Hydrolysis rate of submicron Zn particles for solar H2 synthesis

The hydrolysis rate of Zn particles by up to 50 mol% water vapor in Ar gas was measured by thermogravimetric analysis at atmospheric pressure and 330–360 °C and quantified by a core-shell model. An initial ZnO layer led to an initially linear conversion profile attributed to a fast surface reaction (half-order with respect to water vapor mole fraction, y) followed by a parabolic conversion profile independent of y but dependent on Zn ion diffusion through a ZnO layer. The latter is most important for solar H₂ formation by the Zn/ZnO water-splitting cycle as it determines the required process residence time for Zn hydrolysis. A ready-to-use equation for calculation of ZnO and H₂ formation during Zn hydrolysis is proposed and compared to literature data revealing enhanced hydrolysis rates for submicron Zn particles.     

Adaptive lighting controllers using smart sensors

The aim of this paper is to present an advanced controller for artificial lights evaluated in several rooms in two European Hospitals located in Chania, Greece and Ancona, Italy. Fuzzy techniques have been used for the architecture of the controller. The energy efficiency of the controllers has been calculated by running the controller coupled with validated models of the RADIANCE back-wards ray tracing software. The input of the controller is the difference between the current illuminance value and the desired one, while the output is the change of the light level that should be applied in the artificial lights. Simulation results indicate significant energy saving potentials. Energy saving potential is calculated from the comparison of the current use of the artificial lights by the users and the proposed one. All simulation work has been conducted using Matlab and RADIANCE environment.     

Aggregated mobility-based topology inference for fast sensor data collection

We investigate the problem of efficient data collection in wireless sensor networks where both the sensors and the sink move. We especially study the important, realistic case where the spatial distribution of sensors is non-uniform and their mobility is diverse and dynamic. The basic idea of our protocol is for the sink to benefit of the local information that sensors spread in the network as they move, in order to extract current local conditions and accordingly adjust its trajectory. Thus, sensory motion anyway present in the network serves as a low cost replacement of network information propagation. In particular, we investigate two variations of our method: a) the greedy motion of the sink towards the region of highest density each time and b) taking into account the aggregate density in wider network regions. An extensive comparative evaluation to relevant data collection methods (both randomized and optimized deterministic), demonstrates that our approach achieves significant performance gains, especially in non-uniform placements (but also in uniform ones). In fact, the greedy version of our approach is more suitable in networks where the concentration regions appear in a spatially balanced manner, while the aggregate scheme is more appropriate in networks where the concentration areas are geographically correlated. We also investigate the case of multiple sinks by suggesting appropriate distributed coordination methods.     

DNA replication in the fission yeast: robustness in the face of uncertainty

DNA replication, the process of duplication of a cell's genetic content, must be carried out with great precision every time the cell divides, so that genetic information is preserved. Control mechanisms must ensure that every base of the genome is replicated within the allocated time (S-phase) and only once per cell cycle, thereby safeguarding genomic integrity. In eukaryotes, replication starts from many points along the chromosome, termed origins of replication, and then proceeds continuously bidirectionally until an opposing moving fork is encountered. In contrast to bacteria, where a specific site on the genome serves as an origin in every cell division, in most eukaryotes origin selection appears highly stochastic: many potential origins exist, of which only a subset is selected to fire in any given cell, giving rise to an apparently random distribution of initiation events across the genome. Origin states change throughout the cell cycle, through the ordered formation and modification of origin-associated multisubunit protein complexes. State transitions are governed by fluctuations of cyclin-dependent kinase (CDK) activity and guards in these transitions ensure system memory. We present here DNA replication dynamics, emphasizing recent data from the fission yeast Schizosaccharomyces pombe, and discuss how robustness may be ensured in spite of (or even assisted by) system randomness.     

Semantic-based taxonomy for immersive product design using VR techniques

This work focuses on the design of products related to the aircraft industry. It combines the functionalities and features of an immersive simulation environment, for product design and review, with the existing knowledge about such products and their components. Effort is put in augmenting simulation and design knowledge on virtual geometries, through XML syntax. An immersive environment is developed, which enables the user to design and review a virtual product through highly usable interfaces coupled with product semantics. The semantics are based on a taxonomy defined by certain characteristics/properties of the geometrical objects and the environment. The concept and implementation is tested in an aircraft cabin design use-case.     

