Ce-bastnäsite is the single largest mineral source for light rare-earth elements. In view of the growing industrial importance of rare-earth minerals, it is critical to develop more efficient methods for separating the valuable rare-earth-containing minerals from the surrounding gangue. In this work, we employ a combination of periodic density functional theory (DFT) and molecular mechanics (MM) calculations together with the de novo molecular design program HostDesigner to identify bis-phosphinate ligands that preferentially bind to the (100) Ce-bastnäsite surface rather than the (104) calcite surface. DFT calculations for a simple phosphinate ligand were employed to qualitatively understand key behaviors involved in ligand-metal, ligand-solvent, and solvent-metal interactions. These insights were then used to guide the search for flexible, rigid, and semirigid hydrocarbon linkers to identify candidate bis-phosphinate ligands with the potential to bind preferentially to Ce-bastnäsite. Among the five most promising bis-phosphinate ligands suggested by theoretical studies, three ligands were synthesized and their adsorption characteristics to bastnäsite (100) interfaces were characterized using vibrational sum-frequency (vSFG) spectroscopy, attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, and isothermal titration calorimetry (ITC). The efficacy of the selective interfacial molecular binding was demonstrated by identifying a bis-phosphinate ligand capable of providing an overall higher surface coverage of alkyl groups relative to a monophosphinate ligand. The results highlight the interplay between adsorption binding strength and maximum surface coverage in determining ligand efficiency to render the mineral surface hydrophobic. DFT calculations further indicate that all tested ligands have higher affinity for Ce-bastnäsite than for calcite. This is consistent with the ITC data showing stronger adsorption enthalpy to bastnäsite than to calcite, making these ligands promising candidates for selective flotation of Ce-bastnäsite.

Computational screening of catalysts with complex microstructures and/or multiple interacting sites is challenging. Here, we evaluate the ammonia decomposition on perfect and patched core-shell bimetallic facets using a hierarchy of kinetic models, namely, scaling relations (SRs)-based mean-field microkinetic models (SR-MKM), and first-principles (FP) and SR-based spatially resolved kinetic Monte Carlo (FP- and SR-KMC) simulations. SR-KMC volcanos possess a double-peak on perfect facets and a single peak on patched bimetallic facets. When lateral interactions of the most abundant surface intermediate are strong, only the KMC method predicts the volcano accurately. Dual site (bifunctional) materials exhibit a considerably different volcano curve from monofunctional materials and thus, multifunctional materials' property prediction requires KMC calculations. Our computational screening highlights the importance of the microstructure of multifunctional catalysts.

Physical mixtures of catalysts can sometimes show synergistic activity which exceeds the sum of the activities of the same amount of catalysts employed separately. However, identifying such synergistic mixtures is non-trivial. Not all mixtures display synergy, and the number of combinations that are possible (even for binary mixtures of equal portions) scales very rapidly, and thus they would be costly to screen experimentally. In this work, we show that it is possible to predict synergistic mixtures using combinatorial kinetic simulations based on experimental data collected on individual catalysts. The data was first collected for conditions-of-interest under low conversions (also called near differential conditions, such that each condition approximates a small volume in a non-differential conditions reactor) to build a library of kinetic models (one model for each catalyst). This data was then used for combinatorial kinetic simulations of non-differential reactor scale conversions, and successfully predicted the qualitative behavior of two synergistic physical mixtures. The capability was utilized in the present work in the context of converting CO, C3H6, and NO species in simulated car exhaust, but is a general approach. We provide equations to estimate the costs of the screening method as well as the combinatorial kinetics method and show that the costs of identifying synergistic physical mixtures become much lower with the combinatorial kinetics method even before ten catalysts of interest. The cost of using prediction by the combinatorial kinetics method continue to become lower (relative to screening) when more catalysts are added - and even more so if mixtures beyond two components, or consideration of multiple catalyst ratios, is of interest.

