Development of a new and environment friendly hollow fiber-supported liquid phase microextraction using vesicular aggregate-based supramolecular solvent

Hollow fiber-based liquid phase microextraction (HF-LPME) using conventional solvents is limited by their relative instability and high volatility. The use of supramolecular solvents as a liquid membrane phase could overcome these inconveniences due to their negligible vapour pressure and high viscosity. In the present study, a novel and highly flexible method was developed based on supramolecular solvents constructed of vesicles of decanoic acid, which were used for the first time as a solvent in HF-LPME. This solvent is produced from the coacervation of decanoic acid aqueous vesicles by the action of tetrabutylammonium (Bu(4)N(+)). In this work, halogenated anilines as model compounds were extracted from water samples into a supramolecular solvent impregnated in the pores and also filled inside the porous polypropylene hollow fiber membrane. The extracted anilines were separated and determined by high-performance liquid chromatography. The technique requires minimal sample preparation time and toxic organic solvent consumption, and provides a significant advantage over conventional analytical methods. The important parameters influencing the extraction efficiency were studied and optimized utilizing two different optimization methods: one variable at a time and the Box-Behnken design. Under the optimum conditions, the preconcentration factors were in the range of 74 to 203. Linearity of the method was obtained in the range of 1.0-100 μg L(-1) with the correlation coefficients of determination (R(2)) ranging from 0.9901 to 0.9986. The limits of detection for the target anilines were 0.5-1.0 μg L(-1). The relative standard deviations varied from 3.9% to 6.0%. The relative recoveries of the three halogenated anilines from water samples at a spiking level of 20.0 μg L(-1) were in the range of 90.4-107.4%.     

Polyaniline-coated Fe(3) O(4) nanoparticles: An anion exchange magnetic sorbent for solid-phase extraction

In this study, the capability of the prepared polyaniline-coated Fe(3) O(4) nanoparticles for magnetic solid-phase extraction of three parabens from environmental wastewater, cream, and toothpaste samples is presented. Synthesized Fe(3) O(4) nanoparticles were coated with sulfate-doped polyaniline via polymerization of aniline in the presence of Fe(3) O(4) nanoparticles and sulfuric acid. Here, polyaniline-coated Fe(3) O(4) nanoparticles are presented as anion exchange sorbent, which extract anionic form of parabens via anion exchange with dopant of polyaniline. The experimental conditions affecting extraction efficiency were further studied and optimized. The experimental results showed that maximum extraction efficiency can be obtained at 70 mL sample solution of pH 8, extraction and desorption times of 2 and 1 min, respectively, 100 μL of 3% (v/v) acetic acid in acetonitrile as eluent, and 100 mg of the adsorbent. Under these conditions, the linear dynamic ranges were 0.5-100 μg/L with good correlation coefficients (0.998-0.999). The detection limits were in the range of 0.3-0.4 μg/L and the relative standard deviations were less than 2.4 (n = 5) for the three parabens. Finally, this fast and efficient method was further employed for determination of target analytes in cream, toothpaste, and environmental wastewater samples and satisfactory results were obtained.     

Polarization-controlled spatial localization of near-field energy in planar symmetric coupled oligomers

Arrays of planar symmetric coupled oligomers support higher sensitivity optical response than uncoupled plasmonic systems. In this work, the transition from isolated to collective optical modes in plasmonic oligomers, such as pentamers and quadrumers, is investigated via experimental characterization and simulation with good agreement. The designed and fabricated metallic oligomers consist of a single central disk and outer ring-like disks in nanoscales. It is shown that while the far-field spectral responses of oligomers are polarization-independent, due to the structure symmetry, the spatial localization of near-field energy in nanogaps can be polarization-controlled. This localization is established at a normal-incident light of a single source rather than co-illumination by two light sources accompanied by different incident angles or phase shift. It can overcome the spatial restrictions of conventional optics. The influence of the nano-disk sizes and gaps among them on the intensity and shape of the localized near-field energy in pentamers and quadrumers is also studied.     

Retraction Note to: Thermodynamic study on the interaction of cyanide ion and jack bean urease at different temperatures

Journal of Thermal Analysis and Calorimetry -     

Editorial to the Special Issue on Reconstruction of Porous Media and Materials and Its Applications

