English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks

Timoshenko, J., Jeon, H., Sinev, I., Haase, F., Herzog, A., & Roldan Cuenya, B. (2020). Linking the evolution of catalytic properties and structural changes in copper–zinc nanocatalysts using operando EXAFS and neural-networks. Chemical Science, 11(14), 3727-3736. doi:10.1039/d0sc00382d.

Item is

Basic

show hide
Genre: Journal Article

Files

show Files
hide Files
:
d0sc00382d.pdf (Publisher version), 2MB
Name:
d0sc00382d.pdf
Description:
-
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
2020
Copyright Info:
The Author(s)

Locators

show

Creators

show
hide
 Creators:
Timoshenko, Janis1, Author              
Jeon, Hyosang1, Author              
Sinev, Ilya1, Author              
Haase, Felix1, Author              
Herzog, Antonia1, Author              
Roldan Cuenya, Beatriz1, Author              
Affiliations:
1Interface Science, Fritz Haber Institute, Max Planck Society, ou_2461712              

Content

show
hide
Free keywords: -
 Abstract: Understanding the evolution of unique structural motifs in bimetallic catalysts under reaction conditions, and linking them to the observed catalytic properties is necessary for the rational design of the next generation of catalytic materials. Extended X-ray absorption fine structure (EXAFS) spectroscopy is a premier experimental method to address this issue, providing the possibility to track the changes in the structure of working catalysts. Unfortunately, the intrinsic heterogeneity and enhanced disorder characteristic of catalytic materials experiencing structural transformations under reaction conditions, as well as the low signal-to-noise ratio that is common for in situ EXAFS spectra hinder the application of conventional data analysis approaches. Here we address this problem by employing machine learning methods (artificial neural networks) to establish the relationship between EXAFS features and structural motifs in metals as well as oxide materials. We apply this approach to time-dependent EXAFS spectra acquired from copper–zinc nanoparticles during the electrochemical reduction of CO2 to reveal the details of the composition-dependent structural evolution and brass alloy formation, and their correlation with the catalytic selectivity of these materials.

Details

show
hide
Language(s): eng - English
 Dates: 2020-01-202020-03-052020-04-14
 Publication Status: Published online
 Pages: 10
 Publishing info: -
 Table of Contents: -
 Rev. Type: Peer
 Identifiers: DOI: 10.1039/d0sc00382d
 Degree: -

Event

show

Legal Case

show

Project information

show hide
Project name : OPERANDOCAT - In situ and Operando Nanocatalysis: Size, Shape and Chemical State Effects
Grant ID : 725915
Funding program : Horizon 2020 (H2020)
Funding organization : European Commission (EC)

Source 1

show
hide
Title: Chemical Science
  Other : Chem. Sci.
Source Genre: Journal
 Creator(s):
Affiliations:
Publ. Info: Cambridge, UK : Royal Society of Chemistry
Pages: 10 Volume / Issue: 11 (14) Sequence Number: - Start / End Page: 3727 - 3736 Identifier: ISSN: 2041-6520
CoNE: https://pure.mpg.de/cone/journals/resource/2041-6520