The electrocatalytic oxygen evolution reaction (OER) presents the key transformation in electrochemical water‐splitting majorly determining energy efficiency and economics of hydrogen generation. In this study, the kinetics of the OER over Ni−Co oxide structured by KIT‐6 templating and non‐structured Ni−Co oxide catalysts in alkaline solution have been investigated aiming for insight with regard to the respective kinetically relevant surface reactions. Steady‐state Tafel plot analysis and electrochemical impedance spectroscopy (EIS) were used to determine kinetic parameters, Tafel slopes and the order of reaction. A dual Tafel slope behavior was observed for both catalysts. Tafel slopes of ca. 40 and 120 mV dec−1 and 90 and 180 mV dec−1 at low and high overpotentials appear for structured and non‐structured Ni−Co oxide, respectively. A reaction order of unity was observed for structured Ni−Co oxide, while non‐structured Ni−Co oxide possessed a fractional reaction order in the high overpotential region. The kinetics of OER over structured Ni−Co oxide were governed by Langmuir adsorption with the rate‐limiting step after primary adsorption of surface intermediates. In contrast, non‐structured Ni−Co oxide obeyed the Temkin adsorption isotherm condition with the primary adsorption step being rate‐limiting.
Artificial
intelligence and various types of machine learning are
of increasing interest not only in the natural sciences but also in
a wide range of applied and engineering sciences. In this study, we
rethink the view on combinatorial heterogeneous catalysis and combine
machine learning methods with combinatorial approaches in electrocatalysis.
Several machine learning methods were used to forecast water oxidation
catalysts on the basis of literature published data sets and data
from our own work. The machine learning models exhibit a decent prediction
precision based on the data sets available and confirm that even simple
models are suitable for good forecasts.
Modern research methods produce large amounts of scientifically valuable data. Tools to process and analyze such data have advanced rapidly. Yet, access to large amounts of high-quality data remains limited in many fields, including catalysis research. Implementing the concept of FAIR data (Findable, Accessible, Interoperable, Reusable) in the catalysis community would improve this situation dramatically. The German NFDI initiative (National Research Data Infrastructure) aims to create a unique research data infrastructure covering all scientific disciplines. One of the consortia, NFDI4Cat, proposes a concept that serves all aspects and fields of catalysis research. We present a perspective on the challenging path ahead. Starting out from the current state, research needs are identified. A vision for a integrating all research data along the catalysis value chain, from molecule to chemical process, is developed. Respective core development topics are discussed, including ontologies, metadata, required infrastructure, IP, and the embedding into research community. This Concept paper aims to inspire not only researchers in the catalysis field, but to spark similar efforts also in other disciplines and on an international level.This publication is part of a Special Collection on "Data Science in Catalysis". Please check the ChemCatChem homepage for more articles in the collection.
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