Statistics of random walks in geologically relevant conductance fields.

Statistics of diffusion, modeled by random walks, such as the mean number of distinct sites visited S(t) at time t, the mean probability P_{0}(t) of being at the origin of the walk, and the mean-squared displacements 〈R^{2}(t)〉 of the random walkers have been studied extensively in the past in both regular lattices and such disordered media as percolation clusters and other fractal structures, and universal power laws for such quantities have been derived. S(t) provides insight into reaction properties of geological formations, while P_{0}(t) is directly linked with the problem of back diffusion in remediation of groundwater aquifers. In all such studies, it was assumed that the conductances of the bonds that connect nearest-neighbor sites of the lattices are equal.

Relation between critical exponent of the conductivity and the morphological exponents of percolation theory.

A central unsolved problem in percolation theory over the past five decades has been whether there is a direct relationship between the critical exponents that characterize the power-law behavior of the transport properties near the percolation threshold, particularly the effective electrical conductivity σ_{e}, and the exponents that describe the morphology of percolation clusters. The problem is also relevant to the relation between the static exponents of percolation clusters and the critical dynamics of spin waves in dilute ferromagnets, the elasticity of gels and composite solids, hopping conductivity in semiconductors, solute transport in porous media, and many others. We propose an approach to address the problem by showing that the contributions to σ_{e} can be decomposed into several groups representing the structure of percolation networks, including their mass and tortuosity, as well as constrictivity that describes the fluctuations in the driving potential gradient along the transport paths.

Taylor-Aris Dispersion in Nanotubes: Analytical Solution, Effects of Slip and Surface Potential Landscape, and Measurement of the Slip Length.

Taylor-Aris (T-A) dispersion of a solute in a flowing solvent is a fundamental phenomenon in most mass-transfer processes. Despite its significance and numerous applications in microreactors, colloidal transport in confined media, chromatographic separation, and transport in biological tissues, the effect of the slip length and the topology of surface potential landscapes on T-A dispersion in nanostructured channels has not been studied in detail. We propose a novel methodology for molecular dynamics (MD) simulation of T-A dispersion in such systems, derive an analytical expression for the dispersion coefficient in them, and report on the results of extensive MD simulations of the phenomenon in carbon nanotubes and hexagonal carbon nanochannels.

Species Richness Net Primary Productivity and the Water Balance Problem.

Species energy theory suggests that, because of limitations on reproduction efficiency, a minimum density of plant individuals per viable species exists and that this minimum correlates the total number of plant individuals with the number of species . The simplest assumption is that the mean energy input per individual plant is independent of the number of individuals, making , and thus as well, proportional to the total energy input into the system. The primary energy input to a plant-dominated ecosystem is estimated as its Net Primary Productivity ().

Physics-informed and data-driven discovery of governing equations for complex phenomena in heterogeneous media.

Rapid evolution of sensor technology, advances in instrumentation, and progress in devising data-acquisition software and hardware are providing vast amounts of data for various complex phenomena that occur in heterogeneous media, ranging from those in atmospheric environment, to large-scale porous formations, and biological systems. The tremendous increase in the speed of scientific computing has also made it possible to emulate diverse multiscale and multiphysics phenomena that contain elements of stochasticity or heterogeneity, and to generate large volumes of numerical data for them. Thus, given a heterogeneous system with annealed or quenched disorder in which a complex phenomenon occurs, how should one analyze and model the system and phenomenon, explain the data, and make predictions for length and time scales much larger than those over which the data were collected? We divide such systems into three distinct classes.

Interpreting water demands of forests and grasslands within a new Budyko formulation of evapotranspiration using percolation theory.

The relationship between carbon cycle and water demand is key to understanding global climate change, vegetation productivity, and predicting the future of water resources. The water balance, which enumerates the relative fractions of precipitation P that run off, Q, or are returned to the atmosphere through evapotranspiration, ET, links drawdown of atmospheric carbon with the water cycle through plant transpiration. Our theoretical description based on percolation theory proposes that dominant ecosystems tend to maximize drawdown of atmospheric carbon in the process of growth and reproduction, thus providing a link between carbon and water cycles.

Data-driven discovery of the governing equations for transport in heterogeneous media by symbolic regression and stochastic optimization.

