The Influence of the Mechanical Compliance of a Substrate on the Morphology of Nanoporous Gold Thin Films.

Nanoporous gold (np-Au) has found its use in applications ranging from catalysis to biosensing, where pore morphology plays a critical role in performance. While the morphology evolution of bulk np-Au has been widely studied, knowledge about its thin-film form is limited. This work hypothesizes that the mechanical compliance of the thin film substrate can play a critical role in the morphology evolution. Via experimental and finite-element-analysis approaches, we investigate the morphological variation in np-Au thin films deposited on compliant silicone (PDMS) substrates of a range of thicknesses anchored on rigid glass supports and compare those to the morphology of np-Au deposited on glass. More macroscopic (10 s to 100 s of microns) cracks and discrete islands form in the np-Au films on PDMS compared to on glass. Conversely, uniformly distributed microscopic (100 s of nanometers) cracks form in greater numbers in the np-Au films on glass than those on PDMS, with the cracks located within the discrete islands. The np-Au films on glass also show larger ligament and pore sizes, possibly due to higher residual stresses compared to the np-Au/PDMS films. The effective elastic modulus of the substrate layers decreases with increasing PDMS thickness, resulting in secondary np-Au morphology effects, including a reduction in macroscopic crack-to-crack distance, an increase in microscopic crack coverage, and a widening of the microscopic cracks. However, changes in the ligament/pore widths with PDMS thickness are negligible, allowing for independent optimization for cracking. We expect these results to inform the integration of functional np-Au films on compliant substrates into emerging applications, including flexible electronics.

Designed Y3+ Surface Segregation Increases Stability of Nanocrystalline Zinc Aluminate.

The thermal stability of zinc aluminate nanoparticles is critical for their use as catalyst supports. In this study, we experimentally show that doping with 0.5 mol % Y2O3 improves the stability of zinc aluminate nanoparticles. The dopant spontaneously segregates to the nanoparticle surfaces in a phenomenon correlated with excess energy reduction and the hindering of coarsening. Y3+ was selected based on atomistic simulations on a 4 nm zinc aluminate nanoparticle singularly doped with elements of different ionic radii: Sc3+, In3+, Y3+, and Nd3+. The segregation energies were generally proportional to ionic radii, with Y3+ showing the highest potential for surface segregation. Direct measurements of surface thermodynamics confirmed the decreasing trend in surface energy from 0.99 for undoped to 0.85 J/m2 for Y-doped nanoparticles. Diffusion coefficients calculated from coarsening curves for undoped and doped compositions at 850 °C were 4.8 × 10-12 cm2/s and 2.5 × 10-12 cm2/s, respectively, indicating the coarsening inhibition induced by Y3+ results from a combination of a reduced driving force (surface energy) and decreased atomic mobility.

Distribution of Topological Types in Grain-Growth Microstructures.

An open question in studying normal grain growth concerns the asymptotic state to which microstructures converge. In particular, the distribution of grain topologies is unknown. We introduce a thermodynamiclike theory to explain these distributions in two- and three-dimensional systems. In particular, a bendinglike energy E_{i} is associated to each grain topology t_{i}, and the probability of observing that particular topology is proportional to [1/s(t_{i})]e^{-ßE_{i}}, where s(t_{i}) is the order of an associated symmetry group and ß is a thermodynamiclike constant. We explain the physical origins of this approach and provide numerical evidence in support.

A novel approach to describe chemical environments in high-dimensional neural network potentials.

A central concern of molecular dynamics simulations is the potential energy surfaces that govern atomic interactions. These hypersurfaces define the potential energy of the system and have generally been calculated using either predefined analytical formulas (classical) or quantum mechanical simulations (ab initio). The former can accurately reproduce only a selection of material properties, whereas the latter is restricted to short simulation times and small systems. Machine learning potentials have recently emerged as a third approach to model atomic interactions, and are purported to offer the accuracy of ab initio simulations with the speed of classical potentials. However, the performance of machine learning potentials depends crucially on the description of a local atomic environment. A set of invariant, orthogonal, and differentiable descriptors for an atomic environment is proposed, implemented in a neural network potential for solid-state silicon, and tested in molecular dynamics simulations. Neural networks using the proposed descriptors are found to outperform ones using the Behler-Parinello and smooth overlap of atomic position descriptors in the literature.

Nanolayering around and thermal resistivity of the water-hexagonal boron nitride interface.

