Descriptor–property relationships in heterogeneous catalysis: exploiting synergies between statistics and fundamental kinetic modelling

Abstract

Combined kinetic and statistical approach to shed light on the link between kinetically-relevant descriptors and easily tuneable catalyst properties.

Open Data in Catalysis: From Today's Big Picture to the Future of Small Data

“…Kinetic models can be employed for datasets of small volumes, gaining physico‐chemically meaningful insights on the catalytic and reaction systems [42b,62, 82] . The more fundamental the model, the more powerful it will be to generate knowledge and extrapolate it to other operating conditions and catalysts.…”

“…Nevertheless, the existing information (particularly in the data, but also a priori from quantum calculations or operando characterization, for instance) will obviously limit the knowledge that can be generated via the kinetic model [69] . When designing catalysts, kinetic models are particularly well‐suited, enabling the meaningful identification of optimal catalysts, [42b,83] because the chemical space is inherently reduced by physico‐chemical limitations (e. g. thermodynamic equilibria) and adequately mapped via fundamental relationships.…”

Catalyst screening for the oxidative coupling of methane: from isothermal to adiabatic operation via microkinetic simulations

“…The impact of the operating conditions on the top 10% catalysts ranking is represented by the extent of the overlap between the two boxes (shaded area in the figure). The visual interpretation was supported by statistical testing 47,48 , to identify descriptors that discriminate between both catalyst groups of interest, as described in detail in previous work 49 . In case of weak correlation between the performances in the investigated scenarios, such as in Figure 2 case B, an additional analysis was performed, to gain further insights into the cause of the spread in the data.…”

“…The performances of several OCM catalysts reported in literature were reproduced in the microkinetic simulations herein via a methodology previously developed in our research group, encompassing both microkinetics and statistical testing 49 . A total of 220 realistic catalyst descriptors combinations, corresponding to a variety of real OCM catalysts, were identified starting from six literature datasets: two datasets from the work of Kondratenko et al 50 , two datasets from the work of Olivier et al 51 , one dataset from the work of Huang et al 52 and one dataset from the work of Shahri and Pour 53 .…”

Bifunctional Co‐based Catalysts for Fischer‐Tropsch Synthesis: Descriptors Affecting the Product Distribution

“…The broad variety of concepts available to tailor the product distribution in FT synthesis by combination with additional catalytic functionality for HP, however, still lacks the correlation of the identified product spectrum with the structural properties of the catalyst material. Therefore, the identification of suitable descriptors, as already introduced in heterogeneous catalysis, would be the basis [38,39] . Those descriptors should be easily accessible by standard material characterization techniques and tunable during the material synthesis in addition to their impact on the product distribution in FT synthesis.…”