Experimental and theoretical study of the physicochemical properties of the novel imidazole-based eutectic solvent.

Novel solvents and their applications are experiencing an increasing interest by the scientific community. Imidazole has been utilized as a major component in many successful ionic liquids. However, very limited studies were reported for using it as a hydrogen bond acceptor in the synthesis of eutectic solvents. In this work, a novel eutectic solvent composed of Imidazole and Monoethanolamine (MEA) is synthesized at different molar ratios. The basic physicochemical properties such as melting point, density, viscosity, and refractive index were measured at different temperatures and modeled as a function of molar composition and temperature. FTIR and 1H NMR analyses were conducted and, the nature and strength of the molecular interaction between the two solvent molecules were investigated by conducting combined molecular dynamics (MD) simulations and density functional theory (DFT) calculations. The study revealed the electrostatic H-bonding nature of interaction with strength related to their bond distances. The binding energy between the two DES ingredients is proportional to the amount of MEA in the DES due to increasing the H-bonding interactions between Imidazole and MEA molecules. These findings suggest that DES might be used in a variety of chemical and industrial applications.

Thermal Conductivities of Choline Chloride-Based Deep Eutectic Solvents and Their Mixtures with Water: Measurement and Estimation.

The thermal conductivities of selected deep eutectic solvents (DESs) were determined using the modified transient plane source (MTPS) method over the temperature range from 295 K to 363 K at atmospheric pressure. The results were found to range from 0.198 W·m-1·K-1 to 0.250 W·m-1·K-1. Various empirical and thermodynamic correlations present in literature, including the group contribution method and mixing correlations, were used to model the thermal conductivities of these DES at different temperatures. The predictions of these correlations were compared and consolidated with the reported experimental values. In addition, the thermal conductivities of DES mixtures with water over a wide range of compositions at 298 K and atmospheric pressure were measured. The standard uncertainty in thermal conductivity was estimated to be less than ± 0.001 W·m-1·K-1 and ± 0.05 K in temperature. The results indicated that DES have significant potential for use as heat transfer fluids.

Superhydrophobic nanocarbon-based membrane with antibacterial characteristics.

To overcome the biofouling challenge which faces membrane water treatment processed, the novel superhydrophobic carbon nanomaterials impregnated on/powder activated carbon (CNMs/PAC) was utilized to successfully design prepare an antimicrobial membrane. The research was conducted following a systematic statistical design of experiments technique considering various parameters of composite membrane fabrication. The impact of these parameters of composite membrane on Staphylococcus aureus growth was investigated. The bacteria growth was analyzed through spectrophotometer and SEM. The effect of CNMs' hydrophobicity on the bacterial colonies revealed a decrease in the abundance of bacterial colonies and an alteration in structure with increasing the hydrophobicity. The results revealed that the optimum preparative conditions for carbon loading CNMs/PAC was 363.04 mg with a polymer concentration of 22.64 g/100 g, and a casting knife thickness of 133.91 µm. These conditions have resulted in decreasing the number of bacteria colonies to about 7.56 CFU. Our results provided a strong evidence on the antibacterial effect and consequently on the antibiofouling potential of CNMs/PAC in membrane.

Intermolecular interactions and solvation effects of dimethylsulfoxide on type III deep eutectic solvents.

Reline, a typical type III Deep Eutectic Solvent (DES), composed of urea and choline chloride, is widely used and highly investigated for its thermo-physical properties and intermolecular interactions. The formation of hydrogen bonds between urea and chloride ions and a strong suppression in the hydrogen bonds between the urea molecules lead to a significantly low melting point of reline. Considering commercial applications, aqueous solutions of reline have been extensively analyzed; however, water, being a protic solvent, strongly affects the hydrogen bond network in reline and disrupts its structure. A mixture of reline with aprotic solvent has not been systematically investigated in the literature. To this end, we herein explore the intermolecular interactions between reline and dimethylsulfoxide (DMSO), a commonly used industrial solvent, which unlike water, does not have labile hydrogen. Our experiments revealed that a decrease in the density of the mixture with increasing DMSO amount is less significant as compared with water. Furthermore, molecular dynamics simulation results, as analyzed in terms of the hydrogen bonds between the components and interaction energies, suggest a very limited effect on urea-urea, urea-chloride, and urea-choline interactions in the presence of DMSO. Moreover, DMSO preferentially interacts with urea in the mixture; whereas in the case with water, the chloride ion was preferentially hydrated. Radial distribution analysis indicates that reline, while getting diluted by DMSO, maintains its structure up to 0.7-mole fraction of DMSO. Taken together the results reveal that DMSO reserves intermolecular interactions of reline better than water.

