Novel Powder-Supported Size-Selected Clusters for Heterogeneous Catalysis under Realistic Reaction Conditions

2012 ◽  
Vol 116 (50) ◽  
pp. 26295-26299 ◽  
Author(s):  
V. Habibpour ◽  
M. Y. Song ◽  
Z. W. Wang ◽  
J. Cookson ◽  
C. M. Brown ◽  
...  
2018 ◽  
Vol 14 ◽  
pp. 1655-1659 ◽  
Author(s):  
Ugo Azzena ◽  
Massimo Carraro ◽  
Gloria Modugno ◽  
Luisa Pisano ◽  
Luigi Urtis

The application of heterogeneous catalysis and green solvents to the set up of widely employed reactions is a challenge in contemporary organic chemistry. We applied such an approach to the synthesis and further conversion of tetrahydropyranyl ethers, an important class of compounds widely employed in multistep syntheses. Several alcohols and phenols were almost quantitatively converted into the corresponding tetrahydropyranyl ethers in cyclopentyl methyl ether or 2-methyltetrahydrofuran employing NH4HSO4 supported on SiO2 as a recyclable acidic catalyst. Easy work up of the reaction mixtures and the versatility of the solvents allowed further conversion of the reaction products under one-pot reaction conditions.


2020 ◽  
Vol 22 (15) ◽  
pp. 7738-7746
Author(s):  
Hui Zhou ◽  
Dong Wang ◽  
Xue-Qing Gong

In heterogeneous catalysis, surface hydroxylation is well recognized as a common phenomenon under realistic reaction conditions.


Author(s):  
James F. Haw ◽  
Patrick W. Goguen ◽  
Teng Xu ◽  
Timothy W. Skloss ◽  
Weiguo Song ◽  
...  

Nanomaterials ◽  
2021 ◽  
Vol 11 (10) ◽  
pp. 2756
Author(s):  
Sascha Keßler ◽  
Elrike R. Reinalter ◽  
Johannes Schmidt ◽  
Helmut Cölfen

The tetramethylammonium hydroxide (TMAH)-controlled alkaline etching of nickel hexacyanoferrate (NiHCF) mesocrystals is explored. The alkaline etching enables the formation of hollow framework structures with an increased surface area, the exposure of active Ni and Fe sites and the retention of morphology. The ambient reaction conditions enable the establishment of a sustainable production. Our work reveals novel perspectives on the eco-friendly synthesis of hollow and colloidal superstructures for the efficient degradation of the organic contaminants rhodamine-B and bisphenol-A. In the case of peroxomonosulfate (PMS)-mediated bisphenol-A degradation, the rate constant of the etched mesoframes was 10,000 times higher indicating their significant catalytic activity.


2021 ◽  
Author(s):  
Lucas Foppa ◽  
Luca Ghiringhelli ◽  
Frank Girgsdies ◽  
Maike Hashagen ◽  
Pierre Kube ◽  
...  

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>


ChemCatChem ◽  
2016 ◽  
Vol 9 (1) ◽  
pp. 17-29 ◽  
Author(s):  
Kai F. Kalz ◽  
Ralph Kraehnert ◽  
Muslim Dvoyashkin ◽  
Roland Dittmeyer ◽  
Roger Gläser ◽  
...  

2021 ◽  
Author(s):  
Lucas Foppa ◽  
Luca Ghiringhelli ◽  
Frank Girgsdies ◽  
Maike Hashagen ◽  
Pierre Kube ◽  
...  

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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