Dynamic Viscosity of Graphene- and Ferrous Oxide-Based Nanofluids: Modeling and Experiment

This study focused on measuring the viscosity and analyzing the behavior of two types of nanofluids: ferrous oxide-deionized (DI) water nanofluids and graphene-DI water nanofluids at different temperatures and volume fractions. Zeta potential measurement, which was performed to check the stability of the nanofluids, showed stable suspensions. All viscosity measurements were conducted using a capillary viscometer at temperatures ranging between 25 and 65°C. Both types of nanofluids showed increasing viscosity with increasing nanoparticle loading and decreasing viscosity with increasing temperature. Furthermore, experiments on different-sized ferrous oxide-based nanofluids revealed inverse relation between the size of nanoparticles and viscosity. An accurate model was developed based on the Buckingham Pi theorem to fit all factors affecting viscosity in a dimensionless form. These factors are the viscosity of the base fluid, nanoparticles’ volume fraction, nanoparticles’ size, the temperature of the system, some molecular properties, and zeta potential

Density and Refractive Index of Binary Ionic Liquid Mixtures with Common Cations/Anions, along with ANFIS Modelling

Ionic liquids have many interesting properties as they share the properties of molten salts as well as organic liquids, such as low volatility, thermal stability, electrical conductivity, non-flammability, and much more. Ionic liquids are known to be good solvents for many polar and nonpolar solutes. Combined with their special properties, ionic liquids are good replacements for the conventional toxic and volatile organic solvents. Each ionic liquid has different properties than others. In order to alter, tune, and enhance the properties of ionic liquids, sometimes, it is necessary to mix different ionic liquids to achieve the desired properties. However, using mixtures of ionic liquids in chemical processes requires reliable estimations of the mixtures’ physical properties such as refractive index and density. The ionic liquids used in this work are 1-butyl-3-methylimidazolium thiocyanate ([BMIM][SCN]), 1-butyl-3-methylimidazolium tetrafluoroborate ([BMIM][BF4]), 1-hexyl-3-methylimidazolium tetrafluoroborate ([HMIM][BF4]), and 1-hexyl-3-methylimidazolium hexafluorophosphate ([HMIM][PF6]). These ionic liquids were supplied by Io-li-tec and used as received. However, new measurements for the density and refractive index were taken for the pure ionic liquids to be used as reference. In the present work, the densities and refractive indices of four different binary mixtures of ionic liquids with common cations and/or anions have been measured at various compositions and room conditions. The accuracy of different empirical mixing rules for calculation of the mixtures refractive indices was also studied. It was found that the overall absolute average percentage deviation from the ideal solution in the calculation of the molar volume of the examined binary mixtures was 0.78%. Furthermore, all of the examined mixing rules for the calculation of the refractive indices of the mixtures were found to be accurate. However, the most accurate empirical formula was found to be Heller’s relation, with an average percentage error of 0.24%. Furthermore, an artificial intelligence model, an adaptive neuro-fuzzy inference system (ANFIS), was developed to predict the density and refractive index of the different mixtures studied in this work as well as the published literature data. The predictions of the developed model were analyzed by various methods including both statistical and graphical approaches. The obtained results show that the developed model accurately predicts the density and refractive index with overall R2, RMSE, and AARD% values of 0.968, 7.274, 0.368% and 0.948, 7.32 × 10−3 and 0.319%, respectively, for the external validation dataset. Finally, a variance-based global sensitivity analysis was formed using extended the Fourier amplitude sensitivity test (EFAST). Our modelling showed that the ANFIS model outperforms the best available empirical models in the literature for predicting the refractive index of the different mixtures of ionic liquids

Microbial electrochemical systems for bioelectricity generation: Current state and future directions

Increasing environmental pollution is a major concern, driven by fossil fuel consumption and growing energy demands. Energy production from various waste materials such as wastewater and sludge, could be proposed as a practical and appropriate solution to the energy crisis and waste disposal simultaneously. This approach not only addresses waste management but also generates different forms of bioenergy through biological treatment processes.Bioelectricity, a renewable and cost-effective energy source, is generated by electric potentials and currents that are produced or exist inside living microorganisms. In fact, bioelectricity is obtained from bioelectric potentials in various biological processes and through the conversion of chemical energy into electrical energy. Recently, the application of microbial electrochemical systems (MESs) has gained significant importance as a promising means of bioelectricity production. Various MESs leading to bioelectricity generation include microbial fuel cells (MFCs) and microbial desalination cells (MDCs).This paper explores the definitions of MESs, their detailed mechanisms, configurations, operational parameters, benefits and limitations, as well as the kinetics and thermodynamics associated with bioelectricity generation technologies. Additionally, it investigated the use of various types of artificial intelligence algorithms for modeling and optimizing biological processes for bioelectricity generation