Lattice based kinetic Monte Carlo (KMC) is often used for simulating the dynamics of systems at a supramolecular scale, based on molecular scale transitions. A common challenge in KMC simulations is rapid 'back-and-forth' reactions, which dominate the events executed during simulations and inhibit the ability for simulations to reach longer time scales. Such processes are fast frivolous processes (FFPs) and are one manifestation of a phenomenon referred to as KMC-stiffness. Here, an algorithm for staggered quasi-equilibrium rank-based throttling geared towards transient kinetics (SQERT-T) is presented. Within the SQERT-T methodology, a pace-restrictor reaction and an FFP floor are utilized along with throttling of the process transition rate constants to accelerate the KMC simulations while still retaining sufficient time resolution for sampling of the data. KMC simulations were performed for CO oxidation over RuO2(1 1 0) and over RuO2(1 1 1), and the results were compared to experimental data obtained using RuO2 powders. The experiments and simulations were for transient conditions: the system was subjected to a temperature program which included temperatures in the range of 363 to 453 K. The timescales that were achieved during the KMC simulations in this study would not have been accessible without KMC acceleration, and were enabled by the use of SQERT-T.

First-principles kinetic Monte Carlo (1p-kMC) simulations for CO oxidation on two RuO2 facets, RuO2(110) and RuO2(111), were coupled to the computational fluid to dynamics (CFD) simulations package MFIX, and reactor-scale simulations were then performed. 1p-kMC coupled with CFD has recently been shown as a feasible method for translating molecular scale mechanistic knowledge to the reactor scale, enabling comparisons to in situ and online experimental measurements. Only a few studies with such coupling have been published. This work incorporates multiple catalytic surface facets into the scale-coupled simulation, and three possibilities were investigated: the two possibilities of each facet individually being the dominant phase in the reactor, and also the possibility that both facets were present on the catalyst particles in the ratio predicted by an ab initio thermodynamics-based Wulff construction. When lateral interactions between adsorbates were included in the 1p-kMC simulations, the two surfaces, RuO2(110) and RuO2(111), were found to be of similar order-of-magnitude in activity for the pressure range of 1 x 10(-4) bar to 1 bar, with the RuO2(110) surface-termination showing more simulated activity than the RuO2(111) surface-termination. Coupling between the 1p-kMC and CFD was achieved with a lookup table generated by the error-based modified Shepard interpolation scheme. Isothermal reactor scale simulations were performed and compared to two separate experimental studies, conducted with reactant partial pressures of <= 0.1 bar. Simulations without an isothermality restriction were also conducted and showed that the simulated temperature gradient across the catalytic reactor bed is <0.5 K, which validated the use of the isothermality restriction for investigating the reactor-scale phenomenological temperature dependences. The approach with the Wulff construction based reactor simulations reproduced a trend similar to one experimental data set relatively well, with the (110) surface being more active at higher temperaures; in contrast, for the other experimental data set, our reactor simulations achieve surprisingly and perhaps fortuitously good agreement with the activity and phenomenological pressure dependence when it is assumed that the (111) facet is the only active facet present. The active phase of catalytic CO oxidation over RuO2 remains unsettled, but the present study presents proof of principle (and progress) toward more accurate multiscale modeling from electrons to reactors and new simulation results.

Upgrading of primary alcohols by C-H bond breaking currently requires temperatures of >200 °C. In this work, new understanding from simulation of a temperature-programmed reaction study with methanol over a CeO2(111) surface shows C-H bond breaking and the subsequent desorption of formaldehyde, even below room temperature. This is of particular interest because CeO2 is a naturally abundant and inexpensive metal oxide. We combine density functional theory and kinetic Monte Carlo methods to show that the low-temperature C-H bond breaking occurs via disproportionation of adjacent methoxy species. We further show from calculations that the same transition state with comparable activation energy exists for other primary alcohols; with ethanol, 1-propanol, and 1-butanol explicitly calculated. These findings indicate a promising class of transition states to search for in seeking low-temperature C-H bond breaking over inexpensive oxides.

