Ionization coefficient of monolayer graphene nanoribbon

A semi-analytical model for impact ionization coefficient of graphene nanoribbon (GNR) is presented. The model is derived by calculating probability of electrons reaching ionization threshold energy E_t and the distance travelled by electron gaining E_t. In addition, ionization threshold energy is semi-analytically modelled for GNR. During modelling, we justify our assumptions using analytical modelling and comparison with simulation. Furthermore, it is shown that conventional silicon models are not valid for calculation of ionization coefficient of GNR. Finally, the profile of ionization is presented using the proposed models and the results are compared with that of silicon.     

An optimal replacement policy for complex multi-component systems

Given a reward structure, this paper addresses an optimal replacement problem for complex multi-component systems. To maintain revenue stream resulting from system, the system is inspected according to a homogeneous Poisson process and certain actions are carried out in response to the system state. Decisions are based on a performance measure described by a Squared Bessel process. Given some assumption, we explore the inherent relation between the Squared Bessel process and an extended Gamma (EG) process. Since there are some flow of income and increasing costs due to inspections, the problem is to optimally stop processing the system and carrying out a renewal to maximize the reward functional. To this end, using the local characteristics of the EG process as a stopping criterion and the expected total discounted reward as a measure of policy, this paper aims at determining an optimal operating (stopping) time which truly balances both income and cost and so maximizes the expected discounted reward over a cycle. In support of the model a numerical example is provided to show feasibility of this programme in real application. Attention is restricted to perfect repair and inspection, but the paper provides the structure so that different scenarios can be explored.     

Fabrication and characterization of highly efficient three component CuBTC/graphene oxide/PSF membrane for gas separation application

Metal organic frameworks (MOFs) with marvelous properties have aroused enormous attention for different application especially gas adsorption and separation. In this regard, fabrication of MOF hybrids with carbon based materials is new strategy to upgrade MOF performance. In this study CuBTC (Copper benzene-1,3,5-tricarboxylic acid)/graphene oxide (GO) composite was synthesized and characterized by BET, SEM, TGA, XRD and FT-IR techniques. Then CuBTC and CuBTC/GO composite were incorporated into polysulfone (PSF) polymer to construct mixed matrix membranes (MMMs). The obtained membranes were characterized by SEM, TGA, XRD and tensile tests and their gas permeability was measured. The results were compared to those of CuBTC/PSF MMMs. It was revealed that CuBTC/GO composite as filler showed superior performance relative to CuBTC. For instance, 15 wt% loading of CuBTC/GO in PSF represented outstanding gas separation behavior while the same loading of CuBTC in PSF deteriorated performance of MMM. Well particle dispersion and favorable polymer-filler interaction were responsible for such observed difference. A high H₂/CH₄ and H₂/N₂ selectivity of 80.03 and 70.46 were recorded for CuBTC/GO in PSF (15 wt%) compared to 44.56 and 40.92 for CuBTC in PSF (15 wt%).     

Fast evolving neuro-fuzzy model and its application in online classification and time series prediction

A strategy for improving speed of the previously proposed evolving neuro-fuzzy model (ENFM) is presented in this paper to make it more appropriate for online applications. By considering a recursive extension of Gath?CGeva clustering, the ENFM takes advantage of elliptical clusters for defining validity region of its neurons which leads to better modeling with less number of neurons. But this necessitates the computing of reverse and determinant of the covariance matrices which are time consuming in online applications with large number of input variables. In this paper a strategy for recursive estimation of singular value decomposition components of covariance matrices is proposed which converts the burdensome computations to calculating reverse and determinant of a diagonal matrix while keeping the advantages of elliptical clusters. The proposed method is applied to online detection of epileptic seizures in addition to prediction of Mackey?CGlass time series and modeling a time varying heat exchanger. Simulation results show that required time for training and test of fast ENFM is far less than its basic model. Moreover its modeling ability is similar to the ENFM which is superior to other online modeling approaches.     

Investigating the effect of nano-silica on efficiency of the foam in enhanced oil recovery

Due to the vast production of crude oil and consequent pressure drops through the reservoirs, secondary and tertiary oil recovery processes are highly necessary to recover the trapped oil. Among the different tertiary oil recovery processes, foam injection is one of the most newly proposed methods. In this regard, in the current investigation, foam solution is prepared using formation brine, C₁₉TAB surfactant and air concomitant with nano-silica (SiO₂) as foam stabilizer and mobility controller. The measurements revealed that using the surfactant-nano SiO₂ foam solution not only leads to formation of stable foam, but also can reduce the interfacial tension mostly considered as an effective parameter for higher oil recovery. Finally, the results demonstrate that there is a good chance of reducing the mobility ratio from 1.12 for formation brine and reservoir oil to 0.845 for foam solution prepared by nanoparticles.     

Crushing analysis and multi-objective optimization of different length bi-thin walled cylindrical structures under axial impact loading

This article attempts to increase the crashworthiness characteristics of energy absorbers. It is found that the effect of the bi-tubular arrangement on the energy absorption and peak force is nonlinear. This nonlinearity is somewhat related to friction but is mostly related to the changing of buckling modes. Therefore, it is possible to reach higher Specific Absorbed Energy (SAE) in the bi-tubular case than with two tubes since the weight is the same in both arrangements while the energy absorption is higher in the bi-tubular case. To exploit this, multi-objective optimization of bi-thin walled cylindrical aluminium tubes under axial impact loading is performed. The absorbed energy and the SAE are considered as the objective functions while the maximum crush load is regarded as a constraint. Finally, the optimal dimensions of tubes are found in order to maximize the SAE and energy absorption for a specified maximum crushing force.     

Adsorptive desulfurization and denitrogenation of model fuel by mesoporous adsorbents (MSU-S and CoO-MSU-S)

Adsorption of heterocyclic sulfur- and nitrogen-containing compounds by mesostructure adsorbent (MSU-S) and its modified form with cobalt oxide is studied using model fuel. The results of characteristic tests (XRD, N₂ adsorption–desorption, FTIR, and SEM) indicate that CoO impregnation causes a negative impact on mesoporous structure, crystalline phase, and particle shape along with a positive effect on surface ion exchange. CoO modification increased the adsorption loadings of DBT and BT to about 33.6% and 45.7%, respectively. For nitrogen compounds adsorption with the model fuel, adsorption loadings of quinoline and carbazole increase by 6.7% and 8.6%, respectively. Data fitting for carbazole, DBT, and BT is achieved better by the Langmuir model than the Freundlich model, and the data of quinoline fitted very well to the Freundlich model for CoO-MSU-S.