The single-well chemical tracer test (SWCTT) has emerged in the past decades as a method for measuring oil saturation prior to and/or after EOR operations, to measure the recovery performance in-situ. To use this technology, the partition coefficients of the selected tracers are essential for estimating the level of residual oil at the targeted single well. Commonly, injection of short chain alcohols and ethyl acetate, a reactive tracer, is carried out for the tracer slug, mainly based on site-specific reservoir conditions, to accurately determine the level of oil saturation in-situ. However, injection of ethyl formate has been less common due to its fast hydrolysis rate under elevated temperature, which increases the challenges in data interpretation. Therefore, a systematic study for using ethyl formate under mid-range temperature \((<60\,^{\circ }\hbox {C})\), as commonly found in mature oil field in the USA, shows the potential to be applied for SWCTT. As part of the design effort for a series of EOR field tests to manage the project risk, we particularly assessed the relationships between the partition coefficients of reactive tracers and subsurface conditions such as salinity, temperatures, type of electrolytes, and the equivalent alkane carbon number (EACN) of the crude oil experiments was performed under various reservoir conditions as a function of actual site characteristics at the targeted high saline formations. In brief, our data clearly show that the (oil/water) partition coefficient of ethyl formate increases steadily with increasing NaCl concentrations, ranging from 10,000 (0.17 M) to 250,000 mg/L (4.28 M). A similar upward trend was observed for increasing temperature between 25 and \(52\,^{\circ }\hbox {C}\); however, the partition coefficient decreases inversely with increasing the crude oil EACN over the range from 8 to 12, which are common for domestic oil samples. It was also showed that brine with high NaCl concentration yielded higher partition coefficients. In contrast, brine with high \(\hbox {CaCl}_{2}\) and \(\hbox {BaCl}_{2}\) concentration yielded lower values. And \(\hbox {MgCl}_{2}\) performed somewhat unusual trend in our tests. These results further indicate that the partition coefficient of the reactive tracer, ethyl formate, is sensitive to change in salinity, temperatures, type of electrolytes and EACN, as observed for other chemical tracers. In addition, based on the hydrolysis rate of ethyl formate under various reservoir conditions, the appropriate window of shut-in time can be pre-determined before initiating the field test. We believe that the ability of better understanding the partition coefficients and predicting the shut-in time beforehand could drastically reduce the risks of SWCTT operations.     

A Hierarchical Sampling for Capturing Permeability Trend in Rock Physics

Among all properties of reservoir rocks, permeability is an important parameter which is typically measured from core samples in the laboratory. Due to limitations of core drilling all over a reservoir, simulation of rock porous media is demanded to explore more scenarios not seen in the available data. One of the most accurate methods is cross correlation based simulation (CCSIM) which recently has broadly applied in geoscience and porous media. The purpose of this study is producing realizations with the same permeability trend to a real sample. Berea sandstone sample is selected for this aim. Permeability results, extracted from smaller sub-samples of the original sample, showed that classic Kozeny–Carman permeability trend is not suitable for this sample. One reason can be due to lack of including geometrical and fractal properties of pore-space distribution in this equation. Thus, a general trend based on fractal dimensions of pore-space and tortuosity of the Berea sample is applied in this paper. Results show that direct 3D stochastic modeling of porous media preserves porous structure and fractal behavior of rock. On the other hand, using only 2D images for constructing the 3D pore structures does not reproduce the measured experimental permeability. For this aim, a hierarchical sampling is implemented in two and three steps using both 2D and 3D stochastic modeling. Results showed that two-step sampling is not suitable enough, while the utilized three-step sampling occurs to be show excellent performance by which different models of porous media with the same permeability trend as the Berea sandstone sample can be generated.     

Linking Morphology of Porous Media to Their Macroscopic Permeability by Deep Learning

Flow, transport, mechanical, and fracture properties of porous media depend on their morphology and are usually estimated by experimental and/or computational methods. The precision of the computational approaches depends on the accuracy of the model that represents the morphology. If high accuracy is required, the computations and even experiments can be quite time-consuming. At the same time, linking the morphology directly to the permeability, as well as other important flow and transport properties, has been a long-standing problem. In this paper, we develop a new network that utilizes a deep learning (DL) algorithm to link the morphology of porous media to their permeability. The network is neither a purely traditional artificial neural network (ANN), nor is it a purely DL algorithm, but, rather, it is a hybrid of both. The input data include three-dimensional images of sandstones, hundreds of their stochastic realizations generated by a reconstruction method, and synthetic unconsolidated porous media produced by a Boolean method. To develop the network, we first extract important features of the images using a DL algorithm and then feed them to an ANN to estimate the permeabilities. We demonstrate that the network is successfully trained, such that it can develop accurate correlations between the morphology of porous media and their effective permeability. The high accuracy of the network is demonstrated by its predictions for the permeability of a variety of porous media.

     

Gas absorption using a nanofluid solvent: kinetic and equilibrium study

An experimental study has been performed to explore gas absorption in a nanofluid solvent. Propane and propylene were separately absorbed in a non-aqueous based nanofluid composed of N-methyl 2-pyrolidone (NMP) + small amount of TiO₂ nanoparticle. Absorption was performed at different initial pressures and nanoparticle concentrations. Results showed that the addition of small amount of nanoparticle enhances the rate of absorption. Nanofluid decreased the time needed to achieve equilibrium and increased the maximum amount of gas absorbed.