Analysis of Optical Losses in a Photoelectrochemical Cell: A Tool for Precise Absorptance Estimation

Optical losses in a photoelectrochemical (PEC) cell account for a substantial part of solar‐to‐hydrogen conversion losses, but limited attention is paid to the detailed investigation of optical losses in PEC cells. In this work, an optical model of combined coherent and incoherent light propagation in all layers of the PEC cell based on spectroscopic measurements is presented. Specifically, photoelectrodes using transparent conductive substrates such as F:SnO₂ coated with thin absorber films are focused. The optical model is verified for hematite photoanodes fabricated by atomic layer deposition and successfully used to determine wavelength‐dependent reflection, transmission, layer absorptances, and charge generation rates. Furthermore, the calculated absorptances enable 20–30% more accurate calculations of the absorbed photon‐to‐current efficiency of PEC cells. Our optical model is a powerful tool for the optimization of the optical performance of PEC cells focusing on single absorber or tandem configurations and represents a cornerstone of a complete (optical and electrical) model for PEC water splitting cells.     

Self‐Assembled Conjugated Thiophene‐Based Rotaxane Architectures: Structural,Computational, and Spectroscopic Insights into Molecular Aggregation

A comparative study of the self‐assembly at a variety of surfaces of a dithiophene rotaxane 1 ?β‐CD and its corresponding dumbbell, 1, by means of atomic force microscopy (AFM) imaging and scanning tunneling microscopy (STM) imaging on the micrometer and nanometer scale, respectively. The dumbbell is found to have a greater propensity to form ordered supramolecular assemblies, as a result of π–π interactions between dithiophenes belonging to adjacent molecules, which are hindered in the rotaxane. The fine molecular structure determined by STM was compared to that obtained by molecular modelling. The optical properties of both rotaxane and dumbbell in the solid state were investigated by steady‐state and time‐resolved photoluminescence (PL) experiments on spin‐cast films and diluted solutions. The comparison between the optical features of the threaded and unthreaded systems reveals an effective role of encapsulation in reducing aggregation and exciton migration for the rotaxanes with respect to the dumbbells, thus leading to higher PL quantum efficiency and preserved single‐molecule photophysics for longer times after excitation in the threaded oligomers.     

A Versatile Identification Method Applied to Analysis of Respiratory Mechanics

A method for the straightforward estimation of the param eters in lumped continuous linear models of respiratory mechanics is presented in this paper. First, a discrete-time model is obtained from the samples of input-output data and then transformed into a conuous-time model.     

Understanding and Predicting Data Hotspots in Cellular Networks

The unprecedented growth in mobile data usage is posing significant challenges to cellular operators. One key challenge is how to provide quality of service to subscribers when their residing cell is experiencing a significant amount of traffic, i.e. becoming a traffic hotspot. In this paper, we perform an empirical study on data hotspots in today’s cellular networks using a 9-week cellular dataset with 734K+ users and 5327 cell sites. Our analysis examines in details static and dynamic characteristics, predictability, and causes of data hotspots, and their correlation with call hotspots. We show that using standard machine learning methods, future hotspots can be accurately predicted from past observations. We believe the understanding of these key issues will lead to more efficient and responsive resource management and thus better QoS provision in cellular networks. To the best of our knowledge, our work is the first to empirically characterize traffic hotspots in today’s cellular networks.     

Advanced receiver design for satellite‐based automatic identification system signal detection

This paper describes an innovative receiver architecture for the satellite‐based automatic identification system. The receiver performance has been fully validated in the presence of the typical satellite channel characteristics. In particular, it is shown that the devised receiver provides an excellent performance against the noise, as well as a large resilience against message collisions, Doppler shift, and delay spread. Copyright © 2012 John Wiley & Sons, Ltd.     

