机器学习辅助氮硫共掺杂碳载铂基氧还原催化剂研究

Machine learning-assisted research on nitrogen-sulfur co-doped carbon-loaded platinum-based oxygen reduction catalysts

  • 摘要: 研究采用了一种简便且高效的一锅热解法,成功制备了氮硫二元共掺杂碳载体负载的铂基氧还原催化剂. 通过精确控制前驱体的配比与反应条件,合成了具有不同铂负载量及氮硫掺杂比例的系列催化剂,并优化得到了高性能的样品,即Pt/BP-NS500-40%催化剂. 该催化剂在氧还原反应(ORR)中展现出了卓越的催化活性,其半波电位为0.8379 V,比活性(SA)达到了2.26 A/m2. 催化剂经历5000次循环伏安测试后,其半波电位仅下降了73 mV,表现出较好的稳定性. 为了进一步优化催化剂的设计并降低铂金属的用量,研究还引入了机器学习技术,构建了催化剂性能与其制备条件之间的预测模型. 该模型不仅能够为催化剂的制备提供理论指导,还能够加速高性能催化剂的研发进程.

     

    Abstract: In this study, a simple yet highly efficient one-pot pyrolysis method was employed to successfully synthesize nitrogen-sulfur dual-doped carbon-supported platinum-based oxygen reduction reaction (ORR) catalysts. By precisely controlling the precursor ratios and reaction conditions, a series of catalysts with varying platinum loadings and nitrogen-sulfur doping ratios were synthesized, among which an optimized high-performance sample, denoted as Pt/BP-NS500-40%, was obtained. This catalyst demonstrated exceptional catalytic activity towards ORR, with a half-wave potential of 0.8379 V and an impressive specific activity (SA) of 2.26 A/m2. After undergoing 5,000 cycles of cyclic voltammetry testing, the catalyst exhibited only a 73 mV decrease in half-wave potential, showing good stability. To further optimize catalyst design and reduce platinum metal usage, machine learning techniques were introduced in this study to construct a predictive model relating catalyst performance to its preparation conditions. This model not only provides theoretical guidance for catalyst preparation but also accelerates the development process of high-performance catalysts.

     

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