Breaking linear scaling relationships with transition metal carbides

Transition metal carbides (TMCs) are proposed as catalysts and supports for small metal particles to replace expensive late transition metals as heterogeneous catalysts

Kinetic Monte Carlo simulations for heterogeneous catalysis: Fundamentals, current status and challenges

Kinetic Monte Carlo (KMC) simulations in combination with first-principles-based calculations are rapidly becoming the gold-standard computational framework for bridging the gap between the wide range of length and time-scales over which heterogeneous catalysis unfolds. First-principles KMC (1p-KMC) simulations provide accurate insights into reactions over surfaces, a vital step towards the rational design of novel catalysts. In this perspective article, we briefly outline basic principles, computational challenges, successful applications, as well as future directions and opportunities of this promising and ever more popular kinetic modeling approach

Stability and reactivity of metal nanoclusters supported on transition metal carbides

Small particles of transition metals (TM) supported on transition metal carbides (TMC) – TMn@TMC – provide a plethora of design opportunities for catalytic applications due to their highly exposed active centres, efficient atom utilisation and the physicochemical properties of the TMC support. To date, however, only a very small subset of TMn@TMC catalysts have been tested experimentally and it is unclear which combinations may best catalyse which chemical reactions. Herein, we develop a high-throughput screening approach to catalyst design for supported nanoclusters based on density functional theory, and apply it to elucidate the stability and catalytic performance of all possible combinations between 7 monometallic nanoclusters (Rh, Pd, Pt, Au, Co, Ni and Cu) and 11 stable support surfaces of TMCs with 1 : 1 stoichiometry (TiC, ZrC, HfC, VC, NbC, TaC, MoC and WC) towards CH4 and CO2 conversion technologies. We analyse the generated database to unravel trends or simple descriptors in their resistance towards metal aggregate formation and sintering, oxidation, stability in the presence of adsorbate species, and study their adsorptive and catalytic properties, to facilitate the discovery of novel materials in the future. We identify 8 TMn@TMC combinations as promising catalysts, all of them being new for experimental validation, thus expanding the chemical space for efficient conversion of CH4 and CO2

Deterministic and stochastic population-level simulations of an artificial lac operon genetic network

Background: The lac operon genetic switch is considered as a paradigm of genetic regulation. This system has a positive feedback loop due to the LacY permease boosting its own production by the facilitated transport of inducer into the cell and the subsequent de-repression of the lac operon genes. Previously, we have investigated the effect of stochasticity in an artificial lac operon network at the single cell level by comparing corresponding deterministic and stochastic kinetic models.Results: This work focuses on the dynamics of cell populations by incorporating the above kinetic scheme into two Monte Carlo (MC) simulation frameworks. The first MC framework assumes stochastic reaction occurrence, accounts for stochastic DNA duplication, division and partitioning and tracks all daughter cells to obtain the statistics of the entire cell population. In order to better understand how stochastic effects shape cell population distributions, we develop a second framework that assumes deterministic reaction dynamics. By comparing the predictions of the two frameworks, we conclude that stochasticity can create or destroy bimodality, and may enhance phenotypic heterogeneity.Conclusions: Our results show how various sources of stochasticity act in synergy with the positive feedback architecture, thereby shaping the behavior at the cell population level. Further, the insights obtained from the present study allow us to construct simpler and less computationally intensive models that can closely approximate the dynamics of heterogeneous cell populations. © 2011 Stamatakis and Zygourakis; licensee BioMed Central Ltd

Adaptive coarse-grained Monte Carlo simulation of reaction and diffusion dynamics in heterogeneous plasma membranes

Background: An adaptive coarse-grained (kinetic) Monte Carlo (ACGMC) simulation framework is applied to reaction and diffusion dynamics in inhomogeneous domains. The presented model is relevant to the diffusion and dimerization dynamics of epidermal growth factor receptor (EGFR) in the presence of plasma membrane heterogeneity and specifically receptor clustering. We perform simulations representing EGFR cluster dissipation in heterogeneous plasma membranes consisting of higher density clusters of receptors surrounded by low population areas using the ACGMC method. We further investigate the effect of key parameters on the cluster lifetime.Results: Coarse-graining of dimerization, rather than of diffusion, may lead to computational error. It is shown that the ACGMC method is an effective technique to minimize error in diffusion-reaction processes and is superior to the microscopic kinetic Monte Carlo simulation in terms of computational cost while retaining accuracy. The low computational cost enables sensitivity analysis calculations. Sensitivity analysis indicates that it may be possible to retain clusters of receptors over the time scale of minutes under suitable conditions and the cluster lifetime may depend on both receptor density and cluster size.Conclusions: The ACGMC method is an ideal platform to resolve large length and time scales in heterogeneous biological systems well beyond the plasma membrane and the EGFR system studied here. Our results demonstrate that cluster size must be considered in conjunction with receptor density, as they synergistically affect EGFR cluster lifetime. Further, the cluster lifetime being of the order of several seconds suggests that any mechanisms responsible for EGFR aggregation must operate on shorter timescales (at most a fraction of a second), to overcome dissipation and produce stable clusters observed experimentally. © 2010 Collins et al; licensee BioMed Central Ltd

