(656a) Developing Multi-Scale Models of Bimetallic Catalysts for the Hydrodeoxygenation of Bio-Oil Compounds

Authors: 
McEwen, J. S., Washington State University
Wong, B., Washington State University
Wang, Y., Pacific Northwest National Laboratory
Hensley, A., Washington State University
Collinge, G., Washington State University

            With
the ever-increasing need to find a sustainable source of renewable energy,
bio-oil produced via the fast pyrolysis of biomass is a promising source of
liquid fuel; however, the resulting bio-oil contains oxygenated products that contribute
to poor fuel quality [1].
Hydrodeoxygenation (HDO) is used to refine the bio-oil by reducing the oxygen
content, ideally using a minimal amount of H2 in order to form H2O.
Bimetallic catalysts such as Pd on Fe have demonstrated synergistic behavior
that contributes to a more cost-effective and longer-lasting catalyst [2-4].  Similarly, Pt/Sn catalysts have also
exhibited traits favorable for the HDO of phenolic compounds [5], namely a propensity to promote lower
temperature desorption of aromatic compounds like benzene [6]. Identifying the cause of the different
synergetic effects of these systems can provide insight into the synthesis of a
superior HDO bimetallic catalyst. However, the behavior of bimetallic catalysts
is complex and necessitates a thorough understanding of the nanoscale behavior
of these systems in order to develop a truly predictive model on relevant time
scales. In particular, we present a DFT study that examines the importance of
including coverage effects into theoretical models in order to accurately
predict experimental behavior.

            Quantifying
the lateral interactions between adspecies is necessary in the development of
truly predictive, theoretical models for bimetallic catalysts, as they will
significantly affect the coverage and diffusion of adspecies and therefore the
heterogeneous catalytic environment. However, such a characterization of the
lateral interactions between adspecies for the highly complex HDO reaction has
not yet been attempted. To that end, we have characterized the benzene-benzene
lateral interactions on a Pt (111) surface and a Pt3Sn (111) surface
alloy. This coverage-dependent adsorption behavior was incorporated into a kinetic
model for each surface and used to simulate the temperature-programmed desorption
(TPD) spectra. We benchmarked our simulated spectra by comparing it to
experimental TPD [6]. We found that it is
imperative to incorporate the effect of surface coverage in these theoretical
models if the desorption behavior is to be accurately predicted (see Figure 1).
Furthermore, we found that a mean-field model is sufficient in describing the benzene-benzene
lateral interactions. Using our models, we were able to interpret the desorption
behavior at an atomistic level and provide a deeper level of insight into these
systems. While it was previously speculated that the broad desorption peak for
benzene on Pt (111) was the result of desorption from two adsorption sites of
different binding strengths [6], our
results indicate that the behavior is due to benzene desorbing from a single
site but experiencing strong lateral interactions on the surface. We also found
that the adsorption of benzene was significantly weaker on the Pt3Sn
(111) than on Pt (111), and as such, we were able to assign the monolayer desorption
of benzene to a TPD peak previously attributed entirely to multilayer benzene
desorption [6]. In this regard, we were
also able to attribute the higher temperature desorption peaks observed on Pt3Sn
(111) to benzene desorbing from defects in the alloy surface, where Pt atoms
were not substituted for Sn atoms in the alloying process.

Our
investigation into modeling the lateral interactions of an aromatic molecule on
both the Pt (111) and the Pt3Sn (111) surfaces warrants the
application of our approach onto systems less experimentally characterized,
such as Fe surfaces doped with different noble metals.

Figure 1. TPD spectra for benzene on Pt (111)
simulated with coverage effects (blue) and without coverage effects (light
green) compared to experiment (black) [6].

References

[1] H. Wang, J.
Male, Y. Wang, ACS Catal. 3 (2013) 1047-1070.

[2] Y. Hong, H.
Zhang, J. Sun, K.M. Ayman, A.J.R. Hensley, M. Gu, M.H. Engelhard, J.-S. McEwen,
Y. Wang, ACS Catal. 4 (2014) 3335-3345.

[3] J. Sun, A.M.
Karim, H. Zhang, L. Kovarik, X.S. Li, A.J. Hensley, J.-S. McEwen, Y. Wang, J.
Catal. 306 (2013) 47-57.

[4] A.J.R. Hensley,
Y. Hong, R. Zhang, H. Zhang, J. Sun, Y. Wang, J.-S. McEwen, ACS Catal. 4 (2014)
3381-3392.

[5] M.A.n.
González-Borja, D.E. Resasco, Energy Fuels 25 (2011) 4155-4162.

[6] C. Xu, Y.L.
Tsai, B.E. Koel, J. Phys. Chem. 98 (1994) 585-593.