王璐教授、李彦光教授及其合作者在Nat. Commun.上发表论文

Theory-guided design of hydrogen-bonded cobaltoporphyrin frameworks for highly selective electrochemical H₂O₂ production in acid

作者：

Xuan Zhao¹, Qi Yin², Xinnan Mao¹, Chen Cheng¹, Liang Zhang ¹, Lu Wang ¹*, Tian-Fu Liu ^2,3*, Youyong Li ^1,4 & Yanguang Li ¹*

单位：

¹Institute of Functional Nano & Soft Materials (FUNSOM), Collaborative Innovation Center of Suzhou Nanoscience and Technology, Soochow University, Suzhou 215123, China.

²State Key Laboratory of Structural Chemistry, Fujian Institute of Research on the Structure of Matter, Chinese Academy of Sciences, Fuzhou 350002 Fujian, China.

³University of the Chinese Academy of Sciences, Beijing 100049, China.

⁴Macao Institute of Materials Science and Engineering (MIMSE), MUST-SUDA Joint Research Center for Advanced Functional Materials, Macau University of Science and Technology, Taipa 999078 Macao, China.

摘要：

The pursuit of selective two-electron oxygen reduction reaction to H₂O₂ in acids is demanding and largely hampered by the lack of efficient non-precious-metal-based electrocatalysts. Metal macrocycles hold promise, but have been relatively underexplored. Efforts are called for to promote their inherent catalytic activities and/or increase the surface exposure of active sites. In this contribution, we perform the high-throughput computational screening of thirty-two different metalloporphyrins by comparing their adsorption free energies towards key reaction intermediates. Cobalt porphyrin is revealed to be the optimal candidate with a theoretical overpotential as small as 40 mV. Guided by the computational predictions, we prepare hydrogen-bonded cobaltoporphyrin frameworks in order to promote the solution accessibility of catalytically active sites for H₂O₂ production in acids. The product features an onset potential at ~0.68 V, H₂O₂ selectivity of >90%, turnover frequency of 10.9 s−1 at 0.55 V and stability of ~30 h, the combination of which clearly renders it stand out from existing competitors for this challenging reaction.

影响因子：

14.919

分区情况：

一区

链接：

https://www.nature.com/articles/s41467-022-30523-0