Rheological and Tribological Properties of Nanocellulose-Based Ecolubricants

Based on the response surface methodology, a rheological and tribological study carried out on eco-friendly lubricants is described. Such ecolubricants consisted of fibrillated or crystalline nanocellulose in vegetable oil (castor oil, high oleic sunflower oil or their mixtures). Cellulose nanoparticles showed noticeable friction-reducing and anti-wear properties within the boundary and mixed lubrication regimes, which were found to be dependent on nanocellulose concentration, base oil composition and applied normal force. In general, both types of nanocellulose performed equally well. An excellent tribological performance, with large wear scar diameter reductions, was achieved with 3.3 wt.% (or higher) nanocellulose dispersions in castor oil-rich mixtures. The observed behavior was explained on the basis of enhanced viscosity of castor oil-rich suspensions and the preferential action of the most polar components, nanocellulose and ricinoleic acid, in the vicinity of the contact surfaces.

Results of calculations of soot formation in a tractor diesel engine running on biofuel

When burning fuel in a diesel internal combustion engine, soot is an undesirable product. To reduce the soot content in the exhaust gases, it is necessary to know the physical and chemical bases of the process of soot formation and soot burnout. Due to the determining role of acetylene in the formation of polycyclic aromatic hydrocarbons in rich mixtures in the low-temperature zone and the key role of acetylene in the surface growth of a particle in the high-temperature zone, it is concluded that the rate of surface growth of a particle is proportional to the concentration of acetylene in the reaction zone, which can be determined by the gas-kinetic method, numerically solving a system of differential equations for the components of the gas mixture under consideration.

Solubility, correlation, dissolution thermodynamics and preferential solvation of meloxicam in aqueous mixtures of 2-propanol

Background: Meloxicam is a powerful analgesic and anti-inflammatory drug widely prescribed by physicians in current therapeutics. Because of the very low aqueous equilibrium solubility of meloxicam, this property has been studied in {2-propanol + water} mixtures from (293.15 to 313.15) K to expand the solubility database about pharmaceutical compounds in mixed solvents useful for homogeneous liquid dosage forms design. Methods: Flask shaken method and UV-vis spectrophotometry were used for meloxicam solubility determinations. Jouyban-Acree model was challenged for solubility correlation. The van’t Hoff and Gibbs equations were employed here to calculate the respective apparent standard thermodynamic quantities for the dissolution and mixing processes, namely Gibbs energy, enthalpy, and entropy. In addition, the inverse Kirkwood-Buff integrals were employed to compute the preferential solvation parameters of meloxicam by 2-propanol in the mixtures. Results: Meloxicam solubility increases with temperature arising and maximum solubilities are observed in the mixture of x1 = 0.70 at all temperatures. Jouyban-Acree model correlates the meloxicam solubility very well. Dissolution processes were endothermic in all cases and entropy-driven in the interval 0.20 ≤ x1 ≤ 1.00. Non-linear enthalpy–entropy relationship was observed in the plot of enthalpy vs. Gibbs energy exhibiting negative but variant slopes in the composition region 0.00 < x1 < 0.40 and variant negative and positive slopes in the other mixtures. Meloxicam is preferentially solvated by water in water-rich mixtures, it is apparently solvated by water in 2-propanol-rich mixtures, but it is preferentially solvated by 2-propanol in the composition interval of 0.19 < x1 < 0.78. Conclusion: Solid-liquid equilibrium of meloxicam in {2-propanol + water} mixtures has been studied at several temperatures as a contribution to preformulation studies of homogeneous liquid pharmaceutical dosage forms based on this drug.

Assessment of Cubic Equations of State: Machine Learning for Rich Carbon-Dioxide Systems

Carbon capture and storage (CCS) has attracted renewed interest in the re-evaluation of the equations of state (EoS) for the prediction of thermodynamic properties. This study also evaluates EoS for Peng–Robinson (PR) and Soave–Redlich–Kwong (SRK) and their capability to predict the thermodynamic properties of CO2-rich mixtures. The investigation was carried out using machine learning such as an artificial neural network (ANN) and a classified learner. A lower average absolute relative deviation (AARD) of 7.46% was obtained for the PR in comparison with SRK (AARD = 15.0%) for three components system of CO2 with N2 and CH4. Moreover, it was found to be 13.5% for PR and 19.50% for SRK in the five components’ (CO2 with N2, CH4, Ar, and O2) case. In addition, applying machine learning provided promise and valuable insight to deal with engineering problems. The implementation of machine learning in conjunction with EoS led to getting lower predictive AARD in contrast to EoS. An of AARD 2.81% was achieved for the three components and 12.2% for the respective five components mixture.

Solubility, Solution Thermodynamics, and Preferential Solvation of Amygdalin in Ethanol + Water Solvent Mixtures

The equilibrium solubility of amygdalin in [ethanol (1) + water (2)] mixtures at 293.15 K to 328.15 K was reported. The thermodynamic properties (standard enthalpy ΔsolnH°, standard entropy ΔsolnS°, and standard Gibbs energy of solution ΔsolnG°) were computed using the generated solubility data via van’t Hoff and Gibbs equations. The dissolution process of amygdalin is endothermic and the driving mechanism in all mixtures is entropy. Maximal solubility was achieved in 0.4 mole fraction of ethanol at 328.15 K and the minimal one in neat ethanol at 293.15 K. Van’t Hoff, Jouyban–Acree–van’t Hoff, and Buchowski–Ksiazczak models were used to simulate the obtained solubility data. The calculated solubilities deviate reasonably from experimental data. Preferential solvation parameters of amygdalin in mixture solvents were analyzed using the inverse Kirkwood–Buff integrals (IKBI) method. Amygdalin is preferentially solvated by water in ethanol-rich mixtures, whereas in water-rich mixtures, there is no clear evidence that determines which of water or ethanol solvents would be most likely to solvate the molecule.