Operando Electrical Conductivity and Complex Permittivity Study on Vanadia Oxidation Catalysts

Wernbacher, Anna Maria; Eichelbaum, Maik; Risse, Thomas; Cap, Sébastien; Trunschke, Annette; Schlögl, Robert

doi:10.1021/acs.jpcc.8b07417

Cited by 19 publications

(62 citation statements)

References 85 publications

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“…The "seven pillars" of oxidation catalysis indicate the several factors contributing to reactivity in oxidation reactions: (1) lattice oxygen, (2) metal-oxygen bond strength, (3) host structure, (4) redox properties, (5) multifunctionality of active sites, (6) site isolation, and (7) phase cooperation. 19,20 In the case of vanadium-based oxide catalysts, selectivity has been also related to surface enrichment of one of the metal ions in the presence of reaction feed containing steam [21][22][23] and the associated surface potential barrier, 21,24,25 highlighting that the catalyst dynamics also plays a role. Due to the multiple requirements and the intricacy of the underlying processes, the theoretical description of selective oxidation and the search for new catalysts is extremely challenging.…”

Section: Alkane Selective Oxidationmentioning

confidence: 99%

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

et al. 2021

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The performance in heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes (e.g., the different surface chemical reactions, and the dynamic restructuring of the catalyst material at reaction conditions). Modeling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters (“materials genes”) reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of “clean data,” containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design. Impact statement Artificial intelligence (AI) accepts that there are relationships or correlations that cannot be expressed in terms of a closed mathematical form or an easy-to-do numerical simulation. For the function of materials, for example, catalysis, AI may well capture the behavior better than the theory of the past. However, currently the flexibility of AI comes together with a lack of interpretability, and AI can only predict aspects that were included in the training. The approach proposed and demonstrated in this IMPACT article is interpretable. It combines detailed experimental data (called "clean data") and symbolic regression for the identification of the key descriptive parameters (called "materials genes") that are correlated with the materials function. The approach demonstrated here for the catalytic oxidation of propane will accelerate the discovery of improved or novel materials while also enhancing physical understanding.

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Section: Alkane Selective Oxidationmentioning

confidence: 99%

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

et al. 2021

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“…The "seven pillars" of oxidation catalysis indicate the several factors contributing to reactivity in oxidation reactions: 1. lattice oxygen, 2. metal-oxygen bond strength, 3. host structure, 4. redox properties, 5. multifunctionality of active sites, 6. site isolation, and 7. phase cooperation. 18,19 In the case of vanadiumbased oxide catalysts, selectivity has been also related to surface enrichment of one of the metal ions in the presence of reaction feed containing steam [20][21][22] and the associated surface potential barrier, 20,23,24 highlighting that the catalyst dynamics also plays a role. Due to the multiple requirements and the intricacy of the underlying processes, the theoretical description of selective oxidation and the search for new catalysts is extremely challenging.…”

Section: Alkane Selective Oxidationmentioning

confidence: 99%

Materials Genes of Heterogeneous Catalysis from Clean Experiments and Artificial Intelligence

Foppa

Ghiringhelli

Girgsdies

et al. 2021

Preprint

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Heterogeneous catalysis is an example of a complex materials function, governed by an intricate interplay of several processes, e.g., the different surface chemical reactions, and the dynamic re-structuring of the catalyst material at reaction conditions. Modelling the full catalytic progression via first-principles statistical mechanics is impractical, if not impossible. Instead, we show here how a tailored artificial-intelligence approach can be applied, even to a small number of materials, to model catalysis and determine the key descriptive parameters ("materials genes") reflecting the processes that trigger, facilitate, or hinder catalyst performance. We start from a consistent experimental set of "clean data", containing nine vanadium-based oxidation catalysts. These materials were synthesized, fully characterized, and tested according to standardized protocols. By applying the symbolic-regression SISSO approach, we identify correlations between the few most relevant materials properties and their reactivity. This approach highlights the underlying physicochemical processes, and accelerates catalyst design.<br>

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“…The powder permittivities, denoted with subscript "p", have to be converted to bulk-like permittivities (subscript "b") to account for packing efficiency. As in previous work, [8] we apply the Landau-Lifshitz-Looyenga formulas, [15,16] see eqns. ( 5) and (6).…”

Section: Mcpt Theorymentioning

confidence: 99%

Towards automation of operando experiments: A case study in contactless conductivity measurements

Kraus

Wolf

Prinz

et al. 2021

Preprint

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Automation of experiments is a key component on the path of digitalisation in catalysis and related sciences. Here we present the lessons learned and caveats avoided during the automation of our contactless conductivity measurement set-up, capable of operando measurement of catalytic samples. We briefly discuss the motivation behind the work, the technical groundwork required, and the philosophy guiding our design. The main body of this work is dedicated to the detailing of the implementation of the automation, data structures, as well as the modular data processing pipeline. The open-source toolset developed as part of this work allows us to carry out unattended and reproducible experiments, as well as post-process data according to current best practice. This process is illustrated by implementing two routine sample protocols, one of which was included in the Handbook of Catalysis, providing several case studies showing the benefits of such automation, including increased throughput and higher data quality. The datasets included as part of this work contain catalytic and operando conductivity data, and are self-consistent, annotated with metadata, and are available on a public repository in a machine-readable form. We hope the datasets as well as the tools and workflows developed as part of this work will be an useful guide on the path towards automation and digital catalysis.

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Operando Electrical Conductivity and Complex Permittivity Study on Vanadia Oxidation Catalysts

Cited by 19 publications

References 85 publications

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

Materials genes of heterogeneous catalysis from clean experiments and artificial intelligence

Materials Genes of Heterogeneous Catalysis from Clean Experiments and Artificial Intelligence

Towards automation of operando experiments: A case study in contactless conductivity measurements

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