security plan for reference. At the same time, taking A commercial bank as an example, verify the effectiveness of the method by using the constructed case-based reasoning model to generate emergency security plans intelligently. It provides reference for commercial bank liquidity risk management.

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