On the Promoting Effects of Te and Nb in the Activity and Selectivity of M1 MoV-Oxides for Ethane Oxidative Dehydrogenation

Melzer, Daniel; Mestl, Gerhard; Wanninger, Klaus; Jentys, Andreas; Sanchez‐Sanchez, Maricruz; Lercher, Johannes A.

doi:10.1007/s11244-020-01304-0

Cited by 10 publications

(7 citation statements)

References 47 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…4), where the C–H bond is activated by a V 5+ site, α-hydrogen abstraction of allylic species by a Te 4+ site, and an N-insertion by a Mo 6+ site. 67 Moreover, it was found that the AN yield could be improved with a higher extent of the M1 phase (Fig. 5).…”

Section: Synthesis Of Nitrilesmentioning

confidence: 87%

See 1 more Smart Citation

Light hydrocarbon conversion to acrylonitrile and acetonitrile – a review

Trangwachirachai

Lin

2023

Dalton Trans.

View full text Add to dashboard Cite

show abstract

Section: Synthesis Of Nitrilesmentioning

confidence: 87%

Section: Perspectivementioning

confidence: 99%

Light hydrocarbon conversion to acrylonitrile and acetonitrile – a review

Trangwachirachai

Lin

2023

Dalton Trans.

View full text Add to dashboard Cite

show abstract

“…The particular difficulty is to reach a sufficient yield of valuable products such as olefins or unsaturated oxygenates, that is, to achieve high selectivity of these products at high conversion, while avoiding total oxidation to CO 2 . Numerous studies in the literature and in our own laboratories have investigated these reactions. − However, very diverse reaction conditions have been applied, as shown by the literature survey presented in Table S1. This makes a direct comparison of the catalysts impossible and potentially systematic trends not apparent.…”

Section: Alkane Selective Oxidationmentioning

confidence: 99%

Data-Centric Heterogeneous Catalysis: Identifying Rules and Materials Genes of Alkane Selective Oxidation

Foppa

Rüther²,

Geske³

et al. 2023

J. Am. Chem. Soc.

View full text Add to dashboard Cite

Artificial intelligence (AI) can accelerate catalyst design by identifying key physicochemical descriptive parameters correlated with the underlying processes triggering, favoring, or hindering the performance. In analogy to genes in biology, these parameters might be called “materials genes” of heterogeneous catalysis. However, widely used AI methods require big data, and only the smallest part of the available data meets the quality requirement for data-efficient AI. Here, we use rigorous experimental procedures, designed to consistently take into account the kinetics of the catalyst active states formation, to measure 55 physicochemical parameters as well as the reactivity of 12 catalysts toward ethane, propane, and n-butane oxidation reactions. These materials are based on vanadium or manganese redox-active elements and present diverse phase compositions, crystallinities, and catalytic behaviors. By applying the sure-independence-screening-and-sparsifying-operator symbolic-regression approach to the consistent data set, we identify nonlinear property–function relationships depending on several key parameters and reflecting the intricate interplay of processes that govern the formation of olefins and oxygenates: local transport, site isolation, surface redox activity, adsorption, and the material dynamical restructuring under reaction conditions. These processes are captured by parameters derived from N2 adsorption, X-ray photoelectron spectroscopy (XPS), and near-ambient-pressure in situ XPS. The data-centric approach indicates the most relevant characterization techniques to be used for catalyst design and provides “rules” on how the catalyst properties may be tuned in order to achieve the desired performance.

show abstract

“…5,13 The oxidative dehydrogenation (ODH) of ethane (C 2 ) is a prospective reaction route for the on-demand production of ethylene using similar catalysts. 14,15 One of the main challenges in understanding the underlying catalytic principles is the dynamic behaviour and selfadapting nature of the catalysts under reaction conditions. [16][17][18][19] In literature, this phenomenon is described as 'flexible surfaces', where the chemical potential of the gas phase affects the surface state and changes the surface composition of the bulk catalyst.…”

Section: Introductionmentioning

confidence: 99%