Transmission channels for light in absorbing random media: from diffusive to ballistic-like transport

While the absorption of light is ubiquitous in nature and in applications, the question remains how absorption modifies the transmission channels in random media. We present a numerical study on the effects of optical absorption on the maximal transmission and minimal reflection channels in a two-dimensional disordered waveguide. In the weak absorption regime, where the system length is less than the diffusive absorption length, the maximal transmission channel is dominated by diffusive transport and it is equivalent to the minimal reflection channel. Its frequency bandwidth is determined by the underlying quasimode width. However, when the absorption is strong, light transport in the maximal transmission channel undergoes a sharp transition and becomes ballistic-like transport. Its frequency bandwidth increases with absorption, and the exact scaling varies with the sample's realization. The minimal reflection channel becomes different from the maximal transmission channel and becomes dominated by absorption. Counterintuitively, we observe in some samples that the minimum reflection eigenvalue increases with absorption. Our results show that strong absorption turns open channels in random media from diffusive to ballistic-like.Comment: 11 pages, 7 figure

Coherent control of photocurrent in a strongly scattering photoelectrochemical system

A fundamental issue that limits the efficiency of many photoelectrochemical systems is that the photon absorption length is typically much longer than the electron diffusion length. Various photon management schemes have been developed to enhance light absorption; one simple approach is to use randomly scattering media to enable broadband and wide-angle enhancement. However, such systems are often opaque, making it difficult to probe photo-induced processes. Here we use wave interference effects to modify the spatial distribution of light inside a highly-scattering dye-sensitized solar cell to control photon absorption in a space-dependent manner. By shaping the incident wavefront of a laser beam, we enhance or suppress photocurrent by increasing or decreasing light concentration on the front side of the mesoporous photoanode where the collection efficiency of photoelectrons is maximal. Enhanced light absorption is achieved by reducing reflection through the open boundary of the photoanode via destructive interference, leading to a factor of two increase in photocurrent. This approach opens the door to probing and manipulating photoelectrochemical processes in specific regions inside nominally opaque media.Comment: 21 pages, 4 figures, in submission. The first two authors contributed equally to this paper, and should be regarded as co-first author

Analysis, Synthesis, and Estimation of Fractal-Rate Stochastic Point Processes

Fractal and fractal-rate stochastic point processes (FSPPs and FRSPPs) provide useful models for describing a broad range of diverse phenomena, including electron transport in amorphous semiconductors, computer-network traffic, and sequences of neuronal action potentials. A particularly useful statistic of these processes is the fractal exponent $\alpha$, which may be estimated for any FSPP or FRSPP by using a variety of statistical methods. Simulated FSPPs and FRSPPs consistently exhibit bias in this fractal exponent, however, rendering the study and analysis of these processes non-trivial. In this paper, we examine the synthesis and estimation of FRSPPs by carrying out a systematic series of simulations for several different types of FRSPP over a range of design values for $\alpha$. The discrepancy between the desired and achieved values of $\alpha$ is shown to arise from finite data size and from the character of the point-process generation mechanism. In the context of point-process simulation, reduction of this discrepancy requires generating data sets with either a large number of points, or with low jitter in the generation of the points. In the context of fractal data analysis, the results presented here suggest caution when interpreting fractal exponents estimated from experimental data sets.Comment: 61 pages latex, 10 PS figures, to appear in Fractals 5, 199

Examining Asphaltene Solubility on Deposition in Model Porous Media

Asphaltenes are known to cause severe flow assurance problems in the near-wellbore region of oil reservoirs. Understanding the mechanism of asphaltene deposition in porous media is of great significance for the development of accurate numerical simulators and effective chemical remediation treatments. Here, we present a study of the dynamics of asphaltene deposition in porous media using microfluidic devices. A model oil containing 5 wt % dissolved asphaltenes was mixed with n-heptane, a known asphaltene precipitant, and flowed through a representative porous media microfluidic chip. Asphaltene deposition was recorded and analyzed as a function of solubility, which was directly correlated to particle size and Péclet number. In particular, pore-scale visualization and velocity profiles, as well as three stages of deposition, were identified and examined to determine the important convection−diffusion effects on deposition

Rubber Impact on 3D Textile Composites

A low velocity impact study of aircraft tire rubber on 3D textile-reinforced composite plates was performed experimentally and numerically. In contrast to regular unidirectional composite laminates, no delaminations occur in such a 3D textile composite. Yarn decohesions, matrix cracks and yarn ruptures have been identified as the major damage mechanisms under impact load. An increase in the number of 3D warp yarns is proposed to improve the impact damage resistance. The characteristic of a rubber impact is the high amount of elastic energy stored in the impactor during impact, which was more than 90% of the initial kinetic energy. This large geometrical deformation of the rubber during impact leads to a less localised loading of the target structure and poses great challenges for the numerical modelling. A hyperelastic Mooney-Rivlin constitutive law was used in Abaqus/Explicit based on a step-by-step validation with static rubber compression tests and low velocity impact tests on aluminium plates. Simulation models of the textile weave were developed on the meso- and macro-scale. The final correlation between impact simulation results on 3D textile-reinforced composite plates and impact test data was promising, highlighting the potential of such numerical simulation tools