The water-hexagonal boron nitride interface was investigated by molecular dynamics simulations. Since the properties of the interface change significantly with the interatomic potential, a new method for calibrating the solid-liquid interatomic potential is proposed based on the experimental energy of the interface. The result is markedly different from that given by Lorentz-Berthelot mixing for the Lennard-Jones parameters commonly used in the literature. Specifically, the extent of nanolayering and interfacial thermal resistivity is measured for several interatomic potentials, and the one calibrated by the proposed method gives the least thermal resistivity.

A new interlayer potential for hexagonal boron nitride.

A new interlayer potential is developed for interlayer interactions of hexagonal boron nitride sheets, and its performance is compared with other potentials in the literature using molecular dynamics simulations. The proposed potential contains Coulombic and Lennard-Jones 6-12 terms, and is calibrated with recent experimental data including the hexagonal boron nitride interlayer distance and elastic constants. The potentials are evaluated by comparing the experimental and simulated values of interlayer distance, density, elastic constants, and thermal conductivity using non-equilibrium molecular dynamics. The proposed potential is found to be in reasonable agreement with experiments, and improves on earlier potentials in several respects. Simulated thermal conductivity values as a function of the number of layers and of temperature suggest that the proposed LJ 6-12 potential has the ability to predict some phonon behaviour during heat transport in the out-of-plane direction.

Geometric and topological properties of the canonical grain-growth microstructure.

Many physical systems can be modeled as large sets of domains "glued" together along boundaries-biological cells meet along cell membranes, soap bubbles meet along thin films, countries meet along geopolitical boundaries, and metallic crystals meet along grain interfaces. Each class of microstructures results from a complex interplay of initial conditions and particular evolutionary dynamics. The statistical steady-state microstructure resulting from isotropic grain growth of a polycrystalline material is canonical in that it is the simplest example of a cellular microstructure resulting from a gradient flow of an energy that is directly proportional to the total length or area of all cell boundaries. As many properties of polycrystalline materials depend on their underlying microstructure, a more complete understanding of the grain growth steady state can provide insight into the physics of a broad range of everyday materials. In this paper we report geometric and topological features of these canonical two- and three-dimensional steady-state microstructures obtained through extensive simulations of isotropic grain growth.

Statistical topology of three-dimensional Poisson-Voronoi cells and cell boundary networks.

Voronoi tessellations of Poisson point processes are widely used for modeling many types of physical and biological systems. In this paper, we analyze simulated Poisson-Voronoi structures containing a total of 250000000 cells to provide topological and geometrical statistics of this important class of networks. We also report correlations between some of these topological and geometrical measures. Using these results, we are able to corroborate several conjectures regarding the properties of three-dimensional Poisson-Voronoi networks and refute others. In many cases, we provide accurate fits to these data to aid further analysis. We also demonstrate that topological measures represent powerful tools for describing cellular networks and for distinguishing among different types of networks.

Complete topology of cells, grains, and bubbles in three-dimensional microstructures.

We introduce a general, efficient method to completely describe the topology of individual grains, bubbles, and cells in three-dimensional polycrystals, foams, and other multicellular microstructures. This approach is applied to a pair of three-dimensional microstructures that are often regarded as close analogues in the literature: one resulting from normal grain growth (mean curvature flow) and another resulting from a random Poisson-Voronoi tessellation of space. Grain growth strongly favors particular grain topologies, compared with the Poisson-Voronoi model. Moreover, the frequencies of highly symmetric grains are orders of magnitude higher in the grain growth microstructure than they are in the Poisson-Voronoi one. Grain topology statistics provide a strong, robust differentiator of different cellular microstructures and provide hints to the processes that drive different classes of microstructure evolution.

Correlated grain-boundary distributions in two-dimensional networks.

In polycrystals, there are spatial correlations in grain-boundary species, even in the absence of correlations in the grain orientations, due to the need for crystallographic consistency among misorientations. Although this consistency requirement substantially influences the connectivity of grain-boundary networks, the nature of the resulting correlations are generally only appreciated in an empirical sense. Here a rigorous treatment of this problem is presented for a model two-dimensional polycrystal with uncorrelated grain orientations or, equivalently, a cross section through a three-dimensional polycrystal in which each grain shares a common crystallographic direction normal to the plane of the network. The distribution of misorientations theta, boundary inclinations phi and the joint distribution of misorientations about a triple junction are derived for arbitrary crystal symmetry and orientation distribution functions of the grains. From these, general analytical solutions for the fraction of low-angle boundaries and the triple-junction distributions within the same subset of systems are found. The results agree with existing analysis of a few specific cases in the literature but present a significant generalization.