Strain Controlling Catalytic Efficiency
of Water Oxidation for Ni 1−x Fe x OOH
Alloy
Ester Korkus Hamal and Maytal Caspary Toroker
1 Introduction
Water splitting has attracted great interest in recent years due to its potential of generating energy without causing pollution [1]. Further advancement in this technology is
an important challenge due to the low performance or stability of available catalysts
[2]. Hence, a top priority is to design better catalysts for water splitting [3, 4].
One of the best candidates is nickel oxyhydroxide with iron content
(Ni 1−x Fe x OOH) that has excellent efficiency at alkaline conditions and is now
studied widely [5–10]. An outstanding example is utilizing the Ni 1−x Fe x OOH alloy
as a catalyst atop of hematite photoanode [11–13]. Another example is a layered
BiVO 4 /FeOOH/NiOOH photoanode [14].
Iron content is essential since without this component the material’s catalytic
activity is very poor [15, 16]. Many experimental and theoretical studies have been
devoted to understand why iron improves performance [17–19]. Several insights have
been gained. The main observation was that iron is the active site. Furthermore, iron
can have several oxidation states which facilitates chemical activity.
Hence, altering material composition through doping or alloying is a natural
route to engineer better catalysts. External conditions such as the application of
load or interfacing with a substrate can also have an effect on catalytic performance.
However, using strain as a design strategy for improving catalysts has been less
explored [20, 21].
In this work, we consider the effect of strain on water oxidation with
Ni 1−x Fe x OOH. Our approach is to use density functional theory + U (DFT + U) in
order to model water oxidation while the surface is contracted/expanded. This study
E. K. Hamal · M. C. Toroker (B)
Department of Materials Science and Engineering, Technion-Israel Institute of Technology,
3200003 Haifa, Israel
e-mail: maytalc@technion.ac.il
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2021
E. J. Maginn and J. Errington (eds.), Foundations of Molecular Modeling
and Simulation, Molecular Modeling and Simulation,
https://doi.org/10.1007/978-981-33-6639-8_1
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