Analysis of third harmonic generation and four wave mixing in gold nanostructures by nonlinear finite difference time domain

Third order nonlinear effects and its enhancement in gold nanostructures has been numerically studied. Analysis method is based on computationally solving nonlinear Maxwell's equations, considering dispersion behavior of permittivity described by Drude model and third order nonlinear susceptibility. Simulation is done by method of nonlinear finite difference time domain method, in which nonlinear equations of electric field are solved by Newton-Raphshon method. As the main outcomes of third order nonlinear susceptibility, four wave mixing and third harmonic generation terms are produced around gold nanostructures. Results of analysis on different geometries and structures show that third order nonlinearity products are more enhanced in places where electric field enhancement is occurred due to surface plasmons. Results indicates that enhancement of nonlinearities is strongly occurred in structures whose interface is dielectric. According to analysis results, nonlinear effects are highly concentrated in the vicinity of nanostructures. Hence this approach can be used in applications where localized ultraviolet light is required.     

Application of Artificial Intelligent Tools to Modeling of Glucosamine Preparation from Exoskeleton of Shrimp

The objective of this study was to forecast and optimize the glucosamine production yield from chitin (obtained from Persian Gulf shrimp) by means of genetic algorithm (GA), particle swarm optimization (PSO), and artificial neural networks (ANNs) as tools of artificial intelligence methods. Three factors (acid concentration, acid solution to chitin ratio, and reaction time) were used as the input parameters of the models investigated. According to the obtained results, the production yield of glucosamine hydrochloride depends linearly on acid concentration, acid solution to solid ratio, and time and also the cross-product of acid concentration and time and the cross-product of solids to acid solution ratio and time. The production yield significantly increased with an increase of acid concentration, acid solution ratio, and reaction time. The production yield is inversely related to the cross-product of acid concentration and time. It means that at high acid concentrations, the longer reaction times give lower production yields. The results revealed that the average percent error (PE) for prediction of production yield by GA, PSO, and ANN are 6.84, 7.11, and 5.49%, respectively. Considering the low PE, it might be concluded that these models have a good predictive power in the studied range of variables and they have the ability of generalization to unknown cases.     

Predictive control of drying process using an adaptive neuro-fuzzy and partial least squares approach

In this paper, adaptive neuro-fuzzy inference system (ANFIS), artificial neural network (ANN), and partial least squares (PLS) approaches are applied to predictive control of a drying process. In the proposed approaches, the PLS analysis is used to pre-process actual data and to provide the necessary background to apply ANN and ANFIS approaches. A reasonable section of this study is assigned to the modeling with the aim at predicting the granule particle size and executing by ANFIS and ANN. ANN holds the promise of being capable of producing non-linear models, being able to work under noise conditions, and being fault tolerant to the loss of neurons or connections. Also, the ANFIS approach combines the advantages of fuzzy system and artificial neural network to design architecture and is capable of dealing with both limitation and complexity in the data set. The efficiencies of ANFIS and ANN approaches in prediction are compared and the superior approach is selected. Finally, by deploying the preferred approach, several scenarios are presented to be used in predictive control of spray drying as an accurate, fast running, and inexpensive tool. This is the first study that presents a flexible intelligent approach for predictive control of drying process by ANN, ANFIS, and PLS. The approach of this study may be easily applied to other production process.     

A New Approach for Predrilling the Unconfined Rock Compressive Strength Prediction

Abstract

A reasonable knowledge of rock's physical and mechanical properties could save the cost of drilling and production of a reservoir to a large extent by selection of proper operating parameters. In addition, a master development plan (MDP) for each oilfield may contain many enhanced oil recovery procedures that take advantage of rock mechanical data and principles. Thus, an integrated rock mechanical study can be considered an investment in field development.

The unconfined compressive strength (UCS) of rocks is the important rock mechanical parameter and plays a crucial role when drilling an oil or gas well. A drilling operation is an interaction between the rock and the bit and the rock will fail when the resultant stress is greater than the rock strength. UCS is actually the stress level at which rock is broken down when it is under a uniaxial stress. It can be used for bit selection, real-time wellbore stability analysis, estimation an optimized time for pulling up the bit, design of enhanced oil recovery (EOR) procedures, and reservoir subsidence studies.

