The smallest electrochemical bubbles.

Many of the relevant electrochemical processes in the context of catalysis or energy conversion and storage, entail the production of gases. This often implicates the nucleation of bubbles at the interface, with the concomitant blockage of the electroactive area leading to overpotentials and Ohmic drop. Nanoelectrodes have been envisioned as assets to revert this effect, by inhibiting bubble formation. Experiments show, however, that nanobubbles nucleate and attach to nanoscale electrodes, imposing a limit to the current, which turns out to be independent of size and applied potential in a wide range from 3 nm to tenths of microns. Here we investigate the potential-current response for disk electrodes of diameters down to a single-atom, employing molecular simulations including electrochemical generation of gas. Our analysis reveals that nanoelectrodes of 1 nm can offer twice as much current as that delivered by electrodes with areas four orders of magnitude larger at the same bias. This boost in the extracted current is a consequence of the destabilization of the gas phase. The grand potential of surface nanobubbles shows they can not reach a thermodynamically stable state on supports below 2 nm. As a result, the electroactive area becomes accessible to the solution and the current turns out to be sensitive to the electrode radius. In this way, our simulations establish that there is an optimal size for the nanoelectrodes, in between the single-atom and ∼3 nm, that optimizes the gas production.

Kinetic coevolutionary models predict the temporal emergence of HIV-1 resistance mutations under drug selection pressure.

Drug resistance in HIV type 1 (HIV-1) is a pervasive problem that affects the lives of millions of people worldwide. Although records of drug-resistant mutations (DRMs) have been extensively tabulated within public repositories, our understanding of the evolutionary kinetics of DRMs and how they evolve together remains limited. Epistasis, the interaction between a DRM and other residues in HIV-1 protein sequences, is key to the temporal evolution of drug resistance. We use a Potts sequence-covariation statistical-energy model of HIV-1 protein fitness under drug selection pressure, which captures epistatic interactions between all positions, combined with kinetic Monte-Carlo simulations of sequence evolutionary trajectories, to explore the acquisition of DRMs as they arise in an ensemble of drug-naive patient protein sequences. We follow the time course of 52 DRMs in the enzymes protease, RT, and integrase, the primary targets of antiretroviral therapy. The rates at which DRMs emerge are highly correlated with their observed acquisition rates reported in the literature when drug pressure is applied. This result highlights the central role of epistasis in determining the kinetics governing DRM emergence. Whereas rapidly acquired DRMs begin to accumulate as soon as drug pressure is applied, slowly acquired DRMs are contingent on accessory mutations that appear only after prolonged drug pressure. We provide a foundation for using computational methods to determine the temporal evolution of drug resistance using Potts statistical potentials, which can be used to gain mechanistic insights into drug resistance pathways in HIV-1 and other infectious agents.

Transport of synaptic vesicles is modulated by vesicular reversals and stationary cargo clusters.

Stationary clusters of vesicles are a prominent feature of axonal transport, but little is known about their physiological and functional relevance to axonal transport. Here, we investigated the role of vesicle motility characteristics in modulating the formation and lifetimes of such stationary clusters, and their effect on cargo flow. We developed a simulation model describing key features of axonal cargo transport, benchmarking the model against experiments in the posterior lateral mechanosensory neurons of Caenorhabditis elegans. Our simulations included multiple microtubule tracks and varied cargo motion states, and account for dynamic cargo-cargo interactions. Our model also incorporates static obstacles to vesicle transport in the form of microtubule ends, stalled vesicles and stationary mitochondria. We demonstrate, both in simulations and in an experimental system, that a reduction in reversal rates is associated with a higher proportion of long-lived stationary vesicle clusters and reduced net anterograde transport. Our simulations support the view that stationary clusters function as dynamic reservoirs of cargo vesicles, and reversals aid cargo in navigating obstacles and regulate cargo transport by modulating the proportion of stationary vesicle clusters along the neuronal process.

Cross-linkers at growing microtubule ends generate forces that drive actin transport.

SignificanceComplex cellular processes such as cell migration require coordinated remodeling of both the actin and the microtubule cytoskeleton. The two networks for instance exert forces on each other via active motor proteins. Here we show that, surprisingly, coupling via passive cross-linkers can also result in force generation. We specifically study the transport of actin filaments by growing microtubule ends. We show by cell-free reconstitution experiments, computer simulations, and theoretical modeling that this transport is driven by the affinity of the cross-linker for the chemically distinct microtubule tip region. Our work predicts that growing microtubules could potentially rapidly relocate newly nucleated actin filaments to the leading edge of the cell and thus boost migration.

