Reduced graphene oxide-ZnO hybrid composites as photocatalysts: The role of nature of the molecular target in catalytic performance

Spurred by controversial literature findings, we enwrapped reduced graphene oxide (rGO) in ZnO hierarchical microstructures (rGO loadings spanning from 0.01 to 2 wt%) using an in situ synthetic procedure. The obtained hybrid composites were carefully characterized, aiming at shining light on the possible role of rGO on the claimed increased performance as photocatalysts. Several characterization tools were exploited to unveil the effect exerted by rGO, including steady state and time resolved photoluminescence, electron microscopies and electrochemical techniques, in order to evaluate the physical, optical and electrical features involved in determining the catalytic degradation of rhodamine B and phenol in water.Several properties of native ZnO structures were found changed upon the rGO enwrapping (including optical absorbance, concentration of native defects in the ZnO matrix and double-layer capacitance), which are all involved in determining the photocatalytic performance of the hybrid composites. The findings discussed in the present work highlight the high complexity of the field of application of graphene-derivatives as supporters of semiconducting metal oxides functionality, which has to be analyzed through a multi-parametric approach.     

Extended vertical lists for temporal pattern mining from multivariate time series

In this paper, the problem of mining complex temporal patterns in the context of multivariate time series is considered. A new method called the Fast Temporal Pattern Mining with Extended Vertical Lists is introduced. The method is based on an extension of the level‐wise property, which requires a more complex pattern to start at positions within a record where all of the subpatterns of the pattern start. The approach is built around a novel data structure called the Extended Vertical List that tracks positions of the first state of the pattern inside records and links them to appropriate positions of a specific subpattern of the pattern called the prefix. Extensive computational results indicate that the new method performs significantly faster than the previous version of the algorithm for Temporal Pattern Mining; however, the increase in speed comes at the expense of increased memory usage.     

Advanced Postirradiation Characterization of Nuclear Fuels Using Pulsed Neutrons

Methods for postirradiation characterization of bulk (cm³) irradiated materials or even spent nuclear fuels are sparse due to their extremely radioactive nature. While several methods exist to characterize smaller volumes (<?1 mm³) of such samples, selecting these volumes from larger samples is challenging. X-ray-based methods are prohibitive due to the strong γ-radiation from the sample flooding the detectors. Neutron-based methods available in the proximity of irradiation reactors allow for thermal neutron radiography or computed tomography using a small reactor source, but one cannot assess isotope distributions or microstructural features such as phases, texture, or strain from diffraction measurements due to flux limitations. We present herein a pathway to provide pulsed neutron characterization of bulk irradiated samples using time-of-flight neutron diffraction for microstructural characterization and energy-resolved neutron imaging for assessment of isotopic densities and distributions. Ultimately, laser-driven pulsed neutron sources may allow deployment of these techniques pool-side at irradiation reactors.

     

Effect of Mn doping on hydrolysis of low-temperature synthesized metastable alpha-tricalcium phosphate

In the present work, effect of Mn doping on hydrolysis rate of low-temperature synthesized metastable α-tricalcium phosphate (α-TCP) was investigated. α-TCP powders containing different amount of Mn²⁺ ions (0, 0.5 and 1 mol%) were synthesized by wet co-precipitation process, followed by annealing and crystallization of as-precipitated amorphous calcium phosphate at 700 °C. It was demonstrated that the presence of Mn²⁺ ions significantly retards hydrolysis rate of α-TCP. While pristine α-TCP fully hydrolyzed with a conversion to calcium-deficient hydroxyapatite in 10 h, complete hydrolysis of α-TCP doped with 0.5 and 1 mol% of Mn occurred only after 20 and 35 h, respectively. Initial and final products were characterized by X-ray diffraction (XRD) analysis, infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). Chemical composition of starting and fully hydrolyzed α-TCP powders was determined by inductively coupled plasma optical emission spectrometry (ICP-OES).     

Multi-product continuous plant scheduling: combination of decomposition,genetic algorithm,and constructive heuristic

We propose a polylithic method for medium-term scheduling of a large-scale industrial plant operating in a continuous mode. The method combines a decomposition approach, a genetic algorithm (GA) and a constructive MILP-based heuristic. In the decomposition, decisions are made at two levels, using the rolling horizon approach. At the upper level, a reduced set of products and the time period is chosen to be considered in the lower level. At the lower level, a short-term scheduling MILP-model with event-based representation is used. A heuristic solution to the lower level problem is found using a constructive Moving Window heuristic guided by a genetic algorithm. The GA is applied for finding efficient utilisation of critical units in the lower level problem. For solving the one unit scheduling problem, a parallel dynamic programming algorithm is proposed. Implementation of the dynamic programming algorithm for a graphics processing unit (GPU) is incorporated in the GA for improving its performance. The experimental study of the proposed method on a real case of a large-scale plant shows a significant improvement of the solution quality and the solving time comparing to the pure decomposition algorithm proposed in the earlier study, and confirmed suitability of the proposed approach for the real-life production scheduling. In particular, the reduction of the number of changeovers and their duration in the obtained solution as well as the CPU time of solving the problem was about 60% using the new approach.     

Robust method to provide exponential convergence of model parameters solving linear time-invariant plant identification problem

The scope of this research is a problem of parameters identification of a linear time-invariant plant, which (1) input signal is not frequency-rich, (2) is subjected to initial conditions and external disturbances. The memory regressor extension (MRE) scheme, in which a specially derived differential equation is used as a filter, is applied to solve the above-stated problem. Such a filter allows us to obtain a bounded regressor value, for which a condition of the initial excitation (IE) is met. Using the MRE scheme, the recursive least-squares method with the forgetting factor is used to derive an adaptation law. The following properties have been proved for the proposed approach. If the IE condition is met, then: (1) the parameter error of identification is bounded and converges to zero exponentially (if there are no external disturbances) or to a set (in the case of them) with an adjustable rate, (2) the parameters adaptation rate is a finite value. The above-mentioned properties are mathematically proved and demonstrated via simulation experiments.