Atomistic tight-binding computations of the electronic properties of ZnSe/ZnS core/shell nanocrystals under applied electric field

The effect of applied electric field on the electronic properties of spherical ZnSe/ZnS core/shell nanocrystals of experimentally relevant size is investigated by the atomistic tight-binding theory. Using this model, the calculations show that a range of electronic properties, including the single-particle spectra, atomistic characters, charge densities, excitonic energies, ground-state coulomb energies, overlaps of the electron and hole wave functions and oscillation strengths, all depend on the strengths of the applied electric field. The spatial distributions of the electron and hole wave functions are induced by the applied electric field. The analysis demonstrates a clear manipulation of the electronic properties of ZnSe/ZnS core/shell nanocrystals by introducing and varying the applied electric field strengths. According to the comprehensive investigations, I suppose that these atomistic computations will be of prospective help for experimental works concentrated on the new optoelectronic devices based on the applied electric field.     

An adaptive fuzzy combination model based on self-organizing map and support vector regression for electric load forecasting

Electric load forecasting is an important task in the daily operations of a power utility associated with energy transfer scheduling, unit commitment and load dispatch. Inspired by the various non-linearity of electric load data and the strong learning capacity of support vector regression (SVR) for small sample and balanced data, this paper presents an adaptive fuzzy combination model based on the self-organizing map (SOM), the SVR and the fuzzy inference method. The adaptive fuzzy combination model can effectively count for electric load forecasting with good accuracy and interpretability at the same time. The key idea behind the combination is to build a human-understandable knowledge base by constructing a fuzzy membership function for each homogeneous sub-population. The comparison of different mathematical models and the effectiveness of the presented model are shown by the real data of New South Wales electricity market. The obtained results confirm the validity of the developed model.     

Reducing deformation effect of tunnel with Non-Deformable Support System by Jointed Rock Mass Model

Numerical modeling has been used widely in mining and construction industries in recent years. The most important issue in engineering projects designed with numerical modeling is accurate modeling of rock mass behavior. If the rock mass behavior is modeled accurately, fewer problems will be faced during field application of projects. Selection of the true material model is a very important issue in numerical modeling for the tunnel projects. Non-Deformable Support System (NDSS), which will be mentioned in the scope of this research, does not mean that it does not permit any deformation or is a very stiff system. NDSS is a support system that does not permit deformations exceeding specified deformation amounts which are calculated with determination of the accurate rock mass behavior by the true material model and it must be evaluated with support system and excavation advance specifically. The origin of the paper is that numerical modeling provides more comfortable results in tunneling in case one can determine rock mass deformation and failure behavior appropriately. In (NDSS), however, support system element can only be determined by proper numerical modeling analysis. Moreover, deformation values determined by NDSS analysis are accepted as limit values. Therefore, applied support system should be within deformation tolerance limits determined by NDSS analysis. Briefly, this paper is related to NDSS that should be determined by numerical modeling analysis.In this research, in regard to the excessive deformations in T-35 tunnel which is one of the 33 tunnels of Ankara–Istanbul High-Speed Railway Project, results of the in situ measurements in the tunnel excavated with the new developed NDSS and results of the numerical model made with Jointed Rock Mass Model have been compared. It is determined that the results of the numerical modeling and the in situ measurements are very consistent with each other.     

The dual capacity of the NiSn alloy/MWCNT nanocomposite for sodium and hydrogen ions storage using porous Cu foam as a current collector

A nanostructured NiSn alloy/multi-walled carbon nanotube (MWCNT) composite was successfully synthesized for highly reversible sodium and hydrogen ions storage by using an electrochemical deposition process on porous Cu foam. The surface morphology of the resulting NiSn alloy/MWCNT nanocomposite was characterized using a field-emission scanning electron microscope, indicating the formation of sphere-like NiSn alloy nanoparticles with an average size of 190 nm. On the other hand, X-ray diffraction analysis, energy dispersive and Fourier transform infrared spectroscopies were employed to determine the crystalline structure, elemental surface and chemical composition of the nanocomposite electrode. The initial sodium discharge capacity of the electrode was maximized at ∼550 mAh g⁻¹ under the current density of 1000 mA g⁻¹, and a high hydrogen discharge capacity of 5200 mAh g⁻¹ was obtained at 1100 mA g⁻¹ after 20 cycles. A comprehensive comparison between the sodium and hydrogen ions capacities in this study and those of the literature for different materials and structures was also performed. Accordingly, the resulting nanocomposite electrode with dual capacity may offer promising applications in both sodium-ion battery and hydrogen storage.     

