Dissociation of polar oil components in low salinity water and its impact on crude oil–brine interfacial interactions and physical properties

Despite many efforts into the study of fluids interaction in low salinity water flooding, they are not probing the basics of transport phenomena between the involved phases. This work is aimed to bring new understanding of fluid–fluid interaction during low salinity water flooding through a series of organized experiments in which a crude oil sample with known properties was kept in contact with different brine solutions of various ionic strengths. Measuring brine pH, conductivity and crude oil viscosity and density for a period of 45 days illustrates the strong effect of the contact time and ionic strength on the dissociation of polar components and physical properties of the crude oil and brine. Besides, the interfacial tension (IFT) measurements show that the interfacial interactions are affected by several competitive interfacial processes. By decreasing the ionic strength of the brine, the solubility of naphthenic acids in the aqueous solution increases, and hence, the conductivity and the pH of the aqueous phase decrease. To verify this important finding, UV–Vis spectroscopy and 1H NMR analysis were also performed on aged brine samples. Notably, there is an ionic strength of brine in which the lowest IFT is observed, while the other physical properties are remained relatively unchanged.     

Mechanical characterization of graphene oxide reinforced epoxy at different strain rates

Strain rate has significant effect on mechanical behavior of the thermoset polymers. The rate sensitivity is more complicated for thermoset nanocomposites, which compose of two quite different types of materials. Nanofiller‐reinforced epoxy resin is widely used in the industry. In the present work, epoxy resin is reinforced by 0.05 to 0.7 wt% nanographene oxide (GO). The strain rate sensitivity of the fabricated nanocomposites is investigated through compressive test carried out at the strain rates of 0.001–1,900 s^?1. The stress–strain curves of the nanocomposites indicated considerable difference between the low‐strain and high‐strain‐rate responses of the specimens. The results showed that the compressive strength of the nanocomposites was improved by more than 100% at high strain rates with respect to the low strain rates. Also, the addition of nano‐GO had influence on compressive strength enhancement but not as significant as the effect of strain rate. It was observed that the effect of GO was less important for higher strain rates. The experimental compressive strength and modulus of elasticity of the nanocomposites were casted in empirical relations for low and high strain rates for various filler weight percentages. Scanning electron microscopy was also used to examine the quality of GO dispersion. POLYM. ENG. SCI., 59:1636–1647 2019. © 2019 Society of Plastics Engineers     

Traffic Queuing Management in the Internet of Things: An Optimized RED Algorithm Based Approach

Congestion control is one of the main obstacles in cyberspace traffic. Overcrowding in internet traffic may cause several problems; such as high packet hold-up, high packet dropping, and low packet output. In the course of data transmission for various applications in the Internet of things, such problems are usually generated relative to the input. To tackle such problems, this paper presents an analytical model using an optimized Random Early Detection (RED) algorithm-based approach for internet traffic management. The validity of the proposed model is checked through extensive simulation-based experiments. An analysis is observed for different functions on internet traffic. Four performance metrics are taken into consideration, namely, the possibility of packet loss, throughput, mean queue length and mean queue delay. Three sets of experiments are observed with varying simulation results. The experiments are thoroughly analyzed and the best packet dropping operation with minimum packet loss is identified using the proposed model.     

Fe3O4@FeSO4-MCM-41 Nanoparticles and Reusable Catalyst for the Synthesis of Quinoxalines in Solvent-free Conditions

Silicon - The direct condensation of diketones and 1,2-phenylenediamines was accomplished in the presence of magnetic MCM-41 supported ferrous sulfate (Fe3O4@FeSO4-MCM-41) as a nanosized solid acid...     

Mechanical properties of adhesively single lap-bonded joints reinforced with multi-‌‌‌walled carbon nanotubes and silica nanoparticles

This paper aims to investigate the effect of adding nanoparticles to the adhesive layer on the shear strength and elongation at failure of adhesively bonded single lap joints (SLJs). Two different toughening particles including the silica nanoparticles (SNPs) and the multi-walled carbon nanotubes (MWCNTs) were considered for reinforcing the adhesive joints. The experimental results showed that the highest improvements in the SLJ shear strength and elongation at failure were obtained for 0.2 and 0.8 wt% of MWCNTs and SNPs, respectively. The fractography results indicated that adding nanoparticles improved the failure mode from adhesive to dominant cohesive representing improved adhesion between the adhesive and adherends. Moreover, different damage mechanisms were observed for the adhesives reinforced with different toughening particles. Several mechanisms including crack growth deviation, shear yielding, plastic deformation, and pull out phenomena were observed from scanning electron microscope (SEM) fractography of the fracture surfaces of the joints reinforced by MWCNTs. While in the case of reinforcing by SNPs, the shear yielding, the particle debonding, and subsequent void growth were found as the effective energy absorbing mechanisms.     

