Immobilization of monolayer protected lipophilic gold nanorods on a glass surface

We present a novel process of immobilization of gold nanorods (GNRs) on a glass surface. We demonstrate that by exploiting monolayer protection of the GNRs, their unusual optical properties can be completely preserved. UV-visible spectroscopy and atomic force microscopy analysis are used to reveal the optical and morphological properties of monolayer protected immobilized lipophilic GNRs, and molecular dynamics simulations are used to elucidate their surface molecule arrangements.     

Real-time single-molecule imaging of quantum interference

The observation of interference patterns in double-slit experiments with massive particles is generally regarded as the ultimate demonstration of the quantum nature of these objects. Such matter-wave interference has been observed for electrons, neutrons, atoms and molecules and, in contrast to classical physics, quantum interference can be observed when single particles arrive at the detector one by one. The build-up of such patterns in experiments with electrons has been described as the "most beautiful experiment in physics". Here, we show how a combination of nanofabrication and nano-imaging allows us to record the full two-dimensional build-up of quantum interference patterns in real time for phthalocyanine molecules and for derivatives of phthalocyanine molecules, which have masses of 514?AMU and 1,298?AMU respectively. A laser-controlled micro-evaporation source was used to produce a beam of molecules with the required intensity and coherence, and the gratings were machined in 10-nm-thick silicon nitride membranes to reduce the effect of van der Waals forces. Wide-field fluorescence microscopy detected the position of each molecule with an accuracy of 10?nm and revealed the build-up of a deterministic ensemble interference pattern from single molecules that arrived stochastically at the detector. In addition to providing this particularly clear demonstration of wave-particle duality, our approach could also be used to study larger molecules and explore the boundary between quantum and classical physics.     

Advances in Polyimide‐Based Materials for Space Applications

The space environment raises many challenges for new materials development and ground characterization. These environmental hazards in space include solar radiation, energetic particles, vacuum, micrometeoroids and debris, and space plasma. In low Earth orbits, there is also a significant concentration of highly reactive atomic oxygen (AO). This Progress Report focuses on the development of space‐durable polyimide (PI)‐based materials and nanocomposites and their testing under simulated space environment. Commercial PIs suffer from AO‐induced erosion and surface electric charging. Modified PIs and PI‐based nanocomposites are developed and tested to resist degradation in space. The durability of PIs in AO is successfully increased by addition of polyhedral oligomeric silsesquioxane. Conductive materials are prepared based on composites of PI and either carbon nanotube (CNT) sheets or 3D‐graphene structures. 3D PI structures, which can expand PI space applications, made by either additive manufacturing (AM) or thermoforming, are presented. The selection of AM‐processable engineering polymers in general, and PIs in particular, is relatively limited. Here, innovative preliminary results of a PI‐based material processed by the PolyJet technology are presented.     

Computing discrete shape operators on general meshes

Discrete curvature and shape operators, which capture complete information about directional curvatures at a point, are essential in a variety of applications: simulation of deformable two‐dimensional objects, variational modeling and geometric data processing. In many of these applications, objects are represented by meshes. Currently, a spectrum of approaches for formulating curvature operators for meshes exists, ranging from highly accurate but computationally expensive methods used in engineering applications to efficient but less accurate techniques popular in simulation for computer graphics. We propose a simple and efficient formulation for the shape operator for variational problems on general meshes, using degrees of freedom associated with normals. On the one hand, it is similar in its simplicity to some of the discrete curvature operators commonly used in graphics; on the other hand, it passes a number of important convergence tests and produces consistent results for different types of meshes and mesh refinement.     