Predicting wastewater treatment plant quality parameters using a novel hybrid linear-nonlinear methodology.

Biochemical oxygen demand (BOD), chemical oxygen demand (COD), total dissolved solids (TDS) and total suspended solids (TSS) are the most commonly regulated wastewater effluent parameters. The measurement and prediction of these parameters are essential for assessing the performance and upgrade of wastewater treatment facilities. In this study, a new methodology, combining a linear stochastic model (ARIMA) and nonlinear outlier robust extreme learning machine technique (ORELM) with various preprocesses, is presented to model the quality parameters of effluent wastewater (ARIMA-ORELM). For each of the studied parameters, 144 different (144 × 8 models) linear models (ARIMA) are presented, with the superior model of each parameter being selected based on statistical indices. Moreover, 48 nonlinear models (ORELM) and 48 hybrid models (ARIMA-ORELM) were considered. The use of linear and nonlinear approaches to model the linear and nonlinear terms (respectively) of each time series in the hybrid model increased the efficiency and accuracy of the predictions for all of the time series. The influent wastewater nonlinear TSS model and the effluent COD and BOD models attained the best performance with a high correlation coefficient of 0.95. The use of hybrid models improved the prediction capability of all quality parameters with the best performance being achieved for the effluent BOD model (R2 = 0.99).

Effect of water on the thermo-physical properties of Reline: An experimental and molecular simulation based approach.

Increasing applications of ionic liquids and their analogues, namely Deep Eutectic Solvents (DESs), requires further investigation into the effect of moisture content on the physico-chemical characteristics of these fluids. Although it is common practice to synthesize these fluids in a moisture-controlled environment, as moisture is generally considered to have an impact on their properties, there are no systematic studies on this. We herein examine the effects of water on Reline, a Type-III DES composed of urea and choline chloride. Experiments were performed to obtain the physical properties of aqueous Reline solution. We observed moderate changes in density, speed of sound, refractive index, and pH with increasing water fraction; however, the change in viscosity and conductivity was strong and exponential. In addition, molecular dynamics simulations were performed to analyze the intermolecular interactions of Reline and aqueous Reline solutions. The simulations primarily present the significance of urea-anion interaction to explain the low melting point of the DES. In the presence of water, the anion is preferentially hydrated as compared to urea or the cation. More interestingly, simulations help to classify the effects of water into different regimes. At low water fractions (<5%) the urea-urea interactions are enhanced, as is revealed through the hydrogen bond analysis. Beyond 25% water fractions, the components of Reline are individually hydrated and have high diffusivity, which is further reflected in the change in transport properties. The results presented herein provide valuable information on aqueous Reline solutions both in terms of experimental data and molecular insights, which in turn, we believe, might assist in developing further applications of Reline and other related DESs.

Ethanesulfonic acid-based esterification of industrial acidic crude palm oil for biodiesel production.

