Summary
Methane is a particularly problematic greenhouse gas as its impact is 25 times greater than carbon dioxide over a 100-year period. Human activity has increased the amount of methane in the atmosphere, contributing to climate change. Therefore, there is an imperative for the transformation of methane into useful chemicals. At this time, the most economically available route for the conversion of methane into more valuable chemicals is via synthesis gas, a mixture of CO and H2. The only large-scale process for natural gas conversion involves a reaction known as methane-steam reforming. However, it is an endothermic process that requires high operating temperatures. Methane partial oxidation (MPO) is a promising energy saving alternative because it does not require the use of superheated steam. A major goal is to find a catalyst that exhibits high activity, selectivity and stability at the relevant reaction conditions.
This project envisions the computational prediction of novel MPO catalysts that overcome this challenges by computationally screening a large set of materials consisting of precious metals (Rh, Pd, Pt, Au) and more affordable metals (Co, Ni, Cu) supported on transition metal carbides (TMCs, TM = Ti, Zr, Hf, V, Nb, Ta, Mo, W). These type of catalysts have exhibited outstanding performance in other chemical reactions in the past 5 years. To this end, state-of-the-art Density Functional Theory and Kinetic Monte Carlo frameworks will be employed to provide direct predictions of activity, selectivity, stability and yield for the most promising catalysts at relevant reaction conditions. Moreover, the large amount of results gathered from this project will serve as a big dataset to conduct descriptor analysis, and will suggest key properties that correlate well with their activity for C-H and O-H bond activation. The results obtained will be discussed with our experimental collaborators, who will prepare a selected set of catalysts based on my findings.
This project envisions the computational prediction of novel MPO catalysts that overcome this challenges by computationally screening a large set of materials consisting of precious metals (Rh, Pd, Pt, Au) and more affordable metals (Co, Ni, Cu) supported on transition metal carbides (TMCs, TM = Ti, Zr, Hf, V, Nb, Ta, Mo, W). These type of catalysts have exhibited outstanding performance in other chemical reactions in the past 5 years. To this end, state-of-the-art Density Functional Theory and Kinetic Monte Carlo frameworks will be employed to provide direct predictions of activity, selectivity, stability and yield for the most promising catalysts at relevant reaction conditions. Moreover, the large amount of results gathered from this project will serve as a big dataset to conduct descriptor analysis, and will suggest key properties that correlate well with their activity for C-H and O-H bond activation. The results obtained will be discussed with our experimental collaborators, who will prepare a selected set of catalysts based on my findings.
Unfold all
/
Fold all
More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/891756 |
| Start date: | 01-03-2021 |
| End date: | 28-02-2023 |
| Total budget - Public funding: | 212 933,76 Euro - 212 933,00 Euro |
Cordis data
Original description
Methane is a particularly problematic greenhouse gas as its impact is 25 times greater than carbon dioxide over a 100-year period. Human activity has increased the amount of methane in the atmosphere, contributing to climate change. Therefore, there is an imperative for the transformation of methane into useful chemicals. At this time, the most economically available route for the conversion of methane into more valuable chemicals is via synthesis gas, a mixture of CO and H2. The only large-scale process for natural gas conversion involves a reaction known as methane-steam reforming. However, it is an endothermic process that requires high operating temperatures. Methane partial oxidation (MPO) is a promising energy saving alternative because it does not require the use of superheated steam. A major goal is to find a catalyst that exhibits high activity, selectivity and stability at the relevant reaction conditions.This project envisions the computational prediction of novel MPO catalysts that overcome this challenges by computationally screening a large set of materials consisting of precious metals (Rh, Pd, Pt, Au) and more affordable metals (Co, Ni, Cu) supported on transition metal carbides (TMCs, TM = Ti, Zr, Hf, V, Nb, Ta, Mo, W). These type of catalysts have exhibited outstanding performance in other chemical reactions in the past 5 years. To this end, state-of-the-art Density Functional Theory and Kinetic Monte Carlo frameworks will be employed to provide direct predictions of activity, selectivity, stability and yield for the most promising catalysts at relevant reaction conditions. Moreover, the large amount of results gathered from this project will serve as a big dataset to conduct descriptor analysis, and will suggest key properties that correlate well with their activity for C-H and O-H bond activation. The results obtained will be discussed with our experimental collaborators, who will prepare a selected set of catalysts based on my findings.
Status
CLOSEDCall topic
MSCA-IF-2019Update Date
28-04-2024
Images
No images available.
Geographical location(s)