Brittle Fracture Analysis Using a Ring-Shape Specimen Containing Two Angled Cracks

A ring shaped specimen is used for studying mixed mode fracture in brittle materials. The ring specimen is subjected to a compressive diametral load and contains two angled central cracks. A series of fracture tests are conducted under various mode mixities using the ring specimen. It is shown that the obtained experimental results are in a very good agreement with theoretical predictions of the modified maximum tangential stress criterion.     

Optimization of edible Alyssum homalocarpum seed gum-chitosan coating formulation to improve the postharvest storage potential and quality of apricot (Prunus armeniaca L.)

Edible coating based on Alyssum homalocarpum seed gum (AHSG) and chitosan for coating of fresh apricot was optimized using response surface methodology (RSM). The independent variables included concentrations of AHSG and chitosan in coating solution. The response variables studied were total soluble solid, weight loss, titratably acidity, pH, reducing sugar, moisture content, and firmness. Results indicated that RSM models were significantly (p < .05) qualified for all response variables, except for total soluble solid and titratable acidity. The regression models demonstrated R² ranging from .78 to .99. AHSG concentration influenced the weight loss, pH, and firmness, while chitosan concentration had an effect on pH and moisture content. This study showed that the coating can improve apricot firmness and stability in terms of weight loss, pH, and moisture content. The study suggested that a combination of AHSG 1.02% (w/w) and chitosan 0.33% (w/w) is the best one to achieve the desirable responses.     

THERMODYNAMICS OF MOISTURE SORPTION IN ALFALFA PELLETS

ABSTRACT

Experimental data on sorption isotherms of alfalfa pellets were used to determine the thermodynamic functions (differential heat of sorption, spreading pressure, net integral enthalpy and entropy) of the pellets. The thermodynamic functions were then utilized in modeling the specific heats of pellets as a function of moisture content. The estimated values of the thermodynamic functions at different moisture contents confirmed the results of an earlier study on the influence of moisture sorption on the physical integrity of the pellets. The changes in the thermodynamic properties and physical integrity were prominent when the moisture of the pellets were between 0.10 and 0.12 (mass fraction basis). Using the differential heat of sorption, it was determined that alfalfa pellets have higher affinity for moisture uptake when compared to wheat and lentils. The specific heat of the solids of alfalfa pellets had a parabolic relationship with moisture whereas the specific heat of water fraction increased linearly with moisture content of the pellets.     

Comparison of essential oils compositions of Nepeta persica obtained by supercritical carbon dioxide extraction and steam distillation methods

Essential oil of Nepeta persica cultivated in Iran was obtained by steam distillation and supercritical (carbon dioxide) extraction methods. The oils were analysed by capillary gas chromatography using flame ionization and mass spectrometric detections. The compounds were identified according to their retention indices and mass spectra (EI, 70 eV). The effects of different parameters such as pressure, temperature, modifier volume and extraction times (dynamic and static) on the supercritical fluid extraction (SFE) of N. persica oil were investigated. The results showed that under the pressure of 20.3 MPa, temperature of 45 °C, methanol of 1.5% v/v), dynamic extraction time of 50 min and static extraction time of 25 min extraction was more selective for the 4αβ,7α,7aα-nepetalactone. Twelve compounds were identified in the steam-distilled oil. The major components of N. persica were 4αβ,7α,7aα-nepetalactone (26.5%), cis-β-farnesene (4.4%) and 3,4α-dihydro-4aα,7α,7aα-nepetalactone (3.5%). However, by using supercritical carbon dioxide under optimum conditions, only two components have more than 90.0% of the oil. The extraction yield based on steam distillation was 0.08% (v/w). On the other hand, using SFE extraction yield in the range of 0.22–8.90% (w/w) were obtained at different conditions. The results show that, in Iranian N. persica oil, 4αβ,7α,7aα-nepetalactone is a major component.     

Transforming Hand Drawn Wireframes into Front-End Code with Deep Learning

The way towards generating a website front end involves a designer settling on an idea for what kind of layout they want the website to have, then proceeding to plan and implement each aspect one by one until they have converted what they initially laid out into its Html front end form, this process can take a considerable time, especially considering the first draft of the design is traditionally never the final one. This process can take up a large amount of resource real estate, and as we have laid out in this paper, by using a Model consisting of various Neural Networks trained on a custom dataset. It can be automated into assisting designers, allowing them to focus on the other more complicated parts of the system they are designing by quickly generating what would rather be straightforward busywork. Over the past 20 years, the boom in how much the internet is used and the sheer volume of pages on it demands a high level of work and time to create them. For the efficiency of the process, we proposed a multi-model-based architecture on image captioning, consisting of Convolutional neural network (CNN) and Long short-term memory (LSTM) models. Our proposed approach trained on our custom-made database can be automated into assisting designers, allowing them to focus on the other more complicated part of the system. We trained our model in several batches over a custom-made dataset consisting of over 6300 files and were finally able to achieve a Bilingual Evaluation Understudy (BLEU) score for a batch of 50 hand-drawn images at 87.86%