Dionisios G. Vlachos

Insights into the Interplay of Lewis and Brønsted Acid Catalysts in Glucose and Fructose Conversion to 5-(Hydroxymethyl)furfural and Levulinic Acid in Aqueous Media

5-(Hydroxymethyl)furfural (HMF) and levulinic acid production from glucose in a cascade of reactions using a Lewis acid (CrCl3) catalyst together with a Brønsted acid (HCl) catalyst in aqueous media is investigated. It is shown that CrCl3 is an active Lewis acid catalyst in glucose isomerization to fructose, and the combined Lewis and Brønsted acid catalysts perform the isomerization and dehydration/rehydration reactions. A CrCl3 speciation model in conjunction with kinetics results indicates that the hydrolyzed Cr(III) complex [Cr(H2O)5OH](2+) is the most active Cr species in glucose isomerization and probably acts as a Lewis acid-Brønsted base bifunctional site. Extended X-ray absorption fine structure spectroscopy and Car-Parrinello molecular dynamics simulations indicate a strong interaction between the Cr cation and the glucose molecule whereby some water molecules are displaced from the first coordination sphere of Cr by the glucose to enable ring-opening and isomerization of glucose. Additionally, complex interactions between the two catalysts are revealed: Brønsted acidity retards aldose-to-ketose isomerization by decreasing the equilibrium concentration of [Cr(H2O)5OH](2+). In contrast, Lewis acidity increases the overall rate of consumption of fructose and HMF compared to Brønsted acid catalysis by promoting side reactions. Even in the absence of HCl, hydrolysis of Cr(III) decreases the solution pH, and this intrinsic Brønsted acidity drives the dehydration and rehydration reactions. Yields of 46% levulinic acid in a single phase and 59% HMF in a biphasic system have been achieved at moderate temperatures by combining CrCl3 and HCl.

Top ten fundamental challenges of biomass pyrolysis for biofuels

Pyrolytic biofuels have technical advantages over conventional biological conversion processes since the entire plant can be used as the feedstock (rather than only simple sugars) and the conversion process occurs in only a few seconds (rather than hours or days). Despite decades of study, the fundamental science of biomass pyrolysis is still lacking and detailed models capable of describing the chemistry and transport in real-world reactors is unavailable. Developing these descriptions is a challenge because of the complexity of feedstocks and the multiphase nature of the conversion process. Here, we identify ten fundamental research challenges that, if overcome, would facilitate commercialization of pyrolytic biofuels. In particular, developing fundamental descriptions for condensed-phase pyrolysis chemistry (i.e., elementary reaction mechanisms) are needed since they would allow for accurate process optimization as well as feedstock flexibility, both of which are critical to any modern high-throughput process. Despite the benefits to pyrolysis commercialization, detailed chemical mechanisms are not available today, even for major products such as levoglucosan and hydroxymethylfurfural (HMF). Additionally, accurate estimates for heat and mass transfer parameters (e.g., thermal conductivity, diffusivity) are lacking despite the fact that biomass conversion in commercial pyrolysis reactors is controlled by transport. Finally, we examine methods for improving pyrolysis particle models, which connect fundamental chemical and transport descriptions to real-world pyrolysis reactors. Each of the ten challenges is presented with a brief review of relevant literature followed by future directions which can ultimately lead to technological breakthroughs that would facilitate commercialization of pyrolytic biofuels.

Using first principles to predict bimetallic catalysts for the ammonia decomposition reaction

The facile decomposition of ammonia to produce hydrogen is critical to its use as a hydrogen storage medium in a hydrogen economy, and although ruthenium shows good activity for catalysing this process, its expense and scarcity are prohibitive to large-scale commercialization. The need to develop alternative catalysts has been addressed here, using microkinetic modelling combined with density functional studies to identify suitable monolayer bimetallic (surface or subsurface) catalysts based on nitrogen binding energies. The Ni–Pt–Pt(111) surface, with one monolayer of Ni atoms residing on a Pt(111) substrate, was predicted to be a catalytically active surface. This was verified using temperature-programmed desorption and high-resolution electron energy loss spectroscopy experiments. The results reported here provide a framework for complex catalyst discovery. They also demonstrate the critical importance of combining theoretical and experimental approaches for identifying desirable monolayer bimetallic systems when the surface properties are not a linear function of the parent metals. The decomposition of ammonia is an important process if ammonia is to be used as a hydrogen storage medium. The most active catalyst for this is ruthenium, but its expense has provoked the search for alternatives. Now, using theory to guide the investigation, researchers have identified a bimetallic nickel–platinum surface as an active catalyst for this process.

