Unravelling the Growth Mechanism of Local Entropy Tailored Intermetallic Pd 3 Ni Exhibiting Tetrafunctional Activity in a Water Electrolyzer and Fuel Cell

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Authors: Soumi Mondal, Shreya Sarkar, Siddhi Kediya, Mohd Riyaz, Ashok Singh, Sayan Das, Debabrata Bagchi, Ranjana Burman, Nilutpal Dutta, Ashutosh Kumar Singh, Meera Radhakrishnan, Sebastian C. Peter
Cited by: 6
References: 77

Abstract

Designing a multifunctional electrocatalyst is increasingly in demand. This work deals with successful solution phase synthesis of an ordered compound of Pd and Ni, Pd 3 Ni, which is assumed to be difficult as almost no adjacent elements in the same group form intermetallic compounds. Pd 3 Ni is a highly efficient and electrochemically stable material for tetrafunctional activity, in the hydrogen evolution reaction (HER), oxygen evolution reaction (OER), oxygen reduction reaction (ORR), and ethanol oxidation reaction (EOR), which are involved in both fuel cells and water electrolyzers. Extensive ex situ and in situ characterization has revealed the robustness of this material and the reaction mechanism in different electrochemical reactions. The “local entropy tailoring” reflects the reduced configurational entropy associated with the ordered Pd 3 Ni lattice relative to the alloy, as evidenced by differential scanning calorimetry, enabling site-specific structural stability. This material has shown promising activity in a proton exchange membrane water electrolyzer and a high temperature fuel cell. The tuned surface of the intermetallic compound has enhanced C–C cleavage in ethanol molecules allowing the sluggish 12e – transfer process, and the compound has shown very high stability for >80000 cycles of alkaline HER. The role of pH and potential has been explored in retaining the ordered phase of the intermetallic compound. Tetrafunctionality and its extensive exploration under different reaction conditions have been exhaustively evaluated in this work.

Fields of Study & Subjects

Electrocatalysts for Energy Conversion Hybrid Renewable Energy Systems Machine Learning in Materials Science

Publication details

Journal / source
ACS Nano
Publisher
American Chemical Society (ACS)
Volume
20
Issue
4
Pages
3886–3903
Publication date
19 January 2026
Language
EN