Crystallinity dynamics of gold nanoparticles during sintering or coalescence

The crystallinity of gold nanoparticles during coalescence or sintering is investigated by molecular dynamics. The method is validated by the attainment of the Au melting temperature that increases with increasing particle size approaching the Au melting point. The morphology and crystal dynamics of nanoparticles of (un)equal size during sintering are elucidated. The characteristic sintering time of particle pairs is determined by tracing their surface area evolution during coalescence. The crystallinity is quantified by the disorder variable indicating the system's degree of disorder. The atoms at the grain boundaries are amorphous, especially during particle adhesion and during sintering when grains of different orientation are formed. Initial grain orientation affects final particle morphology leading to exposure of different crystal surfaces that can affect the performance of Au nanoparticles (e.g., catalytic efficiency). Coalescence between crystalline and amorphous nanoparticles of different size results in polycrystalline particles of increasing crystallinity with time and temperature. Crystallinity affects the sintering rate and mechanism. Such simulations of free‐standing Au nanoparticle coalescence are relevant also to Au nanoparticles on supports that do not exhibit strong affinity or strong metal support interactions. © 2015 American Institute of Chemical Engineers AIChE J, 62: 589–598, 2016     

Optimal cross sections of filament-wound toroidal hydrogen storage vessels based on continuum lamination theory

One of the most important issues for the design of toroidal hydrogen storage vessels reflects on the determination of the most efficient meridional cross sections. In this paper we outline the cross-sectional shape determination for filament-wound toroidal pressure vessels based on the continuum theory and the optimality condition of equal shell strains. With the aid of the geodesic law and the equal-stains condition, the continuum-based optimal cross sections are determined while taking into account the shell thickness build-up along the meridional direction. As an additional option, the influence of the theoretically required axial load on the resulting meridian profile is also evaluated and the results show that the meridian curve returns to zero altitude at a certain magnitude of that axial load and thus forms a closed dome. The cross-sectional shapes and structural performance of classical pressure vessels, circular and continuum-based optimal toroidal pressure vessels are respectively determined and compared to each other. The results reveal that toroidal hydrogen storage vessels designed using the optimal cross sections provide better performance than the circular toroidal vessels and the classical vessels.     

Low‐lying energy isomers and global minima of aqueous nanoclusters: Structures and spectroscopic features of the pentagonal dodecahedron (H2O)20 and (H3O)+(H2O)20

We rely on a hierarchical approach to identify the low‐lying isomers and corresponding global minima of the pentagonal dodecahedron (H₂O)₂₀ and the H₃O⁺(H₂O)₂₀ nanoclusters. Initial screening of the isomers is performed using classical interaction potentials, namely the Transferable Interaction 4‐site Potential (TIP4P), the Thole‐Type Flexible Model, versions 2.0 (TTM2‐F) and 2.1 (TTM2.1‐F) for (H₂O)₂₀ and the Anisotropic Site Potential (ASP) for H₃O⁺(H₂O)₂₀. The nano‐networks obtained with those potentials were subsequently refined at the density functional theory (DFT) with the Becke‐3‐parameter Lee–Yang–Parr (B3LYP) functional and at the second order Møller–Plesset perturbation (MP2) levels of theory. For the pentagonal dodecahedron (H₂O)₂₀ it was found that DFT (B3LYP) and MP2 produced the same global minimum. However, this was not the case for the H₃O⁺(H₂O)₂₀ cluster, for which MP2 produced a different network for the global minimum when compared to DFT (B3LYP). The low‐lying networks of H₃O⁺(H₂O)₂₀ correspond to structures having 9 ‘free’ OH bonds and the hydronium ion on the surface of the nanocluster. The IR spectra of the various networks are further analysed in the OH stretching (‘fingerprint’) region and the various bands are assigned to structural arrangements of the underlying hydrogen bonding network. © 2012 Canadian Society for Chemical Engineering     

Methods for interpreting the out‐of‐control signal of multivariate control charts: A comparison study

Multivariate control charts have proved to be a useful tool for identifying an out‐of‐control process. However, one of the main drawbacks of these charts is that they do not indicate which measured variables have been shifted. To overcome this issue, several alternative approaches that aim to diagnose faults the responsible variable(s) for the out‐of‐control signal and help identify aberrant variables may be found in the literature. This paper reviews several techniques that are used to diagnose the responsible variable(s) for the out‐of‐control signal and attempt to make a comparative study among them. In particular, we evaluate the performance of each method under different simulation scenarios in terms of successful identification of the out‐of‐control variables. Special attention has also been given to the computational approaches and especially in the ability of artificial neural networks to identify out‐of‐control signals. The obtained results show that: there is no particular method that can be considered as panacea; the artificial neural networks' performance depends heavily on the training data set.