Rock strength can be estimated along a drilled wellbore using different approaches, including laboratory tests, core–log relationships, and penetration model approaches. Although this rock strength profile can be used for future investigation of formations around the wellbore, they are actually dead information. Dead rock strength data may not be useful for designing a well in a blind location (infill drilling). Rock strength should be predicted prior to drilling operations. These sort of data are helpful in proposing a drilling program for a new well.

In this research, new equations for estimation of rock strength in Ahwaz oilfield are formulated based on statistical analysis. Then, they are utilized for estimation of the rock strength profile of 36 wells in a Middle Eastern oilfield. An artificial neural network is then utilized for prediction of UCS in any predefined well trajectory. Cross-validation tests showed that the results of the network were compatible with reality. This approach has proven to be useful for estimation of any designed well trajectory prior to drilling.     

Batch and fixed‐bed column adsorption of Cu(II), Pb(II) and Cd(II) from aqueous solution onto functionalised SBA‐15 mesoporous silica

Functionalised SBA‐15 mesoporous silica with polyamidoamine groups (PAMAM‐SBA‐15) was successfully prepared with the structure characterised by X‐ray diffraction, nitrogen adsorption–desorption, Fourier transform infrared spectra and thermogravimetric analysis. PAMAM‐SBA‐15 was applied as adsorbent for Cu(II), Pb(II) and Cd(II) ions removal from aqueous solution. The effects of the solution pH, adsorbent dosage and metal ion concentration were studied under the batch mode. The Langmuir model was fitted favourably to the experimental data. The maximum sorptive capacities were determined to be 1.74 mmol g^?1 for Cu(II), 1.16 mmol g^?1 for Pb(II) and 0.97 mmol g^?1 for Cd(II). The overall sorption process was fast and its kinetics was fitted well to a pseudo‐first‐order kinetic model. The mean free energy of sorption, calculated from the Dubinin–Radushkevich isotherm, indicated that the sorption of lead and copper, with E > 16 kJ mol^?1, followed the sorption mechanism by particle diffusion. The adsorbent could be regenerated three times without significant varying its sorption capacity. A series of column tests were performed to determine the breakthrough curves with varying bed heights and flow rates. The breakthrough data gave a good fit to the Thomas model. Maximum sorption capacity of 1.6, 1.3 and 1.0 mmol g^?1 were found for Cu(II), Pb(II) and Cd(II), respectively, at flow rate of 0.4 mL min^?1 and bed height of 8 cm, which corresponds to 83%, 75% and 73% of metallic ion removal, respectively, which very close to the value determined in the batch process. Bed depth service time model could describe the breakthrough data from the column experiments properly. © 2012 Canadian Society for Chemical Engineering     

Application of Amine Modified Magnetic Nanoparticles as an Efficient and Reusable Nanofluid for Removal of Ba2+ in High Saline Waters

Silicon - This study describes an investigation on the application of functionalized nanoparticles used as a sorbent for extraction and removal of barium ions from high saline waters. Magnetic...     

β-Amyloid Targeting with Two-Dimensional Covalent Organic Frameworks: Multi-Scale In-Silico Dissection of Nano-Biointerface

Cytotoxic aggregation of misfolded β-amyloid (Aβ) proteins is the main culprit suspected to be behind the development of Alzheimer's disease (AD). In this study, Aβ interactions with the novel two-dimensional (2D) covalent organic frameworks (COFs) as therapeutic options for avoiding β-amyloid aggregation have been investigated. The results from multi-scale atomistic simulations suggest that amine-functionalized COFs with a large surface area (more than 1000 m²/gr) have the potential to prevent Aβ aggregation. Gibb's free energy analysis confirmed that COFs could prevent protofibril self-assembly in addition to inhibiting β-amyloid aggregation. Additionally, it was observed that the amine functional group and high contact area could improve the inhibitory effect of COFs on Aβ aggregation and enhance the diffusivity of COFs through the blood-brain barrier (BBB). In addition, microsecond coarse-grained (CG) simulations with three hundred amyloids reveal that the presence of COFs creates instability in the structure of amyloids and consequently prevents the fibrillation. These results suggest promising applications of engineered COFs in the treatment of AD and provide a new perspective on future experimental research.