With advances in instrumentation and the tremendous increase in computational power, vast amounts of data are becoming available for many complex phenomena in macroscopically heterogeneous media, particularly those that involve flow and transport processes, which are problems of fundamental interest that occur in a wide variety of physical systems. The absence of a length scale beyond which such systems can be considered as homogeneous implies that the traditional volume or ensemble averaging of the equations of continuum mechanics over the heterogeneity is no longer valid and, therefore, the issue of discovering the governing equations for flow and transport processes is an open question. We propose a data-driven approach that uses stochastic optimization and symbolic regression to discover the governing equations for flow and transport processes in heterogeneous media.

Retraction: Phase transitions, percolation, fracture of materials, and deep learning [Phys. Rev. E 102, 011001(R) (2020)].

Retraction of DOI: 10.1103/PhysRevE.102.

Graphyne-3: a highly efficient candidate for separation of small gas molecules from gaseous mixtures.

Two-dimensional nanosheets, such as the general family of graphenes have attracted considerable attention over the past decade, due to their excellent thermal, mechanical, and electrical properties. We report on the result of a study of separation of gaseous mixtures by a model graphyne-3 membrane, using extensive molecular dynamics simulations and density functional theory. Four binary and one ternary mixtures of H[Formula: see text], CO[Formula: see text], CH[Formula: see text] and C[Formula: see text]H[Formula: see text] were studied.

Elastic moduli of body-centered cubic lattice near rigidity percolation threshold: Finite-size effects and evidence for first-order phase transition.

Extensive numerical simulations of rigidity percolation with only central forces in large three-dimensional lattices have indicated that many of their topological properties undergo a first-order phase transition at the rigidity percolation threshold p_{ce}. In contrast with such properties, past numerical calculations of the elastic moduli of the same lattices had provided evidence for a second-order phase transition. In this paper we present the results of extensive simulation of rigidity percolation in large body-centered cubic (bcc) lattices, and show that as the linear size L of the lattice increases, the elastic moduli close to p_{ce} decrease in a stepwise, discontinuous manner, a feature that is absent in lattices with L<30.

Molecular origin of sliding friction and flash heating in rock and heterogeneous materials.

It is generally believed that earthquakes occur when faults weaken with increasing slip rates. An important factor contributing to this phenomenon is the faults' dynamic friction, which may be reduced during earthquakes with high slip rates, a process known as slip-rate weakening. It has been hypothesized that the weakening phenomenon during fault slip may be activated by thermal pressurization of pores' fluid and flash heating, a microscopic phenomenon in which heat is generated at asperity contacts due to high shear slip rates.

Molecular Dynamics Study of Structure, Folding, and Aggregation of Poly-PR and Poly-GR Proteins.

Poly-proline-arginine (poly-PR) and poly-glycine-arginine (poly-GR) proteins are believed to be the most toxic dipeptide repeat (DPR) proteins that are expressed by the hexanucleotide repeat expansion mutation in C9ORF72, which are associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) diseases. Their structural information and mechanisms of toxicity remain incomplete, however. Using molecular dynamics simulation and all-atom model of proteins, we study folding and aggregation of both poly-PR and poly-GR.

Formation of a Stable Bridge between Two Disjoint Nanotubes with Single-File Chains of Water.

It was recently demonstrated that stable water bridges can form between two relatively large disjoint nanochannels, such as carbon nanotubes (CNTs), under an applied pressure drop. Such bridges are relevant to fabrication of nanostructured materials, drug delivery, water desalination devices, hydrogen fuel cells, dip-pen nanolithography, and several other applications. If the nanotubes are small enough, however, then one has only single-file hydrogen-bonded chains of water molecules.

Phase transitions, percolation, fracture of materials, and deep learning.

Percolation and fracture propagation in disordered solids represent two important problems in science and engineering that are characterized by phase transitions: loss of macroscopic connectivity at the percolation threshold p_{c} and formation of a macroscopic fracture network at the incipient fracture point (IFP). Percolation also represents the fracture problem in the limit of very strong disorder. An important unsolved problem is accurate prediction of physical properties of systems undergoing such transitions, given limited data far from the transition point.

Quantifying accuracy of stochastic methods of reconstructing complex materials by deep learning.

Time and cost are two main hurdles to acquiring a large number of digital image I of the microstructure of materials. Thus, use of stochastic methods for producing plausible realizations of materials' morphology based on one or very few images has become an increasingly common practice in their modeling. The accuracy of the realizations is often evaluated using two-point microstructural descriptors or physics-based modeling of certain phenomena in the materials, such as transport processes or fluid flow.

