Michail Stamatakis

MoS2 monolayer catalyst doped with isolated Co atoms for the hydrodeoxygenation reaction

The conversion of oxygen-rich biomass into hydrocarbon fuels requires efficient hydrodeoxygenation catalysts during the upgrading process. However, traditionally prepared CoMoS2 catalysts, although efficient for hydrodesulfurization, are not appropriate due to their poor activity, sulfur loss and rapid deactivation at elevated temperature. Here, we report the synthesis of MoS2 monolayer sheets decorated with isolated Co atoms that bond covalently to sulfur vacancies on the basal planes that, when compared with conventionally prepared samples, exhibit superior activity, selectivity and stability for the hydrodeoxygenation of 4-methylphenol to toluene. This higher activity allows the reaction temperature to be reduced from the typically used 300 °C to 180 °C and thus allows the catalysis to proceed without sulfur loss and deactivation. Experimental analysis and density functional theory calculations reveal a large number of sites at the interface between the Co and Mo atoms on the MoS2 basal surface and we ascribe the higher activity to the presence of sulfur vacancies that are created local to the observed Co-S-Mo interfacial sites.

A graph-theoretical kinetic Monte Carlo framework for on-lattice chemical kinetics

Existing kinetic Monte Carlo (KMC) frameworks for the simulation of adsorption, desorption, diffusion, and reaction on a lattice often assume that each participating species occupies a single site and represent elementary events involving a maximum of two sites. However, these assumptions may be inadequate, especially in the case of complex chemistries, involving multidentate species or complex coverage and neighboring patterns between several lattice sites. We have developed a novel approach that employs graph-theoretical ideas to overcome these challenges and treat easily complex chemistries. As a benchmark, the Ziff-Gulari-Barshad system is simulated and comparisons of the computational times of the graph-theoretical KMC and a simpler KMC approach are made. Further, to demonstrate the capabilities of our framework, the water-gas shift chemistry on Pt(111) is simulated.

Controlling Hydrogen Activation, Spillover, and Desorption with Pd–Au Single-Atom Alloys

Key descriptors in hydrogenation catalysis are the nature of the active sites for H2 activation and the adsorption strength of H atoms to the surface. Using atomically resolved model systems of dilute Pd-Au surface alloys and density functional theory calculations, we determine key aspects of H2 activation, diffusion, and desorption. Pd monomers in a Au(111) surface catalyze the dissociative adsorption of H2 at temperatures as low as 85 K, a process previously expected to require contiguous Pd sites. H atoms preside at the Pd sites and desorb at temperatures significantly lower than those from pure Pd (175 versus 310 K). This facile H2 activation and weak adsorption of H atom intermediates are key requirements for active and selective hydrogenations. We also demonstrate weak adsorption of CO, a common catalyst poison, which is sufficient to force H atoms to spill over from Pd to Au sites, as evidenced by low-temperature H2 desorption.

Kinetic modelling of heterogeneous catalytic systems.

The importance of heterogeneous catalysis in modern life is evidenced by the fact that numerous products and technologies routinely used nowadays involve catalysts in their synthesis or function. The discovery of catalytic materials is, however, a non-trivial procedure, requiring tedious trial-and-error experimentation. First-principles-based kinetic modelling methods have recently emerged as a promising way to understand catalytic function and aid in materials discovery. In particular, kinetic Monte Carlo (KMC) simulation is increasingly becoming more popular, as it can integrate several sources of complexity encountered in catalytic systems, and has already been used to successfully unravel the underlying physics of several systems of interest. After a short discussion of the different scales involved in catalysis, we summarize the theory behind KMC simulation, and present the latest KMC computational implementations in the field. Early achievements that transformed the way we think about catalysts are subsequently reviewed in connection to latest studies of realistic systems, in an attempt to highlight how the field has evolved over the last few decades. Present challenges and future directions and opportunities in computational catalysis are finally discussed.

Comparison of Deterministic and Stochastic Models of the lac Operon Genetic Network

The lac operon has been a paradigm for genetic regulation with positive feedback, and several modeling studies have described its dynamics at various levels of detail. However, it has not yet been analyzed how stochasticity can enrich the systems behavior, creating effects that are not observed in the deterministic case. To address this problem we use a comparative approach. We develop a reaction network for the dynamics of the lac operon genetic switch and derive corresponding deterministic and stochastic models that incorporate biological details. We then analyze the effects of key biomolecular mechanisms, such as promoter strength and binding affinities, on the behavior of the models. No assumptions or approximations are made when building the models other than those utilized in the reaction network. Thus, we are able to carry out a meaningful comparison between the predictions of the two models to demonstrate genuine effects of stochasticity. Such a comparison reveals that in the presence of stochasticity, certain biomolecular mechanisms can profoundly influence the region where the system exhibits bistability, a key characteristic of the lac operon dynamics. For these cases, the temporal asymptotic behavior of the deterministic model remains unchanged, indicating a role of stochasticity in modulating the behavior of the system.