Kinetic Monte Carlo Simulations of Low-Temperature NH3-SCR over Cu-Exchanged Chabazite.

Cu-exchanged chabazite (Cu-CHA) is widely used for ammonia assisted selective catalytic reduction of nitrogen oxides (NH3-SCR). The Cu+ ions are at low temperatures solvated by NH3 forming mobile [Cu(NH3)2]+ complexes. The dynamic behaviour of the complexes is critical as O2 adsorption requires a pair of complexes to form a [Cu2(NH3)4O2]2+ peroxo-species over which NO couples with NH3. Here we introduce a first principles-based kinetic Monte Carlo approach to explore the effect of the Al-distribution on the reaction kinetics of NH3-SCR over Cu-CHA. The method allows us to scrutinize the interplay between the pairing of [Cu(NH3)2]+ complexes and the reaction landscape for the NH3-SCR reaction over the peroxo-complex. The Al-distribution affects the stability of the [Cu(NH3)2]+ pairs as well as the kinetic parameters of the SCR-reaction. The turn-over frequency is determined by the stability of the [Cu(NH3)2]+ pairs and the relative strength of NO and NH3 adsorption once a pair is present. The results establish the hierarchy of effects that influences the performance of Cu-CHA over NH3-SCR and provide a computational basis for further development of the Cu-CHA material.

A Set of New Promising Solid Chalcogenides for Na-Ion Batteries: Yield from the Crystal Chemical, Monte-Carlo and Quantum-Chemical Calculations.

Free crystal space in more than 600 chalcogenide structures taken out from the ICSD has been theoretically analyzed. As a result, wide voids and channels accessible for Na+-ion migration were found in 236 structures. Among them, 165 compounds have not been described in the literature as Na+-conducting materials. These materials have been subjected to stepwise quantitative calculations. The bond valence site energy method has enabled the identification of 57 entries as the most promising ion conductors in which the Na+-ion migration energy (Em) is less than 0.55 eV for 2D or 3D diffusion. The kinetic Monte-Carlo method has been carried out for these substances; as a result, resulting in nine of the most prospective compounds with Na+-ionic conductivity Ϭ≥10-4 S cm-1 at room temperature were selected, for which the density functional theory calculations have been performed yielding six best candidates. Additionally, a logarithmic relationship was established between the values of Em and the diffusion channel radii as well as a linear relationship between Ϭ and the void radius.

Accelerating the Design of Multishell Upconverting Nanoparticles through Bayesian Optimization.

The photon upconverting properties of lanthanide-doped nanoparticles drive their applications in imaging, optoelectronics, and additive manufacturing. To maximize their brightness, these upconverting nanoparticles (UCNPs) are often synthesized as core/shell heterostructures. However, the large numbers of compositional and structural parameters in multishell heterostructures make optimizing optical properties challenging. Here, we demonstrate the use of Bayesian optimization (BO) to learn the structure and design rules for multishell UCNPs with bright ultraviolet and violet emission. We leverage an automated workflow that iteratively recommends candidate UCNP structures and then simulates their emission spectra using kinetic Monte Carlo. Yb3+/Er3+- and Yb3+/Er3+/Tm3+-codoped UCNP nanostructures optimized with this BO workflow achieve 10- and 110-fold brighter emission within 22 and 40 iterations, respectively. This workflow can be expanded to structures with higher compositional and structural complexity, accelerating the discovery of novel UCNPs while domain-specific knowledge is being developed.

Surface Adatom Diffusion-Assisted Dislocation Nucleation in Metal Nanowires.

We employ a hybrid diffusion- and nucleation-based kinetic Monte Carlo model to elucidate the significant impact of adatom diffusion on incipient surface dislocation nucleation in metal nanowires. We reveal a stress-regulated diffusion mechanism that promotes preferential accumulation of diffusing adatoms near nucleation sites, which explains the experimental observations of strong temperature but weak strain-rate dependence as well as temperature-dependent scatter of the nucleation strength. Furthermore, the model demonstrates that a decreasing rate of adatom diffusion with an increasing strain rate will lead to stress-controlled nucleation being the dominant nucleation mechanism at higher strain rates. Overall, our model offers new mechanistic insights into how surface adatom diffusion directly impacts the incipient defect nucleation process and resulting mechanical properties of metal nanowires.

Dynamic Adaptation of Active Site Driven by Dual-side Adsorption in Single-Atomic Catalysts During CO2 Electroreduction.