An industrial grade acidic crude palm oil (ACPO) pre-treatment process was carried out using ethanesulfonic acid (ESA) as a catalyst in the esterification reaction. ESA was used in different dosages to reduce free fatty acid (FFA) to a minimum level for the second stage of biodiesel production via alkaline transesterification reaction. Different process operating conditions were optimized such as ESA dosage (0.25-3.5% wt/wt), methanol to ACPO molar ratio (1:1-20:1), reaction temperature (40-70 °C), and reaction time (3-150 min). This study revealed the potential use of abundant quantities of ACPO from oil palm mills for biodiesel production. The lab scale results showed the effectiveness of the pre-treatment process using ESA catalyst. Three consecutive catalyst recycling runs were achieved without significant degradation in its performance. Second and third reuse runs needed more reaction time to achieve the target level of FFA content. Esterification and transesterification using ESA and KOH respectively is proposed for biodiesel industrial scale production. The produced biodiesel meets the international standards specifications for biodiesel fuel (EN 14214 and ASTM D6751).

Using artificial neural networks and model predictive control to optimize acoustically assisted Doxorubicin release from polymeric micelles.

We have been developing a drug delivery system that uses Pluronic P105 micelles to sequester a chemotherapeutic drug--namely, Doxorubicin (Dox)--until it reaches the cancer site. Ultrasound is then applied to release the drug directly to the tumor and in the process minimize the adverse side effects of chemotherapy on non-tumor tissues. Here, we present an artificial neural network (ANN) model that attempts to model the dynamic release of Dox from P105 micelles under different ultrasonic power intensities at two frequencies. The developed ANN model is then utilized to optimize the ultrasound application to achieve a target drug release at the tumor site via an ANN-based model predictive control. The parameters of the controller are then tuned to achieve good reference signal tracking. We were successful in designing and testing a controller capable of adjusting the ultrasound frequency, intensity, and pulse length to sustain constant Dox release.

Modeling and sensitivity analysis of acoustic release of Doxorubicin from unstabilized pluronic P105 using an artificial neural network model.

This paper models steady state acoustic release of Doxorubicin (Dox) from Pluronic P105 micelles using Artificial Neural Networks (ANN). Previously collected release data were compiled and used to train, validate, and test an ANN model. Sensitivity analysis was then performed on the following operating conditions: ultrasonic frequency, power density, Pluronic P105 concentration, and temperature. The model showed that drug release was most efficient at lower frequencies. The analysis also demonstrated that release increases as the power density increases. Sensitivity plots of ultrasound intensity revealed a drug release threshold of 0.015 W/cm2 and 0.38 W/cm2 at 20 and 70 kHz, respectively. The presence of a power density threshold provides strong evidence that cavitation plays an important role in acoustically activated drug release from polymeric micelles. Based on the developed model, Dox release is not a strong function of temperature, suggesting that thermal effects do not play a major role in the physical mechanism involved. Finally, sensitivity plots of P105 concentration indicated that higher release was observed at lower copolymer concentrations.

Use of artificial neural network black-box modeling for the prediction of wastewater treatment plants performance.

A reliable model for any wastewater treatment plant is essential in order to provide a tool for predicting its performance and to form a basis for controlling the operation of the process. This would minimize the operation costs and assess the stability of environmental balance. This process is complex and attains a high degree of nonlinearity due to the presence of bio-organic constituents that are difficult to model using mechanistic approaches. Predicting the plant operational parameters using conventional experimental techniques is also a time consuming step and is an obstacle in the way of efficient control of such processes. In this work, an artificial neural network (ANN) black-box modeling approach was used to acquire the knowledge base of a real wastewater plant and then used as a process model. The study signifies that the ANNs are capable of capturing the plant operation characteristics with a good degree of accuracy. A computer model is developed that incorporates the trained ANN plant model. The developed program is implemented and validated using plant-scale data obtained from a local wastewater treatment plant, namely the Doha West wastewater treatment plant (WWTP). It is used as a valuable performance assessment tool for plant operators and decision makers. The ANN model provided accurate predictions of the effluent stream, in terms of biological oxygen demand (BOD), chemical oxygen demand (COD) and total suspended solids (TSS) when using COD as an input in the crude supply stream. It can be said that the ANN predictions based on three crude supply inputs together, namely BOD, COD and TSS, resulted in better ANN predictions when using only one crude supply input. Graphical user interface representation of the ANN for the Doha West WWTP data is performed and presented.