Biomass valorization is an interesting alternative to fossil resources, which is frequently performed via heterogeneous catalysis. Designing new catalysts is a challenging task that can be significantly accelerated in silico. However, biomass molecules are often complex and highly oxygenated, hence rendering calculations more difficult and time consuming. Among these compounds, polyols are particularly important. We developed linear relations of the Brønsted-Evans-Polanyi (BEP) type from the DFT study of C-H or O-H bond dissociation elementary steps for a family of monoalcohol molecules on metallic catalysts (Co, Ni, Ru, Rh, Pd, Ir, Pt). Such relations aim at predicting activation energies from reaction energies. The accuracy of the obtained linear energy models is better than 0.10 eV on the sampling set. Then, the relations were applied for the prediction of the dehydrogenation elementary steps of glycerol, chosen as a prototype of polyalcohols, with an accuracy better than 0.10 eV and with a systematic error around ±0.10 eV for Rh. Keeping in mind that the main difference between glycerol and monoalcohols comes from intramolecular H-bonds present in the former, we designed linear relations for water-assisted dehydrogenation of monoalcohols. These new relations allowed us to improve the prediction on glycerol and to eliminate the systematic deviation in the case of OH bond breaking. Even if in this study we focused on glycerol dehydrogenation, similar methods may be applied to other polyols with other chemical reactions, and considerably speed up the computational design of solid catalysts. This work paves the way for the development of novel numerical techniques to address the issue of biomass conversion.
| Identifer | oai:union.ndltd.org:CCSD/oai:tel.archives-ouvertes.fr:tel-00992665 |
| Date | 30 April 2014 |
| Creators | Zaffran, Jérémie |
| Publisher | Ecole normale supérieure de lyon - ENS LYON |
| Source Sets | CCSD theses-EN-ligne, France |
| Language | English |
| Detected Language | English |
| Type | PhD thesis |
Page generated in 0.0011 seconds