Single-atom iron embedded in N-doped carbon (Fe-N-C) is among the most representative single-atomic catalysts (SACs) for electrochemical CO2 reduction reaction (CO2RR). Despite the simplicity of the active site, the CO2-to-CO mechanism on Fe-N-C remains controversial. Firstly, there is a long debate regarding the rate-determining step (RDS) of the reactions. Secondly, recent computational and experimental studies are puzzled by the fact that the CO-poisoned Fe centers still remain highly active at high potentials. Thirdly, there are ongoing challenges in elucidating the high selectivity of hydrogen evolution reaction (HER) over CO2RR at high potentials. In this work, we introduce a novel CO2RR mechanism on Fe-N-C, which was inspired by the dynamic of active sites in biological systems. By employing grand-canonical density functional theory and kinetic Monte-Carlo, we found that the RDS is not fixed but changes with the applied potential. We demonstrated that our proposed dual-side mechanisms could clarify the reason behind the high catalytic activity of CO-poisoned metal centers, as well as the high selectivity of HER over CO2RR at high potential. This study provides a fundamental explanation for long-standing puzzles of an important catalyst and calls for the importance of considering the dynamic of active sites in reaction mechanisms.

A general expression for the statistical error in a diffusion coefficient obtained from a solid-state molecular-dynamics simulation.

Analysis of the mean squared displacement of species k , r k 2 , as a function of simulation time t constitutes a powerful method for extracting, from a molecular-dynamics (MD) simulation, the tracer diffusion coefficient, D k * . The statistical error in D k * is seldom considered, and when it is done, the error is generally underestimated. In this study, we examined the statistics of r k 2 t curves generated by solid-state diffusion by means of kinetic Monte Carlo sampling. Our results indicate that the statistical error in D k * depends, in a strongly interrelated way, on the simulation time, the cell size, and the number of relevant point defects in the simulation cell. Reducing our results to one key quantity-the number of k particles that have jumped at least once-we derive a closed-form expression for the relative uncertainty in D k * . We confirm the accuracy of our expression through comparisons with self-generated MD diffusion data. With the expression, we formulate a set of simple rules that encourage the efficient use of computational resources for MD simulations.

A Method Probing High-Temperature Oxidation Behavior of Crystalline Materials.

To date, the oxidation behavior of crystal materials is not fully understood; additional research is needed to understand the oxidation of materials. Herein, density functional theory (DFT) calculations and a 3D kinetic Monte Carlo (KMC) model are used to investigate the infiltration and diffusion behaviors of oxygen atoms within the crystal. Oxygen molecules readily adsorbes on crystal surfaces of the material and rapidly dissociates, verified by both first-principles calculations and energy-dispersive spectrometer (EDS) results. The infiltration ability of oxygen atoms into the inner crystal layers is affected by the surrounding oxygen atom, lattice compactness, and other factors. Energy-barrier calculations show that crystal thin/dense layers have significant effects on the crystal oxidation process, so high-pressure technology is used to investigate this correlation experimentally. KMC calculations and thermogravimetric analyses (TGA) show the infiltration behavior of oxygen atoms in the main crystal plane (211) toward the inner layers has the highest proportion to the actual high-temperature oxidation behavior of the title material. The results of both the KMC calculations and thermal experiments show the material peeled off upon further oxidation, which accelerates oxidation. At the same time, high-pressure treatment increases the oxidation resistance of materials at lower temperatures (<600 °C).

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 60s, 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 15 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. This article is part of a discussion meeting issue 'Supercomputing simulations of advanced materials'.

Circular depolarization spectroscopy: A new tool to study photo-imprinting of chirality.

When irradiating a molecular material containing photo-isomerizable groups with pure circularly polarized light, a particular handedness may get imprinted into the material. To study the mechanism and kinetics of this process in situ and operando, we have developed a new chiroptical tool where the circular polarization of the incident circularly polarized light is monitored after transmission through the photoactive layer. Practical limits to the resolution and sensitivity of the measurements as well as its calibration are discussed. To aid interpretation of experimental results, we present kinetic Monte Carlo simulations on a model for the active material involving photo-induced reorientation of molecules in a cholesteric organization. The simulations support the interpretation of a transient minimum in the degree of circular polarization of the transmitted light in terms of a nematic transient state during photo-inversion of a cholesteric organization in the molecular material.

Structure of Amorphous Two-Dimensional Materials: Elemental Monolayer Amorphous Carbon versus Binary Monolayer Amorphous Boron Nitride.

