PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 20, 2020Advanced Functional Materials268 citations

Simultaneously Realizing Rapid Electron Transfer and Mass Transport in Jellyfish‐Like Mott–Schottky Nanoreactors for Oxygen Reduction Reaction

View Full Paper
ZSZehui SunYWYuankun WangLZLibo Zhang

Key Points

Key points are not available for this paper at this time.

Abstract

Abstract Fundamental understanding of constructing elevated catalysts to realize fast electron transfer and rapid mass transport in oxygen reduction reaction (ORR) chemistry by interface regulation and structure design is important but still ambiguous. Herein, a novel jellyfish‐like Mott–Schottky‐type electrocatalyst is developed to realize fast electron transfer and decipher the structure–mass transport connection during ORR process. Both spectroscopy techniques and density functional theory calculation demonstrate electrons spontaneously transfer from Fe to N‐doped graphited carbon at the heterojunction interface, thus accelerating electron transfer from electrode to reactant. Dynamic analysis indicates unique structure can significantly improve mass transport of oxygen‐species due to two factors: one is electrolyte streaming effect caused by tentacle‐like carbon nanotubes; the other is effective collision probability in the semi‐closed cavity. Therefore, this Mott–Schottky‐type catalyst delievers superior ORR performance with high onset potential, positive half wave potential, and large current density. It also exhibits low overpotential when serving as an air cathode in Zn–air batteries. This work deepens understanding of the two key factors—electron transfer and mass transport—on determining the kinetic reaction of ORR process and offers a new avenue in constructing efficient Mott–Schottky electrocatalysts.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Sun et al. (2020) studied this question.

synapsesocial.com/papers/69dff85993e101b251e9c380https://doi.org/10.1002/adfm.201910482
Ask AI
Helpful
Bookmark
Share
View Full Paper