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Rate‐Reactivity Model: A New Theoretical Basis for Systematic Kinetic Characterization of Heterogeneous Catalysts
Author(s) -
Yablonsky Gregory S.,
Redekop Evgeniy A.,
Constales Denis,
Gleaves John T.,
Marin Guy B.
Publication year - 2016
Publication title -
international journal of chemical kinetics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.341
H-Index - 68
eISSN - 1097-4601
pISSN - 0538-8066
DOI - 10.1002/kin.20988
Subject(s) - chemistry , steady state (chemistry) , reactions on surfaces , kinetic energy , thermodynamics , pairwise comparison , biochemical engineering , biological system , observable , catalysis , statistical physics , computational chemistry , computer science , physics , organic chemistry , classical mechanics , quantum mechanics , artificial intelligence , engineering , biology
ABSTRACT Non‐steady‐state kinetic measurements contain a wealth of information about catalytic reactions and other gas–solid chemical interactions, which is extracted from experimental data via kinetic models. The standard mathematical framework of microkinetic models, which are typically used in computational catalysis and for advanced modeling of steady‐state data, encounters multiple challenges when applied to non‐steady‐state data. Robust phenomenological models, such as the steady‐state Langmuir–Hinshelwood–Hougen–Watson equations, are presently unavailable for non‐steady‐state data. Herein, a novel modeling framework is proposed to fulfill this need. The rate‐reactivity model (RRM) is formulated in terms of experimentally observable quantities including the gaseous transformation rates, concentrations, and surface uptakes. The model is linear with respect to these quantities and their pairwise products, and it is also linear in terms of its parameters (reactivities). The RRM parameters have a clear physicochemical meaning and fully characterize the kinetic behavior of a specific catalyst state, but unlike microkinetic models that rely on hypothetical surface intermediates and specific reaction networks, the RRM does not require any assumptions regarding the underlying mechanism. The systematic RRM‐based procedure outlined in this paper enables an effective comparison of various catalysts and the construction of more detailed microkinetic models in a rational manner. The model was applied to temporal analysis of products pulse‐response data as an example, but it is more generally applicable to other non‐steady‐state techniques that provide time‐resolved rates and concentrations. Several numerical examples are given to illustrate the application of the model to simple model reactions.