Formic acid (HCOOH) is a simple molecule that is an abundant product of biomass processing and can serve as an internal source of hydrogen for oxygen removal and upgrading of biomass to chemicals and fuels. In addition, HCOOH can be used as a fuel for low temperature direct fuel cells. We present a systematic study of the HCOOH decomposition reaction mechanism starting from first-principles and including reactivity experiments and microkinetic modeling. In particular, periodic self-consistent Density Functional Theory (DFT) calculations are performed to determine the stability of reactive intermediates and activation energy barriers of elementary steps. Pre-exponential factors are determined from vibrational frequency calculations. Mean-field microkinetic models are developed and calculated reaction rates and reaction orders are then compared with experimentally measured ones. These comparisons provide useful insights on the nature of the active site, most-abundant surface intermediates as a function of reaction conditions and feed composition. Trends across metals on the fundamental atomic-scale level up to selectivity trends will be discussed. Finally, we identify from first-principles alloy surfaces, which may possess better catalytic properties for selective dehydrogenation of HCOOH than monometallic surfaces, thereby guiding synthesis towards promising novel catalytic materials.

We are using modeling to understand the properties of complex materials that show promise for catalysis, filtration and decontamination. These materials are of interests both for chemical defense, as well as in many industrial applications. The main focus of our modeling so far has been determining the structure and properties of mixtures of TiO2 nanoparticles(NPs) and small sheets of graphene. The synthesis technique for these materials was pioneered at ECBC. It involves synthesizing graphene from alizarin molecules in close contact with TiO2 NPs. This was expected to create a close connection between the graphene and TiO2 NPs, and improve their catalytic properties. However, the exact structure of the small graphene patches and how and where they bind to TiO2 is difficult to characterize experimentally. Our models have shown that the binding between graphene TiO2 is quite weak (on the order of 0.01eV/Carbon atom), and that it is not strongly dependant on the addition of O and OH to the surface of the graphene, or to the size of the graphene patches. This appears to confirm the experimental finding that the surface graphene can be removed from the TiO2 particles simply by rubbing. We also modeled the case where graphene is surrounded by TiO2 on both sides, and the binding in this case increases by nearly 2x, suggesting that graphene would prefer to bind between TIO2 NPs. This may be the explanation for the disappearance of the smallest (~5nm) sized pores from the TiO2 NP agglomerates upon addition of graphene. We also find that there is a non-trivial degree of charge transfer between graphene and TiO2, which can be expected to improve TiO2’s photo catalytic properties.

Furthermore, recent experiments³ studying 5A on amorphous mica gave evidence of nucleation via a hot precursor state, with an unusual relationship between N and substrate temperature. Thus motivated, we examine a model of such behavior.⁴ We use rate equations and Walton's relation. We take deposited monomers to be hot initially, traveling ballistically with temperature-independent speed v until a time τ, when they thermalize. For the dimensionless combination z := v τ N^-1/2 ≪ 1 rapid thermalization occurs, with consequent DLA nucleation. For z ≫ 1 we find the novel behavior for hot-monomer aggregation (HMA): χ has, unexpectedly, the same form as for ALA. We scrutinize behavior in both limits as well as in the crossover regime z ~ 1, in which behavior can be described using an effective χ. At low temperatures, the behavior becomes markedly non-Arrhenius, insensitive to temperature. We conclude a discussion of more general applications of this framework.

¹T.L. Einstein, A. Pimpinelli, Diego Luis González, J. Crystal Growth (2014), http://dx.doi.org/10.1016/j.jcrysgro.2014.01.053.

²A. Pimpinelli, L. Tumbek, A. Winkler, J. Phys. Chem. Lett. 5 (2014) 995.

³A. Winkler, L. Tumbek, J. Phys. Chem. Lett. 4 (2013) 4080.

⁴A. Pimpinelli, J.R. Morales-Cifuentes, T.L. Einstein, preprint.

Selective epitaxial growth (SEG) of Ge on Si substrates has proven to be a versatile pathway for producing Ge substrates to enable III-V device integration on Si. However, persistent problems remain, including dislocation formation and high stresses due to lattice parameter and thermal expansion coefficient mismatches between Si and Ge. Further optimization of the SEG process may be significantly assisted by atomistic simulation. Here, we present an atomistic analysis of Ge deposition on SiO₂. We begin by describing a validation process for a Tersoff-based model for the ternary Si-Ge-O system [1,2], in which we compare simulation predictions to detailed experimental data [3,4] for a variety of properties. Using this validated interatomic potential, Ge deposition and islanding on an amorphous SiO₂ surface is studied with direct molecular dynamics and the results are compared to experimental measurements [4] of island size distributions as a function of deposition rate and temperature. A key aspect of our modeling approach is a procedure to accelerate the simulations. While direct molecular dynamics simulations of Ge deposition on SiO₂ are able to capture Ge island nucleation, growth and coarsening, the very fast deposition rates necessary makes difficult direct comparison to experimental measurements of island density and size distributions. In particular, we show that direct molecular dynamics simulations are able to approach, but not quite reach, the deposition conditions in experiment. The accelerated simulations are based on “equation-free” coarse projective integration [5]. Here, measures of the island size distribution dynamics are obtained from short molecular dynamics simulations and then used to evolve numerically the size distribution over large time intervals. The new island size distribution is then used to reconstruct consistent atomic configurations that are subsequently evolved further with molecular dynamics and the process is repeated. Here, we show that the reconstruction of atomic configurations from size distribution moments represents the key challenge in deposition simulations and we propose approaches for achieving this in a computationally tractable manner.

[1] J. Tersoff, Phys. Rev. B39, 5566 (1989).

[2] S. Munetoh, T. Motooka, K. Moriguchi and A. Shintani, Comput. Mater. Sci39, 334 (2007).

[3] Q. Li, J. L. Krauss, S. Hersee, and S. M. Han, J. Phys. Chem. C 111, 779 (2007).

[4] D. Leonhardt and S. M. Han, Surf. Sci. 603, 2624 (2009).

[5] M.E. Kavousanakis, R. Erban, A.G. Boudouvis, C.W. Gear, I.G. Kevrekidis, . (2007) 382-407.