PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
February 14, 20260 citationsOpen Access

Descriptor-Guided Isolated-Site Engineering Activates Layered V2O5 for Oxygen Evolution Catalysis

View Full Paper
BLByoung Guan LeeMBMonaam BenaliMCMin Kyu Choi

Key Points

  • The aim is to investigate transition-metal-incorporated layered V2O5 as an effective oxygen evolution reaction electrocatalyst.
  • Conducted theoretical screening using density functional theory to identify optimal catalysts.
  • Synthesized and validated TM-incorporated V2O5 materials experimentally.
  • Assessed catalytic activity and charge-transfer kinetics in alkaline electrolyte.
  • Identified Fe-, Mn-, and Co-embedded V2O5 as optimal candidates with minimized overpotentials.
  • Co@V2O5 showed enhanced catalytic performance and operational durability.
  • Demonstrated the connection between electronic structures and catalytic efficiency.

Abstract

This preprint reports a combined theoretical and experimental investigation of transition-metal-incorporated layered V2O5 as an oxygen evolution reaction (OER) electrocatalyst platform. Descriptor-guided density functional theory screening identifies Fe-, Mn-, and Co-embedded V2O5 monolayers as optimal candidates with minimized theoretical overpotentials governed by the Gibbs free-energy difference between key OER intermediates and the transition-metal d-band center. Guided by these insights, TM-incorporated V2O5 materials are synthesized and experimentally validated, with Co@V2O5 demonstrating enhanced catalytic activity, favorable charge-transfer kinetics, and operational durability in alkaline electrolyte. The work establishes isolated-site engineering in layered oxides as a practical strategy linking electronic-structure descriptors to experimentally accessible catalytic motifs for efficient, earth-abundant OER catalysts.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Lee et al. (2026) studied this question.

synapsesocial.com/papers/699011932ccff479cfe584e7https://doi.org/10.5281/zenodo.18618240
Ask AI
Helpful
Bookmark
Share
View Full Paper