Structural Sensitivity of the Water Gas Shift Reaction on Platinum Surfaces

We investigate the structure sensitivity of the water-gas shift reaction on Pt, showing that the stepped surfaces Pt(322) and Pt(211) exhibit much higher activities than Pt(111). Statistical analysis of reaction occurrence elucidates the contribution of steps and terraces on the observed rates, indentifying the active sites for each elementary reaction

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. © 2011 American Institute of Physics

Equivalence of on-lattice stochastic chemical kinetics with the well-mixed chemical master equation in the limit of fast diffusion

Well-mixed and lattice-based descriptions of stochastic chemical kinetics have been extensively used in the literature. Realizations of the corresponding stochastic processes are obtained by the Gillespie stochastic simulation algorithm and lattice kinetic Monte Carlo algorithms, respectively. However, the two frameworks have remained disconnected. We show the equivalence of these frameworks whereby the stochastic lattice kinetics reduces to effective well-mixed kinetics in the limit of fast diffusion. In the latter, the lattice structure appears implicitly, as the lumped rate of bimolecular reactions depends on the number of neighbors of a site on the lattice. Moreover, we propose a mapping between the stochastic propensities and the deterministic rates of the well-mixed vessel and lattice dynamics that illustrates the hierarchy of models and the key parameters that enable model reduction. © 2011 Elsevier Ltd

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

A review of multiscale modeling of metal-catalyzed reactions: Mechanism development for complexity and emergent behavior

We review and provide a perspective on multiscale modeling of catalytic reactions with emphasis on mechanism development and application to complex and emergent systems. We start with an overview of length and time scales, objectives, and challenges in first-principles modeling of reactive systems. Subsequently, we review various methods that ensure thermodynamic consistency of mean-field microkinetic models. Next, we describe estimation of reaction rate constants via quantum mechanical and statistical-mechanical methods as well as semi-empirical methods. Among the latter, we discuss the bond-order conservation method for thermochemistry and activation energy estimation. In addition, we review the newly developed group-additivity method on adsorbate/metal systems and linear free energy or Brønsted-Evans-Polanyi (BEP) relations, and their parameterization using DFT calculations to generate databases of activation energies and reaction free energies. Linear scaling relations, which can enable transfer of reaction energetics among metals, are discussed. Computation-driven catalyst design is reviewed and a new platform for discovery of materials with emergent behavior is introduced. The effect of parameter uncertainty on catalyst design is discussed; it is shown that adsorbate-adsorbate interactions can profoundly impact materials design. Spatiotemporal averaging of microscopic events via the kinetic Monte Carlo method for realistic reaction mechanisms is discussed as an alternative to mean-field modeling. A hierarchical multiscale modeling strategy is proposed as a means of addressing (some of) the complexity of catalytic reactions. Structure-based microkinetic modeling is next reviewed to account for nanoparticle size and shape effects and structure sensitivity of catalytic reactions. It is hypothesized that catalysts with multiple sites of comparable activity can exhibit structure sensitivity that depends strongly on operating conditions. It is shown that two descriptor models are necessary to describe the thermochemistry of adsorbates on nanoparticles. Multiscale and accelerated methods for computing free energies in solution, while accounting explicitly for solvent effects in catalytic reactions, are briefly touched upon with the acid catalyzed dehydration of fructose in water as an example. The above methods are illustrated with several reactions, such as the CO oxidation on Au; the hydrogenation of ethylene and hydrogenolysis of ethane on Pt; the glycerol decomposition to syngas on Pt-based materials; the NH decomposition on single metals and bimetallics; and the dehydration of fructose in water. Finally, we provide a summary and outlook. © 2011 Elsevier Ltd

Understanding mixing of Ni and Pt in the Ni/Pt(111) bimetallic catalyst via molecular simulation and experiments

Molecular dynamics (MD) simulations employing embedded atom method potentials and ultrahigh vacuum (UHV) experiments were carried out to study the mixing process between the Ni and Pt atoms in the Ni/Pt(111) bimetallic system. The barrier for a Ni atom to diffuse from the top surface to the subsurface layer is rather high (around 1.7 eV) as calculated using the nudged elastic band (NEB) method. Analysis of the relaxation dynamics of the Ni atoms showed that they undergo diffusive motion through a mechanism of correlated hops. At 600 K, all Ni atoms remain trapped on the top surface due to large diffusion barriers. At 900 K, the majority of Ni atoms diffuse to the second layer and at 1200 K diffusion to the bulk is observed. We also find that smaller Ni coverages and the presence of Pt steps facilitate the Ni/Pt mixing. By simulated annealing simulations, we found that in the mixed state, the Ni fraction oscillates between layers, with the second layer being Ni-richer at equilibrium. The simulation results at multiple time scales are consistent with the experimental data. © 2010 American Institute of Physics

