Graphene-supported single atom catalysts for high performance lithium-oxygen batteries

  • Hoilun Wong
  • , Tongchao Liu
  • , Mohsen Tamtaji
  • , Xiaozhou Huang
  • , Tsz Wing Tang
  • , Md Delowar Hossain
  • , Jun Wang
  • , Yuting Cai
  • , Zhenjing Liu
  • , Hongwei Liu
  • , Khalil Amine
  • , William A. Goddard
  • , Zhengtang Luo

Research output: Contribution to journalArticlepeer-review

Abstract

The optimal choice of d-block metals in single atom catalysts (SACs) is crucial for designing efficient electrocatalysts for activating the Oxygen reduction reaction (ORR)/ Oxygen evolution reaction (OER) in lithium-oxygen batteries (LOBs). Herein, we used the Quantum Mechanics methods to understand the origin of reactivity for a series of 16 d-block metals supported on nitrogen-doped graphene as SACs for ORR and OER in LOBs. Based on the Gibbs free energy calculations, we found that among the 16 SACs investigated, Zn-SAC exhibits the highest electrochemical activity with the lowest overpotential of 0.17 V. We then used machine learning (ML) to develop an intrinsic descriptor, Φ, that correlates the catalytic activity with electronic and chemical properties of the catalytic centers at the M-N4 active site on graphene surface. We established a linear relationship between Φ and the catalytic activity that provides guidance for designing efficient SACs for electrocatalysis in LOBs. To validate these predictions, we report electrochemical measurements showing that Zn-SAC exhibits an ultra-stable cyclability with reduced overpotentials over Mo-SAC and nitrogen-doped graphene (NG), confirming our theoretical prediction. This fundamental work provides a deep understanding on the rational design of efficient SACs for OER/ ORR in LOBs.

Original languageEnglish
Article number109279
JournalNano Energy
Volume121
DOIs
Publication statusPublished - 1 Mar 2024
Externally publishedYes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Density functional theory
  • Machine learning
  • Overpotentials
  • Oxygen evolution reaction
  • Oxygen reduction reaction
  • Single atom catalysts

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