Small-angle neutron scattering characterization of the structure of nanoporous carbons for energy-related applications

We used small-angle neutron scattering (SANS) and neutron contrast variation to study the structure of four nanoporous carbons prepared by thermo-chemical etching of titanium carbide TiC in chlorine at 300, 400, 600, and 800 °C with pore diameters ranging between ∼4 and ∼11 Å. SANS patterns were obtained from dry samples and samples saturated with deuterium oxide (D₂O) in order to delineate origin of the power law scattering in the low Q domain as well as to evaluate pore accessibility for D₂O molecules. SANS cross section of all samples was fitted to Debye-Anderson-Brumberger (DAB), DAB-Kirste-Porod models as well as to the Guinier and modified Guinier formulae for cylindrical objects, which allowed for evaluating the radii of gyration as well as the radii and lengths of the pores under cylindrical shape approximation. SANS data from D₂O-saturated samples indicate that strong upturn in the low Q limit usually observed in the scattering patterns from microporous carbon powders is due to the scattering from outer surface of the powder particles. Micropores are only partially filled with D₂O molecules due to geometrical constraints and or partial hydrophobicity of the carbon matrix. Structural parameters of the dry carbons obtained using SANS are compared with the results of the gas sorption measurements and the values agree for carbide-derived carbons (CDCs) obtained at high chlorination temperatures (>600 °C). For lower chlorination temperatures, pore radii obtained from gas sorption overestimate the actual pore size as calculated from SANS for two reasons: inaccessible small pores are present and the model-dependent fitting based on density functional theory models assumes non-spherical pores, whereas SANS clearly indicates that the pore shape in microporous CDC obtained at low chlorination temperatures is nearly spherical.     

The adoption of artificial intelligence and robotics in the hotel industry: prospects and challenges

Electronic Markets - The growth of technology has resulted in the use of state-of-the-art systems such as artificial intelligence (AI) and robot-based applications and services in the hotel...     

Highly collective atomic transport mechanism in high-entropy glass-forming metallic liquids

Quasielastic neutron scattering(QENS) has been used to study the atomic relaxation process and microscopic transport mechanism in high-entropy glass-forming metallic(HE-GFM) liquids. Self-intermediate scattering functions obtained from the QENS data show unusually large stretching, which indicates highly heterogeneous atomic dynamics in HE-GFM liquids. In these liquids, a group of atoms over a length scale of about 21 ? diffuses collectively even well above the melting temperature. However, the temperature dependence of diffusion process in one of the HE-GFM liquid is Arrhenius, but in the other HE-GFM liquid it is non-Arrhenius. Although the glass-forming ability of these HE-GFM liquids is very poor, the diffusion coefficients obtained from the QENS data indicate the long range atomic transport process is much slower than that of the best metallic glass-forming liquids at their melting temperatures.     

Innovative and intensified technology for the biological pretreatment of agro waste for ethanol production

Lignocellulosic biomass is an abundant, renewable resource, but the structural and chemical complexity of biomass acts as a hindrance in its effective utilization for cellulosic ethanol production. Hence, effective pretreatment is always necessary to remove the surrounding matrix of lignin prior to the enzymatic hydrolysis. Pretreatment of rice straw by Pleurotus florida was found to be effective and resulted in 49% lignin degradation, whereas fungus along with grape leaves resulted in 99% lignin degradation. This method not only explores a pathway for utilizing the solid agro waste but also results in a value-added product of edible mushrooms that has proved to be the best pretreatment technology for ethanol production. FTIR and SEM analysis confirmed the structural transformation taking place during the pretreatment. The components of grape leaves were also analyzed using GC-MS.     

Incorporating rotational invariance in convolutional neural network architecture

Pattern Analysis and Applications - Convolutional neural networks (CNNs) are one of the deep learning architectures capable of learning complex set of nonlinear features useful for effectively...     

Fractional-order Proportional-integral-derivative-based Automatic Generation Control in Deregulated Power Systems

Abstract—In this article, implementation of a fractional-order proportional-integral-derivative controller is proposed as supplementary automatic generation control of interconnected multi-area deregulated power systems that operates under the effects of bilateral contracts on the dynamics. The tuning of the fractional-order proportional-integral-derivative controller parameters is formulated as an optimization problem and solved by employing a genetic algorithm. The traditional automatic generation control loop incorporated with the concept of the distribution company participation matrix and fractional-order proportional-integral-derivative supplementary controller is simulated for different operating cases; a comparison among the conventional integral controller, hybrid fuzzy proportional-integral controller, genetic algorithm-tuned proportional-integral controller, and genetic algorithm-tuned proportional-integral-derivative controllers is presented. The simulation results show that the system employing a genetic algorithm-tuned fractional-order proportional-integral-derivative controller has better performance than the integer-order hybrid fuzzy proportional-integral, genetic algorithm-tuned proportional-integral, and genetic algorithm-tuned proportional-integral-derivative controllers in terms of settling time and overshoot.