The Cover picture showcases selectivity differences for catalytic conversion of benzylic alcohol over Pd and AuPd nanoparticles. These systems are of interest because the conversion occurs at low temperatures in the liquid phase without the need for additional solvents.In their Communication, A. Savara et al. conclude that the different oxygen adsorption properties between Pd and AuPd are the molecular origins of the selectivity differences observed towards toluene, benzaldehyde and other products. Microkinetic modeling of Pd and AuPd matched the experimental selectivities for 18 different trends as a function of oxygen pressure, liquid phase alcohol concentration and temperature. The microkinetic modeling indicated that reduced oxygen adsorption on AuPd relative to Pd —consistent with density functional theory— explains the differences in selectivity observed. More information can be found in the Communication by A. Savara et al. on page 253 in Issue 2, 2017 (DOI: 10.1002/cctc.201601295).

The same mechanism and microkinetic model used for benzyl alcohol oxidation over Pd/C was shown to apply to benzyl alcohol oxidation over AuPd/C. Almost all of the selectivity differences could be explained by a decrease in oxygen adsorption on AuPd. After isolating oxygen adsorption as being the origin of the selectivity differences, density functional theory was used to investigate the oxygen adsorption properties of a pure Pd surface, a pure Au surface, and an alloyed AuPd surface. The calculations showed that Au–Pd alloying decreased the oxygen adsorption properties relative to pure Pd, which explained the selectivity differences, consistent with the microkinetic modeling.

Lattice based Kinetic Monte Carlo (KMC) simulations offer a powerful simulation technique for investigating large reaction networks while retaining spatial configuration information, unlike ordinary differential equations. However, large chemical reaction networks can contain reaction processes with rates spanning multiple orders of magnitude. This can lead to the problem of “KMC stiffness” (similar to stiffness in differential equations), where the computational expense has the potential to be overwhelmed by very short time-steps during KMC simulations, with the simulation spending an inordinate amount of KMC steps/CPU time simulating fast frivolous processes (FFPs) without progressing the system (reaction network). In order to achieve simulation times that are experimentally relevant or desired for predictions, a dynamic throttling algorithm involving separation of the processes into speed-ranks based on event frequencies has been designed and implemented with the intent of decreasing the probability of FFP events, and increasing the probability of slow process events—allowing rate limiting events to become more likely to be observed in KMC simulations. This Staggered Quasi-Equilibrium Rank-based Throttling for Steady-state (SQERTSS) algorithm is designed for use in achieving and simulating steady-state conditions in KMC simulations. As shown in this work, the SQERTSS algorithm also works for transient conditions: the correct configuration space and final state will still be achieved if the required assumptions are not violated, with the caveat that the sizes of the time-steps may be distorted during the transient period.

We introduce for the first time uncertainty quantification and global sensitivity analysis to assess the effect of error in linear scaling relations (LSRs) and Bronsted-Evans-Polanyi (BEP) relations on activity and selectivity maps of microkinetic models, which have recently been used for in silico prediction of new materials. The method is applied to ethanol hydrodeoxygenation (HDO). Selectivity trends are driven by adsorbate thermochemistry rather than kinetics. Uncertainty quantification calculations show that the most likely location of the maximum conversion can be estimated to be within about 10 kcal/mol in the C and 0 binding energies. Broad selectivity trends are even more robust. Model rates show uncertainties of 2-3 orders of magnitude about the median. Uncertainty in the activity predictions is dominated by that of the LSRs. Our calculations demonstrate that there is a common initial mechanism of ethanol HDO and decomposition, and the stability of decomposition products is crucial to determining the selectivity. (C) 2016 Elsevier Inc. All rights reserved.

We have used density functional theory calculations to investigate the temperature-programmed desorption (TPD) of methanol from CeO2(111). For the first time, low-temperature water formation and high-temperature methanol desorption are explained by our calculations. High coverages of methanol, which correspond to experimental conditions, are required to properly describe these features of the TPD spectrum. We identify a mechanism for the low-temperature formation of water involving the dissociation of two methanol molecules on the same surface O atom and filling of the resulting surface vacancy with one of the methoxy products. After water desorption, methoxy groups are stabilized on the surface and react at higher temperatures to form methanol and formaldehyde by a disproportionation mechanism. Alternatively, the stabilized methoxy groups undergo sequential C-H scission reactions to produce formaldehyde. Calculated energy requirements and methanol/formaldehyde selectivity agree with the experimental data.