| |
Record |
Links |
|
Author |
Cai, Y.; Michiels, R.; De Luca, F.; Neyts, E.; Tu, X.; Bogaerts, A.; Gerrits, N. |
|
| |
Title |
Improving Molecule–Metal Surface Reaction Networks Using the Meta-Generalized Gradient Approximation: CO2Hydrogenation |
Type |
A1 Journal Article |
| |
Year  |
2024 |
Publication |
The Journal of Physical Chemistry C |
Abbreviated Journal |
J. Phys. Chem. C |
|
| |
Volume |
128 |
Issue |
21 |
Pages |
8611-8620 |
|
| |
Keywords |
A1 Journal Article; Plasma, laser ablation and surface modeling Antwerp (PLASMANT) ; |
|
| |
Abstract |
Density functional theory is widely used to gain insights into molecule−metal surface reaction networks, which is important for a better understanding of catalysis. However, it is well-known that generalized gradient approximation (GGA)
density functionals (DFs), most often used for the study of reaction networks, struggle to correctly describe both gas-phase molecules and metal surfaces. Also, GGA DFs typically underestimate reaction barriers due to an underestimation of the selfinteraction energy. Screened hybrid GGA DFs have been shown to reduce this problem but are currently intractable for wide usage. In this work, we use a more affordable meta-GGA (mGGA) DF in combination with a nonlocal correlation DF for the first time to study and gain new insights into a catalytically important surface
reaction network, namely, CO2 hydrogenation on Cu. We show that the mGGA DF used, namely, rMS-RPBEl-rVV10, outperforms typical GGA DFs by providing similar or better predictions for metals and molecules, as well as molecule−metal surface adsorption
and activation energies. Hence, it is a better choice for constructing molecule−metal surface reaction networks. |
|
| |
Address |
|
|
| |
Corporate Author |
|
Thesis |
|
|
| |
Publisher |
|
Place of Publication |
|
Editor |
|
|
| |
Language |
|
Wos |
|
Publication Date |
2024-05-30 |
|
| |
Series Editor |
|
Series Title |
|
Abbreviated Series Title |
|
|
| |
Series Volume |
|
Series Issue |
|
Edition |
|
|
| |
ISSN |
1932-7447 |
ISBN |
|
Additional Links |
|
|
| |
Impact Factor |
3.7 |
Times cited |
|
Open Access |
|
|
| |
Notes |
H2020 Marie Sklodowska-Curie Actions, 813393 ; Fonds Wetenschappelijk Onderzoek, 1114921N ; H2020 European Research Council, 810182 ; Nederlandse Organisatie voor Wetenschappelijk Onderzoek, 019.202EN.012 ; |
Approved |
Most recent IF: 3.7; 2024 IF: 4.536 |
|
| |
Call Number |
PLASMANT @ plasmant @ |
Serial |
9248 |
|
| Permanent link to this record |
