Trends in Oxygen Electrocatalysis of 3 d‐Layered (Oxy)(Hydro)Oxides. Issue 15 (3rd July 2019) - Zhao, Zhenghang; Schlexer Lamoureux, Philomena; Kulkarni, Ambarish; Bajdich, Michal
Abstract: First‐row layered transition metal (oxy)(hydro)oxides (LTMOs) form an important class of earth‐abundant materials. They are well‐known as active alkaline oxygen evolution reaction (OER) catalysts, [1, 5] and are also often used as metal‐ion battery anodes [6] or as metal‐air bifunctional electrodes. [7] However, their electrochemical activities, particularly for the oxygen reduction reaction (ORR), across the whole 3d ‐element series remain largely unexplored. In this work, we perform a systematic screening of these catalysts for both OER and ORR using a surface edge‐site model with exposed active sites for metal double hydroxides M(OH)2, oxyhydroxides MOOH and oxides MO2 . We establish OER and ORR activities and scaling relations of the whole series across +2, +3 and +4 oxidation states, and successfully reproduce the experimental activities of a few pure layered (oxy)(hydro)oxides. We predict CoOOH/CoO2 and NiOOH/NiO2 as active and stable OER catalysts. We also predict Fe(OH)2 /FeOOH, Mn(OH)2 /MnOOH and Co(OH)2 as active and stable ORR catalysts. This makes Co‐(oxy)(hydro)oxides only bifunctional catalyst in this series. Using linear regression, our results indicate that trends across the 3d ‐series can be obtained from only a few bulk, surface and atomic type descriptors. Particularly, we identify that the number of outer d ‐electrons at the surface‐active site as the most important descriptor of activity. Abstract : ORR/OER catalysts : First‐row layered transition metal (oxy)(hydro)oxides (LTMOs) are appealing as Pt‐free catalysts for oxygen reduction and evolution reactions (ORR/OER). Through density functional theory and machine learning regression analysis of LTMOs as electrocatalysts, we identify electrochemical trends and descriptors to design and predict the ORR and OER of LTMOs.