We describe a percolation-type approach to modeling of the processes of aging and certain other properties of tissues analyzed as systems consisting of interacting cells. Tissues are considered as structures made of regular healthy, senescent, dead (
apoptotic) cells, and studied dynamically, with the ongoing processes including regular cell division to fill vacant sites left by dead cells, healthy cells becoming senescent or dying, and other processes. Statistical-mechanics description can provide patterns of time dependence and snapshots of morphological system properties. An illustrative application of the developed theoretical modeling approach is reported, confirming recent experimental findings that inhibition of senescence can lead to extended lifespan.
We model within the kinetic Monte Carlo method the initiation of neck formation and then later evolution of the resulting bridging regions for configurations involving small particles initially positioned fitted between large particles for situations
typical for sintering of FCC nanocrystals, e.g., noble-metal nanoparticles. Neck initiation mechanisms by layering or clustering are identified. The stability of the resulting bridging configurations depends on several parameters, notably, on the relative small to large particle size ratio, and we explain recent experimental findings on improved sintering achieved for certain bimodal size distributions.
We report a kinetic Monte Carlo modeling study of nanocrystal layer sintering. Features that are of interest for the dynamics of the layer as a whole, especially the morphology of the evolving structure, are considered. It is found that the kinetics
of sintering is not entirely a local process, with the layer morphology affected by the kinetics in a larger than few-particle neighborhood. Consideration of a single layer of particles makes the numerics manageable and allows visualization of the results, as well as numerical simulations of several realizations for statistical averaging of properties of interest. We identify optimal regimes for sintering, considering several particle size distributions and temperature control protocols.
We model shell formation of core-shell noble metal nanoparticles. A recently developed kinetic Monte Carlo approach is utilized to reproduce growth morphologies realized in recent experiments on core-shell nanoparticle synthesis, which reported smoot
h epitaxially grown shells. Specifically, we identify growth regimes that yield such smooth shells, but also those that lead to the formation of shells made of small clusters. The developed modeling approach allows us to qualitatively study the effects of temperature and supply the shell-metal atoms on the resulting shell morphology, when grown on a pre-synthesized nanocrystal core.
Kinetic Monte Carlo approach is developed to study aspects of sintering of dispersed nanoparticles of bimodal size distributions. We explore mechanisms of neck development when sintering is initiated at elevated temperatures for nanosize crystalline
surfaces of particles of different sizes. Specifically, the role of smaller particles fitting between larger particles, on the sintering of the latter is considered. Formation of stable necks bridging particles at the nanoscale was found to be governed by layering or clustering mechanisms at the facing surfaces, with clustering leading to a much faster formation of the bridging structure. Temperature, particle sizes and local arrangement, as well as other geometrical factors were found to have a profound effect on sintering mediated by a smaller particle placed in a void between larger particles.
We consider growth of nanoclusters and nanopillars in a model of surface deposition and restructuring yielding morphologies of interest in designing catalysis applications. Kinetic Monte Carlo numerical modeling yields examples of the emergence of FC
C-symmetry surface features, allowing evaluation of the fraction of the resulting active sites with desirable properties, such as (111)-like coordination, as well as suggesting the optimal growth regimes.