Parallel kinetic Monte Carlo simulation framework incorporating accurate models of adsorbate lateral interactions

Ab initio kinetic Monte Carlo (KMC) simulations have been successfully applied for over two decades to elucidate the underlying physico-chemical phenomena on the surfaces of heterogeneous catalysts. These simulations necessitate detailed knowledge of the kinetics of elementary reactions constituting the reaction mechanism, and the energetics of the species participating in the chemistry. The information about the energetics is encoded in the formation energies of gas and surface-bound species, and the lateral interactions between adsorbates on the catalytic surface, which can be modeled at different levels of detail. The majority of previous works accounted for only pairwise-additive first nearest-neighbor interactions. More recently, cluster-expansion Hamiltonians incorporating long-range interactions and many-body terms have been used for detailed estimations of catalytic rate [C. Wu, D. J. Schmidt, C. Wolverton, and W. F. Schneider, J. Catal. 286, 88 (2012)]. In view of the increasing interest in accurate predictions of catalytic performance, there is a need for general-purpose KMC approaches incorporating detailed cluster expansion models for the adlayer energetics. We have addressed this need by building on the previously introduced graph-theoretical KMC framework, and we have developed Zacros, a FORTRAN2003 KMC package for simulating catalytic chemistries. To tackle the high computational cost in the presence of long-range interactions we introduce parallelization with OpenMP. We further benchmark our framework by simulating a KMC analogue of the NO oxidation system established by Schneider and co-workers [J. Catal. 286, 88 (2012)]. We show that taking into account only first nearest-neighbor interactions may lead to large errors in the prediction of the catalytic rate, whereas for accurate estimates thereof, one needs to include long-range terms in the cluster expansion.

From Biomass‐Derived Furans to Aromatics with Ethanol over Zeolite

We report a novel catalytic conversion of biomass-derived furans and alcohols to aromatics over zeolite catalysts. Aromatics are formed via Diels-Alder cycloaddition with ethylene, which is produced in situ from ethanol dehydration. The use of liquid ethanol instead of gaseous ethylene, as the source of dienophile in this one-pot synthesis, makes the aromatics production much simpler and renewable, circumventing the use of ethylene at high pressure. More importantly, both our experiments and theoretical studies demonstrate that the use of ethanol instead of ethylene, results in significantly higher rates and higher selectivity to aromatics, due to lower activation barriers over the solid acid sites. Synchrotron-diffraction experiments and proton-affinity calculations clearly suggest that a preferred protonation of ethanol over the furan is a key step facilitating the Diels-Alder and dehydration reactions in the acid sites of the zeolite.

A mathematical and computational approach for integrating the major sources of cell population heterogeneity.

Several approaches have been used in the past to model heterogeneity in bacterial cell populations, with each approach focusing on different source(s) of heterogeneity. However, a holistic approach that integrates all the major sources into a comprehensive framework applicable to cell populations is still lacking. In this work we present the mathematical formulation of a cell population master equation (CPME) that describes cell population dynamics and takes into account the major sources of heterogeneity, namely stochasticity in reaction, DNA-duplication, and division, as well as the random partitioning of species contents into the two daughter cells. The formulation also takes into account cell growth and respects the discrete nature of the molecular contents and cell numbers. We further develop a Monte Carlo algorithm for the simulation of the stochastic processes considered here. To benchmark our new framework, we first use it to quantify the effect of each source of heterogeneity on the intrinsic and the extrinsic phenotypic variability for the well-known two-promoter system used experimentally by Elowitz et al. (2002). We finally apply our framework to a more complicated system and demonstrate how the interplay between noisy gene expression and growth inhibition due to protein accumulation at the single cell level can result in complex behavior at the cell population level. The generality of our framework makes it suitable for studying a vast array of artificial and natural genetic networks. Using our Monte Carlo algorithm, cell population distributions can be predicted for the genetic architecture of interest, thereby quantifying the effect of stochasticity in intracellular reactions or the variability in the rate of physiological processes such as growth and division. Such in silico experiments can give insight into the behavior of cell populations and reveal the major sources contributing to cell population heterogeneity.

Pt/Cu single-atom alloys as coke-resistant catalysts for efficient C–H activation

The recent availability of shale gas has led to a renewed interest in C-H bond activation as the first step towards the synthesis of fuels and fine chemicals. Heterogeneous catalysts based on Ni and Pt can perform this chemistry, but deactivate easily due to coke formation. Cu-based catalysts are not practical due to high C-H activation barriers, but their weaker binding to adsorbates offers resilience to coking. Using Pt/Cu single-atom alloys (SAAs), we examine C-H activation in a number of systems including methyl groups, methane and butane using a combination of simulations, surface science and catalysis studies. We find that Pt/Cu SAAs activate C-H bonds more efficiently than Cu, are stable for days under realistic operating conditions, and avoid the problem of coking typically encountered with Pt. Pt/Cu SAAs therefore offer a new approach to coke-resistant C-H activation chemistry, with the added economic benefit that the precious metal is diluted at the atomic limit.

Catalysis at the sub-nanoscale: complex CO oxidation chemistry on a few Au atoms

Au has been widely used as jewelry since ancient times due to its bulk, chemically inert properties. During the last three decades, nanoscale Au has attracted remarkable attention and has been shown to be an exceptional catalyst, especially for oxidation reactions. Herein, we elucidate a puzzle in catalysis by using multiscale computational modeling: the experimentally observed “magic number” CO oxidation catalytic behavior of sub-nanoscale Au clusters. Our results demonstrate that support effects (cluster charging), symmetry-induced electronic effects on the clusters, catalyst reconstruction, competing chemical pathways and formation of carbonate contribute to the marked differences in the observed catalytic behavior of Aun− clusters with n = 6, 8 and 10 atoms. This is the first demonstration of multiscale simulations on sub-nanoscale catalysts unraveling the magic number activity for the CO oxidation reaction on Au.