Effect of heterogeneity and spatial correlations on the structure of a tumor invasion front in cellular environments.

Analysis of invasion front has been widely used to decipher biological properties, as well as the growth dynamics of the corresponding populations. Likewise, the invasion front of tumors has been investigated, from which insights into the biological mechanisms of tumor growth have been gained. We develop a model to study how tumors' invasion front depends on the relevant properties of a cellular environment.

Morphology and kinetics of random sequential adsorption of superballs: From hexapods to cubes.

Superballs represent a class of particles whose shapes are defined by the domain |x|^{2p}+|y|^{2p}+|z|^{2p}≤R^{2p}, with p∈(0,∞) being the deformation parameter. 0 View Article and Find Full Text PDF

Regulation of migration of chemotactic tumor cells by the spatial distribution of collagen fiber orientation.

Collagen fibers, an important component of the extracellular matrix (ECM), can both inhibit and promote cellular migration. In vitro studies have revealed that the fibers' orientations are crucial to cellular invasion, while in vivo investigations have led to the development of tumor-associated collagen signatures (TACS) as an important prognostic factor. Studying biophysical regulation of cell invasion and the effect of the fibers' orientation not only deepens our understanding of the phenomenon, but also helps classify the TACSs precisely, which is currently lacking.

Exact enumeration approach to first-passage time distribution of non-Markov random walks.

We propose an analytical approach to study non-Markov random walks by employing an exact enumeration method. Using the method, we derive an exact expansion for the first-passage time (FPT) distribution of any continuous differentiable non-Markov random walk with Gaussian or non-Gaussian multivariate distribution. As an example, we study the FPT distribution of the fractional Brownian motion with a Hurst exponent H∈(1/2,1) that describes numerous non-Markov stochastic phenomena in physics, biology, and geology and for which the limit H=1/2 represents a Markov process.

Enhancing images of shale formations by a hybrid stochastic and deep learning algorithm.

Accounting for the morphology of shale formations, which represent highly heterogeneous porous media, is a difficult problem. Although two- or three-dimensional images of such formations may be obtained and analyzed, they either do not capture the nanoscale features of the porous media, or they are too small to be an accurate representative of the media, or both. Increasing the resolution of such images is also costly.

Efficient Transport Between Disjoint Nanochannels by a Water Bridge.

Water channels are important to new purification systems, osmotic power harvesting in salinity gradients, hydroelectric voltage conversion, signal transmission, drug delivery, and many other applications. To be effective, water channels must have structures more complex than a single tube. One way of building such structures is through a water bridge between two disjoint channels that are not physically connected.

Molecular dynamics study of structure, folding, and aggregation of poly-glycine-alanine (Poly-GA).

Poly-glycine-alanine (poly-GA) proteins are widely believed to be one of the main toxic dipeptide repeat molecules associated with amyotrophic lateral sclerosis (ALS) and frontotemporal dementia diseases. Using discontinuous molecular dynamics simulation and an all-atom model of the proteins, we study folding, stability, and aggregation of poly-GA. The results demonstrate that poly-GA is an aggregation-prone protein that, after a long enough time, forms β-sheet-rich aggregates that match recent experiment data and that two unique helical structures are formed very frequently, namely, β-helix and double-helix.

Effect of the geometry of confining media on the stability and folding rate of -helix proteins.

Protein folding in confined media has attracted wide attention over the past 15 years due to its importance to both and applications. It is generally believed that protein stability increases by decreasing the size of the confining medium, the medium's walls are repulsive, and that the maximum folding temperature in confinement is in a pore whose size is only slightly larger than the smallest dimension of a protein's folded state. Until recently, the stability of proteins in pores with a size very close to that of the folded state has not received the attention it deserves.

Complex Behavior of Ordered and Icelike Water in Carbon Nanotubes near Its Bulk Boiling Point.

We report the results of extensive molecular dynamics (MD) simulation of water in a carbon nanotube (CNT) with a specific diameter over a wide range of temperatures from 343 to 423 K. In order to characterize the nature of water, we have computed the Kirkwood g-factor, the ten Wolde parameter, the radial distribution, the cage correlation, the intermediate scattering functions, the mean-square displacements of the water molecules, and the connectivity of the oxygen atoms. The computed properties provide evidence for complex behavior.