谷歌Chrome浏览器插件
订阅小程序
在清言上使用

Mesoscale Diffusion Enhancement of Carbon-Bowl-Shaped Nanoreactor toward High-Performance Electrochemical H2O2 Production

ACS Applied Materials & Interfaces(2021)

引用 15|浏览2
暂无评分
摘要
Gas-involving electrocatalytic reactions are of critical importance in the development of carbon-neutral energy technologies. However, the catalytic performance is always limited by the unsatisfactory diffusion properties of reactants as well as products. In spite of significant advances in catalyst design, the development of mesoscale mass diffusion and process intensification is still challenging due to the lack of material platforms, synthesis methods, and mechanism understanding. In this work, as a proof of concept, we demonstrated achieving these two critical factors in one system by designing a mesoporous carbon bowl (MCB) nanoreactor with both abundant highly active sites and enhanced diffusion properties. The catalysts with controlled opening morphology and mesoporous channels were carefully synthesized via a hydrogen-bonding uneven self-assembling followed by pyrolysis. Taking the two-electron oxygen reduction reaction (ORR) for the H2O2 production as a model, which is a strong diffusion-limiting reaction, the optimal MCB samples achieved a high H2O2 selectivity (>90%) across a wide potential window of 0.6 V, and a large cathodic current density of −2.7 mA cm–2 (at 0.1 V vs RHE). The electrochemical evaluation and finite-element simulation study for a series of MCBs revealed that the similar active sites intrinsically determined the H2O2 selectivity, while the well-designed mesoporous bowl configuration with different window sizes boosted the ORR activity by significantly accelerating the local mass diffusion. This work sheds new insights into the engineering of intrinsic active sites and local mass diffusion properties for electrocatalysts, which bridges the research of electrocatalysis from fundamental atomic-scale and practical macroscale devices.
更多
查看译文
关键词
nanoreactor,diffusion,carbon-bowl-shaped,high-performance
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要