Prospects for hydrogen as a transport fuel

Early forecasts for hydrogen's role in transport usually proved over-optimistic, with several seeing hydrogen as an important transport fuel by year 2010 or even much earlier. Over the past century, vehicular passenger transport has experienced hypergrowth in terms of task, energy use and greenhouse gas emissions. For a variety of reasons, future decades may well see a significantly reduced global passenger transport task, as well as a widespread phasing-out of internal combustion engine vehicles, especially in cities. In contrast, the global freight transport task is unlikely to decline much, and could even grow, so that freight transport will dominate total transport energy use. Even if the world does finally respond seriously to climate change, likely policies will not favour hydrogen for private passenger vehicles for many decades. Nevertheless, hydrogen has clear superiority over electric vehicles for heavy freight transport. Given this advantage, it may be desirable to promote hydrogen for freight well before large amounts of renewable hydrogen are available from surplus intermittent renewable energy electricity.     

Process simulation and integration of IGCC systems for H2/syngas/electricity generation with control on CO2 emissions

IGCC is a pre-combustion technology that can be effectively used to produce both hydrogen and electricity while reducing the greenhouse gas (GHG) emissions. Two process models are developed in Aspen Plus^® software and are compared techno-economically. The conventional design of IGCC process is taken as case 1, whereas, case 2 represents the conceptual design of sequential integration of reforming model with the gasification unit to enhance the syngas yield. The case 2 utilizes the steam generated in the gasification process to sustain the methane reforming process which consequently enhances both the H₂ production capacity and cold gas efficiency. It has been analyzed from results that case 2 can enhance the process performance by 4.77% and economics in terms of cost of electricity by 5.9% compared to the conventional process. However, the utilization of natural gas in the case 2 is considered as a standalone fuel so the process performance of NGCC power plants has been also incorporated to ensure the realistic analysis. The results also showed that case 2 design offers 3.9% higher process performance than the cumulative (IGCC + NGCC) processes, respectively. Moreover, the CO₂ specific emissions and LCOE for the case 2 is also lower than the case.     

Strong organic acid-assistant synthesis of holey graphitic carbon nitride for efficient visible light photocatalytic H2 generation

A facile and cost-effective method was developed for the synthesis of holey N-deficient graphitic carbon nitride nanosheets (FCN) using trifluoroacetic-acid-treated urea as a precursor. The role of trifluoroacetic acid on the composition, structure and photocatalytic performance of the prepared catalysts was carefully investigated. The obtained samples displayed laminated porous morphology with nitrogen defects, larger specific surface areas, extended range of spectral response and enhanced electron mobility of charge carriers. Consequently, the optimized catalyst FCN-400 exhibited superb photocatalytic performance and excellent cycling stability for hydrogen evolution. The hydrogen evolution rate over FCN-400 reached 309.3 μmol/h under visible light irradiation, which is 11.3-fold of that of urea-derived graphitic carbon nitride (27.3 μmol/h).     

Automatic abstraction controller in reinforcement learning agent via automata

Reinforcement learning (RL) for solving large and complex problems faces the curse of dimensions problem. To overcome this problem, frameworks based on the temporal abstraction have been presented; each having their advantages and disadvantages. This paper proposes a new method like the strategies introduced in the hierarchical abstract machines (HAMs) to create a high-level controller layer of reinforcement learning which uses options. The proposed framework considers a non-deterministic automata as a controller to make a more effective use of temporally extended actions and state space clustering. This method can be viewed as a bridge between option and HAM frameworks, which tries to suggest a new framework to decrease the disadvantage of both by creating connection structures between them and at the same time takes advantages of them. Experimental results on different test environments show significant efficiency of the proposed method.