Thermodynamic and Transport Properties of Biomass-Derived Furfural, Furfuryl Alcohol and Their Mixtures

The limited reserves and well-known disadvantages of using fossil energy sources have increased the need for appropriate renewable substitutes in the production of various chemicals and materials. Biomass has been shown to be worthy of attention since it can be converted to biofuels and value-added chemicals relatively easily. The design of biomass valorisation process requires knowledge on the thermodynamic behaviour of the biomass-derived compounds, such as furfural and furfuryl alcohol. The thermodynamic and transport properties of the binary system furfural + furfuryl alcohol were studied at various temperatures and pressures. Density, speed of sound and refractive index were measured in the temperature range T = (288.15–345.15) K and viscosity was measured at temperatures up to 373.15 K, all at atmospheric pressure. Further, the density of pure components was obtained in the temperature range (293.15–413.15) K for furfural and (293.15–373.15) K for furfuryl alcohol at pressures up to 60.0 MPa. The obtained density values were correlated using the modified Tammann–Tait equation with an average absolute deviation lower than 0.009% for furfural and furfuryl alcohol. The optimised parameters were used for the calculation of the isothermal compressibility, the isobaric thermal expansivity, the internal pressure and the isobaric and isochoric specific heat capacities. The reported data are a valuable source of information for the further application of the investigated compounds.

Elastocaloric signature of nematic fluctuations

The elastocaloric effect (ECE) relates changes in entropy to changes in strain experienced by a material. As such, ECE measurements can provide valuable information about the entropy landscape proximate to strain-tuned phase transitions. For ordered states that break only point symmetries, bilinear coupling of the order parameter with strain implies that the ECE can also provide a window on fluctuations above the critical temperature and hence, in principle, can also provide a thermodynamic measure of the associated susceptibility. To demonstrate this, we use the ECE to sensitively reveal the presence of nematic fluctuations in the archetypal Fe-based superconductor Ba(Fe1−xCox)2As2. By performing these measurements simultaneously with elastoresistivity in a multimodal fashion, we are able to make a direct and unambiguous comparison of these closely related thermodynamic and transport properties, both of which are sensitive to nematic fluctuations. As a result, we have uncovered an unanticipated doping dependence of the nemato-elastic coupling and of the magnitude of the scattering of low-energy quasi-particles by nematic fluctuations—while the former weakens, the latter increases dramatically with increasing doping.

The Data Mining Technique Using RapidMiner Software for New Zeotropic Refrigerant

This research presents the development of environmentally-friendly and energy efficient refrigerant for medium temperature refrigeration systems that new azeotropic refrigerant mixture of hydrofluorocarbons and hydrocarbon that can retrofit in the refrigeration system using R404A. The medium back pressure refrigeration testing standard that follow CAN/ANSI/AHRI540 standard air-conditioning, heating, and refrigeration institute (AHRI) and The properties of refrigerants and refrigeration simulation system that used national institute of standards and technology (NIST) reference fluid thermodynamic and transport properties database (REFPROP) software and NIST vapor compression cycle model accounting for refrigerant thermodynamic and transport properties (CYCLE_D-HX) software. The methodology uses decision tree function in datamining by rapid minor software that first of KDnuggets annual software poll that showed new azeotropic refrigerant mixture had cooling capacity, refrigerant effect, GWP and boiling point were lower than R404A but work and pressure for medium temperature refrigeration system of azeotropic refrigerant mixture were higher than R404A. The artificial intelligence (AI) by data mining technic can predictive environmentally-friendly and energy efficient refrigerant for medium temperature refrigeration. The result of refrigerant mixed by R134A, R32, R125 and R1270 and is consistent with the evolution of fourth-generation refrigerants that contain a mixture of HFCs and HCs which are required to produce a low-GWP, zero-ozone-depletion-potential (ODP), high-capacity, low-operating-pressure, and nontoxic refrigerant.