B. Ariana Thompson , Shengguang Wang , Konstantinos Goulas , M. Ross Kunz , Rebecca Fushimi
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引用次数: 0
Abstract
Transient kinetic data contain a wealth of information about intrinsic features of a catalyst as well as the reaction mechanism. Currently, high volume transient data is underutilized, and data science methods could increase the value of information that can be extracted from this data, integrate experimental with theoretical data sources, and accelerate the pace of catalyst technology advancement. Transient kinetic characterizations with simple probe molecules exhibiting reversible adsorption, irreversible adsorption and bulk-surface diffusion are presented as training components for similar experiments with more complex surface reactions. By increasing the availability and accessibility of transient kinetic data through details of its structure and acquisition, we aim to decrease the barrier for data scientists to apply machine learning methods to this valuable data source.
期刊介绍:
The Journal of Catalysis publishes scholarly articles on both heterogeneous and homogeneous catalysis, covering a wide range of chemical transformations. These include various types of catalysis, such as those mediated by photons, plasmons, and electrons. The focus of the studies is to understand the relationship between catalytic function and the underlying chemical properties of surfaces and metal complexes.
The articles in the journal offer innovative concepts and explore the synthesis and kinetics of inorganic solids and homogeneous complexes. Furthermore, they discuss spectroscopic techniques for characterizing catalysts, investigate the interaction of probes and reacting species with catalysts, and employ theoretical methods.
The research presented in the journal should have direct relevance to the field of catalytic processes, addressing either fundamental aspects or applications of catalysis.