The structure of amorphous materials has been debated since the 1930s as a binary question: amorphous materials are either Zachariasen continuous random networks (Z-CRNs) or Z-CRNs containing crystallites. It was recently demonstrated, however, that amorphous diamond can be synthesized in either form. Here we address the question of the structure of single-atom-thick amorphous monolayers. We reanalyze the results of prior simulations for amorphous graphene and report kinetic Monte Carlo simulations based on alternative algorithms. We find that crystallite-containing Z-CRN is the favored structure of elemental amorphous graphene, as recently fabricated, whereas the most likely structure of binary monolayer amorphous BN is altogether different than either of the two long-debated options: it is a compositionally disordered "pseudo-CRN" comprising a mix of B-N and noncanonical B-B and N-N bonds and containing "pseudocrystallites", namely, honeycomb regions made of noncanonical hexagons. Implications for other nonelemental 2D and bulk amorphous materials are discussed.

Multiscale Model of CVD Growth of Graphene on Cu(111) Surface.

Due to its outstanding properties, graphene has emerged as one of the most promising 2D materials in a large variety of research fields. Among the available fabrication protocols, chemical vapor deposition (CVD) enables the production of high quality single-layered large area graphene. To better understand the kinetics of CVD graphene growth, multiscale modeling approaches are sought after. Although a variety of models have been developed to study the growth mechanism, prior studies are either limited to very small systems, are forced to simplify the model to eliminate the fast process, or they simplify reactions. While it is possible to rationalize these approximations, it is important to note that they have non-trivial consequences on the overall growth of graphene. Therefore, a comprehensive understanding of the kinetics of graphene growth in CVD remains a challenge. Here, we introduce a kinetic Monte Carlo protocol that permits, for the first time, the representation of relevant reactions on the atomic scale, without additional approximations, while still reaching very long time and length scales of the simulation of graphene growth. The quantum-mechanics-based multiscale model, which links kinetic Monte Carlo growth processes with the rates of occurring chemical reactions, calculated from first principles makes it possible to investigate the contributions of the most important species in graphene growth. It permits the proper investigation of the role of carbon and its dimer in the growth process, thus indicating the carbon dimer to be the dominant species. The consideration of hydrogenation and dehydrogenation reactions enables us to correlate the quality of the material grown within the CVD control parameters and to demonstrate an important role of these reactions in the quality of the grown graphene in terms of its surface roughness, hydrogenation sites, and vacancy defects. The model developed is capable of providing additional insights to control the graphene growth mechanism on Cu(111), which may guide further experimental and theoretical developments.

Gaining Insight into the Electrochemical Interface Dynamics in an Organic Redox Flow Battery with a Kinetic Monte Carlo Approach.

Finding low-cost and nontoxic redox couples for organic redox flow batteries is challenging due to unrevealed reaction mechanisms and side reactions. In this study, a 3D kinetic Monte Carlo model to study the electrode-anolyte interface of a methyl viologen-based organic redox flow battery is presented. This model captures various electrode processes, such as ionic displacement and degradation of active materials. The workflow consists of input parameters obtained from density functional theory calculations, a kinetic Monte Carlo algorithm to simulate the discharging process, and an electric double layer model to account for the electric field distribution near the electrode surface. Galvanostatic discharge is simulated at different anolyte concentrations and input current densities, which demonstrate that the model captured the formation of the electrical double layer due to ionic transport. The simulated electrochemical kinetics (potential, charge density) are found to be in agreement with the Nernst equation and the obtained EDL structure corresponded with published molecular dynamics results. The model's flexibility allows further applications of simulating the behavior of different redox couples and makes it possible to consider other molecular-scale phenomena. This study paves the way for computational screening of active species by assessing their potential kinetics in electrochemical environments.

Kinetic Monte Carlo simulations of Ge-Sb-Te thin film crystallization.

Simulation of atomic redistribution in Ge-Sb-Te (GST)-based memory cells during SET/RESET cycling is needed in order to understand GST memory cell failure and to design improved non-volatile memories. However, this type of atomic scale simulations is extremely challenging. In this work, we propose to use a simplified GST system in order to catch the basics of atomic redistribution in Ge-rich GST (GrGST) films using atomistic kinetic Monte Carlo simulations. Comparison between experiments and simulations shows good agreements regarding the influence of Ge excess on GrGST crystallization, as well as concerning the GST growth kinetic in GrGST films, suggesting the crystallized GST ternary compound to be off-stoichiometric. According to the simulation of atomic redistribution in GrGST films during SET/RESET cycling, the film microstructure stabilized during cycling is significantly dependent of the GST ternary phase stoichiometry. The use of amorphous layers exhibiting the GST ternary phase stoichiometry placed at the bottom or at the top of the GrGST layer is shown to be a way of controlling the microstructure evolution of the film during cycling. The significant evolution of the local composition in the amorphous solution during cycling suggests a non-negligible variation of the crystallization temperature with operation time.