Predicting the adsorption behavior in bulk from metal clusters

The physicochemical properties of materials are directly related to their size. The ability to understand and eventually tailor the materials' properties over multiple length scales has always been of a primary research goal. Using quantum mechanical calculations and mathematical modeling, we establish a novel theoretical framework capable of predicting the catalytic behavior of bulk metals and alloys and specifically the adsorbate binding energy, using electronic structure information from sub-nanometer cluster models as input. These models demonstrate that bulk-phase concepts can be reproduced from clusters; a first step towards bridging the properties of materials at different length scales. © 2011 Elsevier B.V. All rights reserved

Optimal design of multi-channel microreactor for uniform residence time distribution

Multi-channel microreactors can be used for various applications that require chemical or electrochemical reactions in either liquid, gaseous or multi phase. For an optimal control of the chemical reactions, one key parameter for the design of such microreactors is the residence time distribution of the fluid, which should be as uniform as possible in the series of microchannels that make up the core of the reactor. Based on simplifying assumptions, an analytical model is proposed for optimizing the design of the collecting and distributing channels which supply the series of rectangular microchannels of the reactor, in the case of liquid flows. The accuracy of this analytical approach is discussed after comparison with CFD simulations and hybrid analytical-CFD calculations that allow an improved refinement of the meshing in the most complex zones of the flow. The analytical model is then extended to the case of microchannels with other cross-sections (trapezoidal or circular segment) and to gaseous flows, in the continuum and slip flow regimes. In the latter case, the model is based on second-order slip flow boundary conditions, and takes into account the compressibility as well as the rarefaction of the gas flow

Evaluation of Microbubbles as Contrast Agents for Ultrasonography and Magnetic Resonance Imaging

Background: Microbubbles (MBs) can serve as an ultrasound contrast agent, and has the potential for magnetic resonance imaging (MRI). Due to the relatively low effect of MBs on MRI, it is necessary to develop new MBs that are more suitable for MRI. In this study, we evaluate the properties of SonoVueH and custom-made Fe 3O 4-nanoparticle-embedded microbubbles (Fe3O4-MBs) in terms of contrast agents for ultrsonography (US) and MRI. Methodology/Principal Findings: A total of 20 HepG2 subcutaneous-tumor-bearing nude mice were randomly assigned to 2 groups (i.e., n = 10 mice each group), one for US test and the other for MRI test. Within each group, two tests were performed for each mouse. The contrast agent for the first test is SonoVueH, and the second is Fe 3O 4-MBs. US was performed using a Technos MPX US system (Esaote, Italy) with a contrast-tuned imaging (CnTI TM) mode. MRI was performed using a 7.0T Micro-MRI (PharmaScan, Bruker Biospin GmbH, Germany) with an EPI-T2 * sequence. The data of signal-to-noise ratio (SNR) from the region-of-interest of each US and MR image was calculated by ImageJ (National Institute of Health, USA). In group 1, enhancement of SonoVueH was significantly higher than Fe 3O 4-MBs on US (P,0.001). In group 2, negative enhancement of Fe3O4-MBs was significantly higher than SonoVueH on MRI (P,0.001). The time to peak showed no significant differences between US and MRI, both of which used the same MBs (P.0.05). The SNR analysis of the enhancement process reveals a strong negative correlation in both cases (i.e., SonoVueH r=20.733, Fe 3O 4-MBs r = 20.903

Relational grounding facilitates development of scientifically useful multiscale models

We review grounding issues that influence the scientific usefulness of any biomedical multiscale model (MSM). Groundings are the collection of units, dimensions, and/or objects to which a variable or model constituent refers. To date, models that primarily use continuous mathematics rely heavily on absolute grounding, whereas those that primarily use discrete software paradigms (e.g., object-oriented, agent-based, actor) typically employ relational grounding. We review grounding issues and identify strategies to address them. We maintain that grounding issues should be addressed at the start of any MSM project and should be reevaluated throughout the model development process. We make the following points. Grounding decisions influence model flexibility, adaptability, and thus reusability. Grounding choices should be influenced by measures, uncertainty, system information, and the nature of available validation data. Absolute grounding complicates the process of combining models to form larger models unless all are grounded absolutely. Relational grounding facilitates referent knowledge embodiment within computational mechanisms but requires separate model-to-referent mappings. Absolute grounding can simplify integration by forcing common units and, hence, a common integration target, but context change may require model reengineering. Relational grounding enables synthesis of large, composite (multi-module) models that can be robust to context changes. Because biological components have varying degrees of autonomy, corresponding components in MSMs need to do the same. Relational grounding facilitates achieving such autonomy. Biomimetic analogues designed to facilitate translational research and development must have long lifecycles. Exploring mechanisms of normal-to-disease transition requires model components that are grounded relationally. Multi-paradigm modeling requires both hyperspatial and relational grounding