Binomial distribution based τ-leap accelerated stochastic simulation

Recently, Gillespie introduced the tau-leap approximate, accelerated stochastic Monte Carlo method for well-mixed reacting systems [J. Chem. Phys. 115, 1716 (2001)]. In each time increment of that method, one executes a number of reaction events, selected randomly from a Poisson distribution, to enable simulation of long times. Here we introduce a binomial distribution tau-leap algorithm (abbreviated as BD-tau method). This method combines the bounded nature of the binomial distribution variable with the limiting reactant and constrained firing concepts to avoid negative populations encountered in the original tau-leap method of Gillespie for large time increments, and thus conserve mass. Simulations using prototype reaction networks show that the BD-tau method is more accurate than the original method for comparable coarse-graining in time.

Correlating Particle Size and Shape of Supported Ru/γ-Al2O3 Catalysts with NH3 Decomposition Activity

While ammonia synthesis and decomposition on Ru are known to be structure-sensitive reactions, the effect of particle shape on controlling the particle size giving maximum turnover frequency (TOF) is not understood. By controlling the catalyst pretreatment conditions, we have varied the particle size and shape of supported Ru/gamma-Al(2)O(3) catalysts. The Ru particle shape was reconstructed by combining microscopy, chemisorption, and extended X-ray absorption fine structure (EXAFS) techniques. We show that the particle shape can change from a round one, for smaller particles, to an elongated, flat one, for larger particles, with suitable pretreatment. Density functional theory calculations suggest that the calcination most likely leads to planar structures. We show for the first time that the number of active (here B(5)) sites is highly dependent on particle shape and increases with particle size up to 7 nm for flat nanoparticles. The maximum TOF (based on total exposed Ru atoms) and number of active (B(5)) sites occur at approximately 7 nm for elongated nanoparticles compared to at approximately 1.8-3 nm for hemispherical nanoparticles. A complete, first-principles based microkinetic model is constructed that can quantitatively describe for the first time the effect of varying particle size and shape on Ru activity and provide further support of the characterization results. In very small nanoparticles, particle size polydispersity (due to the presence of larger particles) appears to be responsible for the observed activity.

Revealing pyrolysis chemistry for biofuels production: conversion of cellulose to furans and small oxygenates.

Biomass pyrolysis utilizes high temperatures to produce an economically renewable intermediate (pyrolysis oil) that can be integrated with the existing petroleum infrastructure to produce biofuels. The initial chemical reactions in pyrolysis convert solid biopolymers, such as cellulose (up to 60% of biomass), to a short-lived (less than 0.1 s) liquid phase, which subsequently reacts to produce volatile products. In this work, we develop a novel thin-film pyrolysis technique to overcome typical experimental limitations in biopolymer pyrolysis and identify α-cyclodextrin as an appropriate small-molecule surrogate of cellulose. Ab initio molecular dynamics simulations are performed with this surrogate to reveal the long-debated pathways of cellulose pyrolysis and indicate homolytic cleavage of glycosidic linkages and furan formation directly from cellulose without any small-molecule (e.g., glucose) intermediates. Our strategy combines novel experiments and first-principles simulations to allow detailed chemical mechanisms to be constructed for biomass pyrolysis and enable the optimization of next-generation biorefineries.