Nonequilibrium site distribution governs charge-transfer electroluminescence at disordered organic heterointerfaces.

The interface between electron-donating (D) and electron-accepting (A) materials in organic photovoltaic (OPV) devices is commonly probed by charge-transfer (CT) electroluminescence (EL) measurements to estimate the CT energy, which critically relates to device open-circuit voltage. It is generally assumed that during CT-EL injected charges recombine at close-to-equilibrium energies in their respective density of states (DOS). Here, we explicitly quantify that CT-EL instead originates from higher-energy DOS site distributions significantly above DOS equilibrium energies. To demonstrate this, we have developed a quantitative and experimentally calibrated model for CT-EL at organic D/A heterointerfaces, which simultaneously accounts for the charge transport physics in an energetically disordered DOS and the Franck-Condon broadening. The 0-0 CT-EL transition lineshape is numerically calculated using measured energetic disorder values as input to 3-dimensional kinetic Monte Carlo simulations. We account for vibrational CT-EL overtones by selectively measuring the dominant vibrational phonon-mode energy governing CT luminescence at the D/A interface using fluorescence line-narrowing spectroscopy. Our model numerically reproduces the measured CT-EL spectra and their bias dependence and reveals the higher-lying manifold of DOS sites responsible for CT-EL. Lowest-energy CT states are situated ∼180 to 570 meV below the 0-0 CT-EL transition, enabling photogenerated carrier thermalization to these low-lying DOS sites when the OPV device is operated as a solar cell rather than as a light-emitting diode. Nonequilibrium site distribution rationalizes the experimentally observed weak current-density dependence of CT-EL and poses fundamental questions on reciprocity relations relating light emission to photovoltaic action and regarding minimal attainable photovoltaic energy conversion losses in OPV devices.

Chemical Etching of Screw Dislocated Transition Metal Dichalcogenides.

Chemical etching can create novel structures inaccessible by growth and provide complementary understanding on the growth mechanisms of complex nanostructures. Screw dislocation-driven growth influences the layer stackings of transition metal dichalcogenides (MX2) resulting in complex spiral morphologies. Herein, we experimentally and theoretically study the etching of screw dislocated WS2 and WSe2 nanostructures using H2O2 etchant. The kinetic Wulff constructions and Monte Carlo simulations establish the etching principles of single MX2 layers. Atomic force microscopy characterization reveals diverse etching morphology evolution behaviors around the dislocation cores and along the exterior edges, including triangular, hexagonal, or truncated hexagonal holes and smooth or rough edges. These behaviors are influenced by the edge orientations, layer stackings, and the strain of screw dislocations. Ab initio calculation and kinetic Monte Carlo simulations support the experimental observations and provide further mechanistic insights. This knowledge can help one to understand more complex structures created by screw dislocations through etching.

Adsorption Equilibrium and Mechanism and of Water Molecule on the Surfaces of Molybdenite (MoS2) Based on Kinetic Monte-Carlo Method.

The oxidation/weathering of molybdenite (MoS2) is too slow to be monitored, even under pure oxygen and high temperatures, while it proceeds rapidly through humid air. The adsorption of water molecules on molybdenite is necessary for the wet oxidation/weathering of molybdenite. Therefore, we employ kinetic Monte Carlo modeling to clarify the adsorption isotherm, site preferences and kinetics of water on different surfaces of molybdenite. Our results indicate that (1) the adsorption capacity and adsorption rate coefficient of H2O on the (110) surface are significantly larger than those on the (001) surface at a temperature of 0~100 °C and a relative humidity of 0~100%, suggesting that the (110) surface is the predominant surface controlling the reactivity and solubility of molybdenite in its interaction with water; (2) the kinetic Monte Carlo modeling considering the adsorption/desorption rate of H2O, dissociation/formation rate of H2O and adsorption/desorption of dissociated H indicates that the adsorption and dissociation of H2O on the (110) surface can be completed in one microsecond (ms) at 298 K and in wet conditions; (3) the adsorption and dissociation of H2O on molybdenite are not the rate-limiting steps in the wet oxidation/weathering of molybdenite; and (4) kinetic Monte Carlo modeling explains the experimental SIMS observation that H2O and OH (rather than H+/H- or H2O) occupy the surface of MoS2 in a short time. This study provides new molecular-scale insights to aid in our understanding of the oxidation/weathering mechanism of molybdenite as the predominant mineral containing molybdenum in the Earth's crust.