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Author:

Liang, Hongxing (Liang, Hongxing.) | Yang, Kang (Yang, Kang.) | Zhao, Chenchen (Zhao, Chenchen.) | Zhai, Chuantian (Zhai, Chuantian.) | Wu, Liang (Wu, Liang.) | Du, Wenbo (Du, Wenbo.) (Scholars:杜文博)

Indexed by:

EI Scopus SCIE

Abstract:

Reconciling the inherent trade-off between anodic efficiency and discharge voltage in the design of high-energy-density magnesium-air (Mg-air) batteries has been a persistent challenge. Herein, we propose a pioneering active learning strategy that integrates physically motivated variables, machine learning, exploration of the Pareto front, experimental data feedback, and generated data feedback for the purpose of designing magnesium anodes. Within an extensive compositional space (similar to 350,000 possibilities), we have pinpointed a novel alloy, Mg-1Ga-1Ca-0.5In, exhibiting exceptional performance with high efficiency (64 +/- 5.5 % at 1 mA cm(-2), 64 +/- 0.5 % at 10 mA cm(-2)) and high voltage (1.80 +/- 0.00 V at 1 mA cm(-2), 1.57 +/- 0.01 V at 10 mA cm(-2)), surpassing conventional methods of alloying. Subsequent experiments and density functional theory (DFT) calculations have unveiled that the outstanding performance of Mg-1Ga-1Ca-0.5In stems from "grain boundary activation" induced by active second phases and "intra-grain inhibition" resulting from the orbital hybridization between solute atoms and Mg atoms. This study provides a novel research paradigm and offers valuable insights for the further development of high-performance Mg-air batteries.

Keyword:

Theoretical calculation Self-corrosion Magnesium air battery Magnesium alloy Machine learning

Author Community:

  • [ 1 ] [Liang, Hongxing]Beijing Univ Technol, Coll Mat & Engn, Beijing 100021, Peoples R China
  • [ 2 ] [Zhao, Chenchen]Beijing Univ Technol, Coll Mat & Engn, Beijing 100021, Peoples R China
  • [ 3 ] [Zhai, Chuantian]Beijing Univ Technol, Coll Mat & Engn, Beijing 100021, Peoples R China
  • [ 4 ] [Du, Wenbo]Beijing Univ Technol, Coll Mat & Engn, Beijing 100021, Peoples R China
  • [ 5 ] [Yang, Kang]Anhui Univ Technol, Sch Mat Sci & Engn, Maanshan 243002, Peoples R China
  • [ 6 ] [Wu, Liang]Chongqing Univ, Coll Mat Sci & Engn, Chongqing 400044, Peoples R China

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Source :

APPLIED SURFACE SCIENCE

ISSN: 0169-4332

Year: 2024

Volume: 657

6 . 7 0 0

JCR@2022

Cited Count:

WoS CC Cited Count:

SCOPUS Cited Count: 3

ESI Highly Cited Papers on the List: 0 Unfold All

WanFang Cited Count:

Chinese Cited Count:

30 Days PV: 2

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