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Updated: Sep 26, 2026

Synthesis of Bimetallic Pt/Sn-based Nanoparticles in Ionic Liquids
Published on: August 23, 2018
Unraveling the Bimetallic Synergy: A Unified Framework for Synthesis, Dynamic Evolution, and Multi-Electron
Jingru Hu1, Xianyuan Xu1, Dengsheng Ma1
1College of Environmental Science and Engineering, Hunan University and Key Laboratory of Environmental Biology and Pollution Control, Hunan University, Ministry of Education, Changsha, P. R. China.
Abstract:
Bimetallic electrocatalysts provide additional geometric and electronic degrees of freedom for regulating multi-electron reactions and can relax the scaling constraints encountered at monometallic sites in favorable cases. However, rational development of such catalysts remains challenging due to the mismatch between static as-synthesized structures and dynamically evolving active sites under operating conditions. This Review presents an integrated framework connecting synthesis, structural evolution, characterization, and multi-electron electrocatalytic behaviors. Representative bimetallic systems, ranging from alloys, intermetallics, heterointerfaces, and core-shell structures to atomically dispersed dual-metal sites are compared in terms of structural precision, scalability, and ability to control metal-metal proximity. Key characterization approaches are summarized, highlighting the necessity of combined microscopy, spectroscopy, operando analysis, isotope labeling, and theoretical calculations to validate genuine bimetallic synergy. Applications are discussed across water splitting (HER/OER), oxygen reduction (ORR), CO2 reduction (CO2RR), nitrogen-cycle conversion (NOxRR), and electrochemical urea synthesis (EUS). Across these systems, two universal synergy modes are identified: electronic/geometric tuning to optimize intermediate adsorption and functional site division to enable tandem multi-step reactions. Finally, challenges in controllable dynamic reconstruction, structural durability under industrial conditions, together with prospects of data-driven catalyst discovery are outlined to guide scalable catalyst development.
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