Exact distributed kinetic Monte Carlo simulations for on-lattice chemical kinetics: lessons learnt from medium- and large-scale benchmarks

Kinetic Monte-Carlo (KMC) simulations have been instrumental in multiscale catalysis studies, enabling the elucidation of the complex dynamics of heterogeneous catalysts and the prediction of macroscopic performance metrics, such as activity and selectivity. However, the accessible length- and time-scales have been a limiting factor in such simulations. For instance, handling lattices containing millions of sites with “traditional” sequential KMC implementations is prohibitive owing to large memory requirements and long simulation times. We have recently established an approach for exact, distributed, lattice-based simulations of catalytic kinetics which couples the Time-Warp algorithm with the Graph-Theoretical KMC framework, enabling the handling of complex adsorbate lateral interactions and reaction events within large lattices. In this work, we develop a lattice-based variant of the Brusselator system, a prototype chemical oscillator pioneered by Prigogine and Lefever in the late 60’s, to benchmark and demonstrate our approach. This system can form spiral wave patterns, which would be computationally intractable with sequential KMC, while our distributed KMC approach can simulate such patterns 16 and 36 times faster with 625 and 1600 processors, respectively. The medium- and large-scale benchmarks thus conducted, demonstrate the robustness of the approach, and reveal computational bottlenecks that could be targeted in further development efforts

Wetting Properties of Clathrate Hydrates in the Presence of Polycyclic Aromatic Compounds: Evidence of Ion-Specific Effects

Polycyclic aromatic hydrocarbons (PAHs) have attracted remarkable multidisciplinary attention due to their intriguing π–π stacking configurations, showing enormous opportunity for their use in a variety of advanced applications. To secure progress, detailed knowledge on PAHs’ interfacial properties is required. Employing molecular dynamics, we probe the wetting properties of brine droplets (KCl, NaCl, and CaCl2) on sII methane–ethane hydrate surfaces immersed in various oil solvents. Our simulations show synergistic effects due to the presence of PAHs compounded by ion-specific effects. Our analysis reveals phenomenological correlations between the wetting properties and a combination of the binding free-energy difference and entropy changes upon oil solvation for PAHs at oil/brine and oil/hydrate interfaces. The detailed thermodynamic analysis conducted upon the interactions between PAHs and various interfaces identifies molecular-level mechanisms responsible for wettability alterations, which could be applicable for advancing applications in optics, microfluidics, biotechnology, medicine, as well as hydrate management

Microscopic insights on clathrate hydrate growth from non-equilibrium molecular dynamics simulations

Clathrate hydrates form and grow at interfaces. Understanding the relevant molecular processes is crucial for developing hydrate-based technologies. Many computational studies focus on hydrate growth within the aqueous phase using the 'direct coexistence method', which is limited in its ability to investigate hydrate film growth at hydrocarbon-water interfaces. To overcome this shortcoming, a new simulation setup is presented here, which allows us to study the growth of a methane hydrate nucleus in a system where oil-water, hydrate-water, and hydrate-oil interfaces are all simultaneously present, thereby mimicking experimental setups. Using this setup, hydrate growth is studied here under the influence of two additives, a polyvinylcaprolactam oligomer and sodium dodecyl sulfate, at varying concentrations. Our results confirm that hydrate films grow along the oil-water interface, in general agreement with visual experimental observations; growth, albeit slower, also occurs at the hydrate-water interface, the interface most often interrogated via simulations. The results obtained demonstrate that the additives present within curved interfaces control the solubility of methane in the aqueous phase, which correlates with hydrate growth rate. Building on our simulation insights, we suggest that by combining data for the potential of mean force profile for methane transport across the oil-water interface and for the average free energy required to perturb a flat interface, it is possible to predict the performance of additives used to control hydrate growth. These insights could be helpful to achieve optimal methane storage in hydrates, one of many applications which are attracting significant fundamental and applied interests

Assessing the Impact of Adlayer Description Fidelity on Theoretical Predictions of Coking on Ni(111) at Steam Reforming Conditions

Methane steam reforming is an important industrial process for hydrogen production, employing Ni as a low-cost, highly active catalyst, which, however, suffers from coking due to methane cracking. Coking is the accumulation of a stable poison over time, occurring at high temperatures; thus, to a first approximation, it can be treated as a thermodynamic problem. In this work, we developed an Ab initio kinetic Monte Carlo (KMC) model for methane cracking on Ni(111) at steam reforming conditions. The model captures C−H activation kinetics in detail, while graphene sheet formation is described at the level of thermodynamics, to obtain insights into the “terminal (poisoned) state” of graphene/coke within reasonable computational times. We used cluster expansions (CEs) of progressively higher fidelity to systematically assess the influence of effective cluster interactions between adsorbed or covalently bonded C and CH species on the “terminal state” morphology. Moreover, we compared the predictions of KMC models incorporating these CEs into mean-field microkinetic models in a consistent manner. The models show that the “terminal state” changes significantly with the level of fidelity of the CEs. Furthermore, high-fidelity simulations predict C−CH island/rings that are largely disconnected at low temperatures but completely encapsulate the Ni(111) surface at high temperatures