Coarse-grained stochastic processes and Monte Carlo simulations in lattice systems

In this paper we present a new class of coarse-grained stochastic processes and Monte Carlo simulations, derived directly from microscopic lattice systems and describing mesoscopic length scales. As our primary example, we mainly focus on a microscopic spin-flip model for the adsorption and desorption of molecules between a surface adjacent to a gas phase, although a similar analysis carries over to other processes. The new model can capture large scale structures, while retaining microscopic information on intermolecular forces and particle fluctuations. The requirement of detailed balance is utilized as a systematic design principle to guarantee correct noise fluctuations for the coarse-grained model. We carry out a rigorous asymptotic analysis of the new system using techniques from large deviations and present detailed numerical comparisons of coarse-grained and microscopic Monte Carlo simulations. The coarse-grained stochastic algorithms provide large computational savings without increasing programming complexity or the CPU time per executed event compared to microscopic Monte Carlo simulations.

Recent developments on multiscale, hierarchical modeling of chemical reactors

Abstract A multiscale, hierarchical computational framework is presented for modeling homogeneous–heterogeneous reactors, which exhibit a large disparity in length and time scales. Scales range from quantum, to atomistic, to mesoscopic, to macroscopic. The coupling mechanisms between scales are discussed and illustrated with examples from CO and CH4 oxidation on platinum. Estimation of reaction mechanism parameters, based on first principle quantum calculations and semi-empirical techniques, is briefly reviewed. These kinetic mechanisms are key input into molecular, continuum, or mesoscopic models. Some emphasis is placed on surface diffusion, which typically falls outside the realm of atomistic models, but it can affect reaction rates and pattern formation on catalytic surfaces. An efficient methodology for parameter optimization of multiscale models is also presented. Finally, we show how mesoscopic models constitute a promising alternative to atomistic Monte Carlo (MC) simulations to account for intermolecular forces, which cannot be properly captured through continuum, mean field (MF) models. Application of these mesoscopic theories to microporous catalysts, such as zeolites, is also discussed.

A Review of Multiscale Analysis: Examples from Systems Biology, Materials Engineering, and Other Fluid–Surface Interacting Systems

Abstract Multiscale simulation is an emerging scientific field that spans many disciplines, including physics, chemistry, mathematics, statistics, chemical engineering, mechanical engineering, and materials science. This review paper first defines this new scientific field and outlines its objectives. An overview of deterministic, continuum models and discrete, particle models is then given. Among discrete, particle models, emphasis is placed on Monte Carlo stochastic simulation methods in well-mixed and spatially distributed systems. Next, a classification of multiscale methods is carried out based on separation of length and time scales and the computational and mathematical approach taken. Broadly speaking, hybrid simulation and coarse graining or mesoscopic modeling are identified as two general and complementary approaches of multiscale modeling. The former is further classified into onion- and multigrid-type simulation depending on length scales and the presence or not of gradients. Several approaches, such as the net event, the probability weighted, the Poisson and binomial τ -leap, and the hybrid, are discussed for acceleration of stochastic simulation. In order to demonstrate the unifying principles of multiscale simulation, examples from different areas are discussed, including systems biology, materials growth and other reacting systems, fluids, and statistical mechanics. While the classification is general and examples from other scales and tools are touched upon, in this review emphasis is placed on stochastic models, their coarse graining, and their integration with continuum deterministic models, i.e., on the coupling of mesoscopic and macroscopic scales. The concept of hierarchical multiscale modeling is discussed in some length. Finally, the importance of systems-level tools such as sensitivity analysis, parameter estimation, optimization, control, model reduction, and bifurcation in multiscale analysis is underscored.

Modeling of high-temperature microburners

Flame propagation in microchannels is modeled using two-dimensional parabolic simulations with detailed multicomponent transport, gas-phase chemistry, heat loss through the wall, radical recombination at walls, and possible temperature discontinuity at the wall due to lack of thermal accommodation. We show that under certain conditions of preheating and insulation, methane/air flames are able to propagate in microchannels, providing a possible explanation for recent experimental observation. It is found that in very small reactors, radial gradients and temperature discontinuity at the wall are negligible but become significant as the diameter is increased. On the other hand, the near-entrance heat loss and radical quenching at the wall are key issues in controlling flame propagation in microchannels. Finally, a brief comparison between elliptic and parabolic simulations is presented. Although ignition distances are shorter in elliptic simulations due to heat propagation upstream of the flame front, similar trends are observed.