Reports: DNI555009-DNI5: Identification of Electrocatalysts and Reaction Mechanisms for Electrochemical Conversion of Methane to Methanol Using Density Functional Theory

Venkatasubramanian Viswanathan, PhD, Carnegie Mellon University

The work on understanding the target requirements for flare-gas recovery published in ACS Sustainable Chem. Eng., 2016, 4 (3), pp 1736–1745, received substantial interest. We initiated discussions with WATT Fuel Cell, a Pittsburgh based company to find ways to identify materials that could lead to more efficient methane to methanol conversion. We began exploring the potential for electrocatalysts that could activate methane. In particular, our focus has been on oxide based materials that could lead to activation of methane. In order to describe these materials with great accuracy, we have been exploring the important question of assessing the applicability of density functional theory based methods for material identification. DFT-calculations are known to have finite uncertainty and in recent years, the development of Bayesian Error Estimation (BEE) methods have allowed systematic ways to exploring uncertainty associated with DFT calculations. We began by assessing the effects of the unit cell volume which is crucial in the context of oxide materials, as strain effects could lead to vastly different bonding properties. We developed a systematic method published in Phys. Rev. B 94, 064105, that outlines an approach to identify uncertainty in unit cell volume and the associated uncertainty in mechanical properties and the strain energy. The method relies on using energy-strain relationship and at each strain, it uses an ensemble of energies to get an ensemble of unit cell volume and the associated mechanical properties. The method is computationally efficient as it provides an uncertainty estimate at the same cost of a self-consistent DFT calculation. We prove the validity of this method by showing that the values bound popular GGA level functionals such as PBE, PBEsol and RPBE.

Using this information, we began exploring the class of rutile oxides as possible candidate catalysts. In order to perform a benchmark, we have carried out a validation study on understanding electrocatalytic oxygen evolution which is undoubtedly a competing process during methane activation. Using BEEF-vdW exchange correlation functional, we calculated the reaction free energies for oxygen evolution reaction on CrO2, MnO2, RuO2, IrO2, TiO2 and SnO2. We also have developed a systematic approach to propogating the finite uncertainty associated with the density functional theory calculations on to the predictions of activity. Using all of this, we intend to explore further methane activation of these rutile oxide surfaces. In the next year, we will explore the reactivity of these oxide surfaces for methane activation and possible selective oxidation to methanol.