Modeling Hydrogen Sulfide Emission Rates in Gravity Sewage Collection Systems

The factors affecting sulfide buildup in gravity sewers are complex, consisting of biological and physical processes, both in the aqueous and the gas phases of the sewer. The rate of each of these processes varies (among other parameters) according to flow characteristics, temperature, and pH. Under fast and turbulent flow conditions, the stripping of hydrogen sulfide into the gas phase may become the dominant process. The paper presents a semiempirical approach to the problem of quantifying hydrogen sulfide emission rates in sewers. Kinetics of hydrogen sulfide emission as a function of hydraulic parameters was measured in the laboratory using methods adopted from flocculation theory. A flocculation unit was used to impart a selected velocity gradient (G) into the water, and sulfide concentration was measured with time. The process was repeated for a number of G values. Regression analysis was then used to fit the rate of hydrogen sulfide emission equation against G. An equation was developed linking G to HL (head loss) in sewers assuming plug flow conditions. The hydraulic model and the kinetic model were linked (via G) to give the desired rate equation for hydrogen sulfide emission along a sewer line. The model was used to predict H2S emission from a uniform flow sewer and the effect of parameters such as pH, sewer slope and degree of fullness was studied. As expected, results show that low pH, high slope, and low degree of fullness enhance emission rates. Reasonable agreement was attained when the model output was compared with measured results from a field test sewer in Virginia, South Africa, under conditions where sulfide stripping was the rate-dominant process.     

A framework for robust active super tier systems

The scenario in which services are obtained by combining sub services is of a great importance. Reliability and fault tolerance are important concerns in the design of such distributed systems. This paper proposes a framework for designing robust client–server tier systems. First, we suggest implementing robust tier automata for each tier τ _i used by the processors that the tier contains. The robust tier automata are useful abstractions that allow distribution of the tier’s tasks (and its stubs) in a way that ensures availability, consistency, and recovery in the presence of faults. Then we suggest an active tier framework in which the computation of a user request progresses on several paths in the tier system; those paths may intersect, leaving footprints for each other. And last, we propose the super tier architecture, which correlates equivalent tiers in order to gain more robustness and load balancing. We present and analyze optimistic, pessimistic, and semi-optimistic approaches for computing over the active tier system with super tiers.     

A Bayesian regression model for multivariate functional data

In this paper we present a model for the analysis of multivariate functional data with unequally spaced observation times that may differ among subjects. Our method is formulated as a Bayesian mixed-effects model in which the fixed part corresponds to the mean functions, and the random part corresponds to individual deviations from these mean functions. Covariates can be incorporated into both the fixed and the random effects. The random error term of the model is assumed to follow a multivariate Ornstein–Uhlenbeck process. For each of the response variables, both the mean and the subject-specific deviations are estimated via low-rank cubic splines using radial basis functions. Inference is performed via Markov chain Monte Carlo methods.     

Modeling the Aeration Efficiency of a Passively Aerated Vertical-Flow Biological Filter

A model aimed at calculating the aeration efficiency of a passively aerated biological vertical bed unit is presented. The model allows the calculation of the mass of air that would flow via convection into the vertical bed, therefore enabling the prediction of the maximal capacity of the bed as an aerobic biological reactor. Aeration efficiency, defined as the volume of air that would enter the bottom of the bed as a function of the volume of water that is drained from it, is predicted in the model as a function of the mean particle size of the gravel media, and the diameter and number of the aeration tubes installed. The model was calibrated in the laboratory and verified using results from a pilot scale vertical bed treating secondary municipal wastewater effluents. The principal model equation is: EPAVB = NPDp4/(NPDp4+0.285pDr22), where EPAFB=efficiency of the passive air pump (—); NP=number of aeration pipes (—); Dp=aeration pipes’ diameter (m); p=medium porosity (—); and Dr=vertical bed diameter (m).     

Reducing error in neural network time series forecasting

Neural network time series forecasting error comprises autocorrelation error, due to an imperfect model, and random noise, inherent in the data. Both problems are addressed here, the first using a two stage training, growth-network neuron: the autocorrelation error (ACE) neuron. The second is considered as a post-processing noise filtering problem. These techniques are applied in forecasting the sunspot time series, with comparison of stochastic, BFGS and conjugate gradient solvers.