Ambient Protection of Few‐Layer Black Phosphorus via Sequestration of Reactive Oxygen Species

Few‐layer black phosphorous (BP) has emerged as a promising candidate for next‐generation nanophotonic and nanoelectronic devices. However, rapid ambient degradation of mechanically exfoliated BP poses challenges in its practical deployment in scalable devices. To date, the strategies employed to protect BP have relied upon preventing its exposure to atmospheric conditions. Here, an approach that allows this sensitive material to remain stable without requiring its isolation from the ambient environment is reported. The method draws inspiration from the unique ability of biological systems to avoid photo‐oxidative damage caused by reactive oxygen species. Since BP undergoes similar photo‐oxidative degradation, imidazolium‐based ionic liquids are employed as quenchers of these damaging species on the BP surface. This chemical sequestration strategy allows BP to remain stable for over 13 weeks, while retaining its key electronic characteristics. This study opens opportunities to practically implement BP and other environmentally sensitive 2D materials for electronic applications.     

Multi-objective Reinforcement Learning for Responsive Grids

Grids organize resource sharing, a fundamental requirement of large scientific collaborations. Seamless integration of Grids into everyday use requires responsiveness, which can be provided by elastic Clouds, in the Infrastructure as a Service (IaaS) paradigm. This paper proposes a model-free resource provisioning strategy supporting both requirements. Provisioning is modeled as a continuous action-state space, multi-objective reinforcement learning (RL) problem, under realistic hypotheses; simple utility functions capture the high level goals of users, administrators, and shareholders. The model-free approach falls under the general program of autonomic computing, where the incremental learning of the value function associated with the RL model provides the so-called feedback loop. The RL model includes an approximation of the value function through an Echo State Network. Experimental validation on a real data-set from the EGEE Grid shows that introducing a moderate level of elasticity is critical to ensure a high level of user satisfaction.     

Dynamic state and parameter estimation applied to neuromorphic systems

Neuroscientists often propose detailed computational models to probe the properties of the neural systems they study. With the advent of neuromorphic engineering, there is an increasing number of hardware electronic analogs of biological neural systems being proposed as well. However, for both biological and hardware systems, it is often difficult to estimate the parameters of the model so that they are meaningful to the experimental system under study, especially when these models involve a large number of states and parameters that cannot be simultaneously measured. We have developed a procedure to solve this problem in the context of interacting neural populations using a recently developed dynamic state and parameter estimation (DSPE) technique. This technique uses synchronization as a tool for dynamically coupling experimentally measured data to its corresponding model to determine its parameters and internal state variables. Typically experimental data are obtained from the biological neural system and the model is simulated in software; here we show that this technique is also efficient in validating proposed network models for neuromorphic spike-based very large-scale integration (VLSI) chips and that it is able to systematically extract network parameters such as synaptic weights, time constants, and other variables that are not accessible by direct observation. Our results suggest that this method can become a very useful tool for model-based identification and configuration of neuromorphic multichip VLSI systems.     

Combining algorithms for automatic detection of optic disc and macula in fundus images

This paper proposes an efficient combination of algorithms for the automated localization of the optic disc and macula in retinal fundus images. There is in fact no reason to assume that a single algorithm would be optimal. An ensemble of algorithms based on different principles can be more accurate than any of its individual members if the individual algorithms are doing better than random guessing. We aim to obtain an improved optic disc and macula detector by combining the prediction of multiple algorithms, benefiting from their strength and compensating their weaknesses. The location with maximum number of detectors’ outputs is formally the hotspot and is used to find the optic disc or macula center. An assessment of the performance of integrated system and detectors working separately is also presented. Our proposed combination of detectors achieved overall highest performance in detecting optic disc and fovea closest to the manually center chosen by the retinal specialist.     

Analysis of an injection/compression liquid composite molding process

The injection/compression liquid composite molding (LCM) process is simulated by using the control/volume finite element method (CV/FEM). The flow in the runner and the fiber-free areas is simplified by using an equivalent permeability approach. Several molding experiments were conducted using a tub-shaped mold and the structural reaction injection molding (SRIM) process for a poly(urethane/isocyanurate) matrix and a glass fiber preform. Good agreement is found between the experimental results and the simulation.     

Forming transmembrane channels using end-functionalized nanotubes

Using dissipative particle dynamics (DPD) simulations, we examine the interaction between amphiphilic nanotubes and lipid bilayer membranes. The nanotubes are represented by a hydrophobic shaft that is end-functionalized with hydrophilic groups. Nanotubes that are capped by a monolayer of hydrophilic beads or also encompass hydrophilic "hairs" on just one end of the shaft are found to spontaneously penetrate and assume a transmembrane position; the process, however, depends critically on the membrane tension. On the other hand, nanotubes that include hydrophilic hairs at both ends of the hydrophobic shaft are not observed to spontaneously self-organize into the bilayer. When the membrane is stretched to form a pore, the nanotubes with two hairy ends adsorb on the edge of the pore and become localized in the membrane, thus forming a transmembrane channel. The findings from these studies provide guidelines for creating biomimetic nanotube channels that are capable of selectively transporting molecules through the membrane in response to changes in the local environment.     

Computer simulation of polypropylene/organoclay nanocomposites: characterization of atomic scale structure and prediction of binding energy

Molecular simulation techniques are used to explore and characterize the atomic scale structure, and to predict binding energies and basal spacing of polymer/clay nanocomposites based on polypropylene (PP) and maleated polypropylene (PPMA), montmorillonite (MMT), and different alkylammonium ions (quats) as surfactants. Our evidences suggest that shorter hydrocarbonic chains are more effective in producing favorable binding energies with respect to longer ones, and the substitutions of hydrogen atoms with polar groups on the quaternary ammonium salt (quat) generally results in greater interaction between quat and both polymer and clay. Under the hypothesis, that montmorillonite platelets are uniformly dispersed in a polymer matrix, the modified polypropylene yields higher interfacial strength with clay than neat polypropylene. The use of neat PP and quats with higher molecular volume offer the higher values of the basal spacing and thus, in principle, they should be more effective in the exfoliation process.