Molecular Structures of Surface Metal Oxide Species

Author(s):  
Israel Wachs
2021 ◽  
Author(s):  
Daniyal Kiani ◽  
Sagar Sourav ◽  
Jonas Baltrusaitis ◽  
Israel E Wachs

The experimentally validated computational models developed herein, for the first time, show that Mn-promotion does not enhance the activity of the surface Na2WO4 catalytic active sites for CH4 heterolytic dissociation...


Author(s):  
M. H. Yao ◽  
David J. Smith ◽  
I. E. Wachs

In the present work we have studied two-dimensional metal oxide overlayers (Re2O7, WO3, etc) deposited on a second high-surface-area metal oxide substrate (TiO2, Al2O3, etc). The molecular structure of these surface metal oxide species has been extensively studied in the past decade by Raman spectroscopy and EXAFS because of their importance in catalytic applications. However, direct observations of these overlayers are still needed in order to better understand the basic properties of the overlayer species. High resolution electron microscopy(HREM) is one of the most commonly used methods for direct characterization of small catalyst particles. It can provide particle size distributions and information about particle disposition over the support materials. But it is generally believed that bright field imaging is only well suited for particles sizes larger than about 10 Å. For smaller metal particles on typical supports, HAADF in STEM has been proposed as a better choice.


Catalysts ◽  
2021 ◽  
Vol 11 (9) ◽  
pp. 1107
Author(s):  
Zhuoying Jiang ◽  
Jiajie Hu ◽  
Matthew Tong ◽  
Anna C. Samia ◽  
Huichun (Judy) Zhang ◽  
...  

This paper describes an innovative machine learning (ML) model to predict the performance of different metal oxide photocatalysts on a wide range of contaminants. The molecular structures of metal oxide photocatalysts are encoded with a crystal graph convolution neural network (CGCNN). The structure of organic compounds is encoded via digital molecular fingerprints (MF). The encoded features of the photocatalysts and contaminants are input to an artificial neural network (ANN), named as CGCNN-MF-ANN model. The CGCNN-MF-ANN model has achieved a very good prediction of the photocatalytic degradation rate constants by different photocatalysts over a wide range of organic contaminants. The effects of the data training strategy on the ML model performance are compared. The effects of different factors on photocatalytic degradation performance are further evaluated by feature importance analyses. Examples are illustrated on the use of this novel ML model for optimal photocatalyst selection and for assessing other types of photocatalysts for different environmental applications.


1998 ◽  
Vol 132 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Marlene M Ostromecki ◽  
Loyd J Burcham ◽  
Israel E Wachs ◽  
Narayanan Ramani ◽  
John G Ekerdt

1996 ◽  
Vol 32 (1-4) ◽  
pp. 47-55 ◽  
Author(s):  
Israel E. Wachs ◽  
Jih-Mirn Jehng ◽  
Goutam Deo ◽  
Bert M. Weckhuysen ◽  
Vadim V. Guliants ◽  
...  

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