Hierarchically Structured Magnetic Nanoconstructs with Enhanced Relaxivity and Cooperative Tumor Accumulation

Iron oxide nanoparticles are formidable multifunctional systems capable of contrast enhancement in magnetic resonance imaging, guidance under remote fields, heat generation, and biodegradation. Yet, this potential is underutilized in that each function manifests at different nanoparticle sizes. Here, sub‐micrometer discoidal magnetic nanoconstructs are realized by confining 5 nm ultra‐small super‐paramagnetic iron oxide nanoparticles (USPIOs) within two different mesoporous structures, made out of silicon and polymers. These nanoconstructs exhibit transversal relaxivities up to ≈10 times (r ₂ ≈ 835 mm ^?1 s^?1) higher than conventional USPIOs and, under external magnetic fields, collectively cooperate to amplify tumor accumulation. The boost in r ₂ relaxivity arises from the formation of mesoscopic USPIO clusters within the porous matrix, inducing a local reduction in water molecule mobility as demonstrated via molecular dynamics simulations. The cooperative accumulation under static magnetic field derives from the large amount of iron that can be loaded per nanoconstuct (up to ≈65 fg) and the consequential generation of significant inter‐particle magnetic dipole interactions. In tumor bearing mice, the silicon‐based nanoconstructs provide MRI contrast enhancement at much smaller doses of iron (≈0.5 mg of Fe kg^?1 animal) as compared to current practice.     

High static gain single-phase PFC based on a hybrid boost converter

In this paper, a single-phase unity power factor rectifier, based on a hybrid boost converter, resulting from the integration of a conventional dc–dc boost converter and a switched-capacitor voltage doubler is proposed, analysed, designed and tested. The high-power rectifier is controlled by two feedback loops with the same control strategy employed in the conventional boost-based rectifier. The main feature of the proposed rectifier is its ability to output a dc voltage larger than the double of the peak value of the input line voltage, while subjecting the power switches to half of the dc-link voltage, which contributes to reducing the cost and increasing the efficiency. Experimental data were obtained from a laboratory prototype with an input voltage of 220 Vrms, line frequency of 60 Hz, output voltage of 800 Vdc, load power of 1000 W and switching frequency of 50 kHz. The efficiency of the prototype, measured in the laboratory, was 96.5% for full load and 97% for half load.     

Toward automatic phenotyping of developing embryos from videos.

We describe a trainable system for analyzing videos of developing C. elegans embryos. The system automatically detects, segments, and locates cells and nuclei in microscopic images. The system was designed as the central component of a fully automated phenotyping system. The system contains three modules 1) a convolutional network trained to classify each pixel into five categories: cell wall, cytoplasm, nucleus membrane, nucleus, outside medium; 2) an energy-based model, which cleans up the output of the convolutional network by learning local consistency constraints that must be satisfied by label images; 3) a set of elastic models of the embryo at various stages of development that are matched to the label images.     

On the intersubject generalization ability in extracting kinematic information from afferent nervous signals

In the recent past, many efforts have been carried out in order to evaluate the feasibility of implementing closed-loop controlled neuroprostheses based on the processing of sensory electroneurographic (ENG) signals. The success of these techniques mostly relies on the development of processing algorithms capable of extracting the necessary kinematic information from these signals. Soft-computing algorithms can be very useful when dealing with the complexity of the neuromuscular system because of their generalization ability and model-free structure. In this paper, these techniques were used to extract angular position information from the ENG signals recorded from muscle afferents in animal model using cuff electrodes. Specifically, a genetic algorithm-based dynamic nonsingleton fuzzy logic system (named GA-DNSFLS) was developed and tested on different types of angular trajectories (characterized by small or large angular excursions). In particular, two different Takagi-Sugeno-Kang (TSK)-like structures were used in the consequent part of the neuro-fuzzy model in order to verify which one could improve the generalization abilities (intrasubject and intersubject). The results showed that the GA-DNSFLS was able to reconstruct the trajectories giving interesting results in terms of correlation between the actual and the predicted trajectories for small excursion movements during intrasubject and intersubject tests. Particularly, one of the TSK models showed better results in terms of intersubject generalization. The simulations conducted with the large excursion movements led in some cases to interesting results but further experiments are necessary in order to analyze this point more in deep.