Distributed flow optimization and cascading effects in weighted complex networks

We investigate the effect of a specific edge weighting scheme $\sim (k_i k_j)^{\beta}$ on distributed flow efficiency and robustness to cascading failures in scale-free networks. In particular, we analyze a simple, yet fundamental distributed flow model: current flow in random resistor networks. By the tuning of control parameter $\beta$ and by considering two general cases of relative node processing capabilities as well as the effect of bandwidth, we show the dependence of transport efficiency upon the correlations between the topology and weights. By studying the severity of cascades for different control parameter $\beta$, we find that network resilience to cascading overloads and network throughput is optimal for the same value of $\beta$ over the range of node capacities and available bandwidth

The role of input noise in transcriptional regulation

Even under constant external conditions, the expression levels of genes fluctuate. Much emphasis has been placed on the components of this noise that are due to randomness in transcription and translation; here we analyze the role of noise associated with the inputs to transcriptional regulation, the random arrival and binding of transcription factors to their target sites along the genome. This noise sets a fundamental physical limit to the reliability of genetic control, and has clear signatures, but we show that these are easily obscured by experimental limitations and even by conventional methods for plotting the variance vs. mean expression level. We argue that simple, global models of noise dominated by transcription and translation are inconsistent with the embedding of gene expression in a network of regulatory interactions. Analysis of recent experiments on transcriptional control in the early Drosophila embryo shows that these results are quantitatively consistent with the predicted signatures of input noise, and we discuss the experiments needed to test the importance of input noise more generally.Comment: 11 pages, 5 figures minor correction

Democratization in a passive dendritic tree : an analytical investigation

One way to achieve amplification of distal synaptic inputs on a dendritic tree is to scale the amplitude and/or duration of the synaptic conductance with its distance from the soma. This is an example of what is often referred to as “dendritic democracy”. Although well studied experimentally, to date this phenomenon has not been thoroughly explored from a mathematical perspective. In this paper we adopt a passive model of a dendritic tree with distributed excitatory synaptic conductances and analyze a number of key measures of democracy. In particular, via moment methods we derive laws for the transport, from synapse to soma, of strength, characteristic time, and dispersion. These laws lead immediately to synaptic scalings that overcome attenuation with distance. We follow this with a Neumann approximation of Green’s representation that readily produces the synaptic scaling that democratizes the peak somatic voltage response. Results are obtained for both idealized geometries and for the more realistic geometry of a rat CA1 pyramidal cell. For each measure of democratization we produce and contrast the synaptic scaling associated with treating the synapse as either a conductance change or a current injection. We find that our respective scalings agree up to a critical distance from the soma and we reveal how this critical distance decreases with decreasing branch radius

Dynamical principles in neuroscience

Dynamical modeling of neural systems and brain functions has a history of success over the last half century. This includes, for example, the explanation and prediction of some features of neural rhythmic behaviors. Many interesting dynamical models of learning and memory based on physiological experiments have been suggested over the last two decades. Dynamical models even of consciousness now exist. Usually these models and results are based on traditional approaches and paradigms of nonlinear dynamics including dynamical chaos. Neural systems are, however, an unusual subject for nonlinear dynamics for several reasons: (i) Even the simplest neural network, with only a few neurons and synaptic connections, has an enormous number of variables and control parameters. These make neural systems adaptive and flexible, and are critical to their biological function. (ii) In contrast to traditional physical systems described by well-known basic principles, first principles governing the dynamics of neural systems are unknown. (iii) Many different neural systems exhibit similar dynamics despite having different architectures and different levels of complexity. (iv) The network architecture and connection strengths are usually not known in detail and therefore the dynamical analysis must, in some sense, be probabilistic. (v) Since nervous systems are able to organize behavior based on sensory inputs, the dynamical modeling of these systems has to explain the transformation of temporal information into combinatorial or combinatorial-temporal codes, and vice versa, for memory and recognition. In this review these problems are discussed in the context of addressing the stimulating questions: What can neuroscience learn from nonlinear dynamics, and what can nonlinear dynamics learn from neuroscience?This work was supported by NSF Grant No. NSF/EIA-0130708, and Grant No. PHY 0414174; NIH Grant No. 1 R01 NS50945 and Grant No. NS40110; MEC BFI2003-07276, and Fundación BBVA

A novel PKC activating molecule promotes neuroblast differentiation and delivery of newborn neurons in brain injuries

Neural stem cells are activated within neurogenic niches in response to brain injuries. This results in the production of neuroblasts, which unsuccessfully attempt to migrate toward the damaged tissue. Injuries constitute a gliogenic/non-neurogenic niche generated by the presence of anti-neurogenic signals, which impair neuronal differentiation and migration. Kinases of the protein kinase C (PKC) family mediate the release of growth factors that participate in different steps of the neurogenic process, particularly, novel PKC isozymes facilitate the release of the neurogenic growth factor neuregulin. We have demonstrated herein that a plant derived diterpene, (EOF2; CAS number 2230806-06-9), with the capacity to activate PKC facilitates the release of neuregulin 1, and promotes neuroblasts differentiation and survival in cultures of subventricular zone (SVZ) isolated cells in a novel PKC dependent manner. Local infusion of this compound in mechanical cortical injuries induces neuroblast enrichment within the perilesional area, and noninvasive intranasal administration of EOF2 promotes migration of neuroblasts from the SVZ towards the injury, allowing their survival and differentiation into mature neurons, being some of them cholinergic and GABAergic. Our results elucidate the mechanism of EOF2 promoting neurogenesis in injuries and highlight the role of novel PKC isozymes as targets in brain injury regeneration