Hydrogen peroxide filled poly(methyl methacrylate) microcapsules: potential oxygen delivery materials

This paper describes the synthesis of H2O2–H2O filled poly(methyl methacrylate) (PMMA) microcapsules as potential candidates for controlled O2 delivery. The microcapsules are prepared by a water-in-oil solvent emulsion and evaporation method. The results of this study describe the effect of process parameters on the characteristics of the microcapsules and on their in vitro performance. The size of the microcapsules, as determined from scanning electron microscopy, ranges from ∼5 to 30 μm and the size distribution is narrow. The microcapsules exhibit an internal morphology with entrapped H2O2–H2O droplets randomly distributed in the PMMA continuous phase. In vitro release studies of 4.5 wt% H2O2-loaded microcapsules show that ∼70% of the H2O2 releases in 24 h. This corresponds to a total O2 production of ∼12 cc/gram of dry microcapsules. Shelf-life studies show that the microcapsules retain ∼84 wt% of the initially loaded H2O2 after nine months storage at 2–8 °C, which is an attractive feature for clinical applications

General method for prediction of thermal conductivity for well-characterized hydrocarbon mixtures and fuels up to extreme conditions using entropy scaling

A general and efficient technique is developed to predict the thermal conductivity of well-characterized hydrocarbon mixtures, rocket propellant (RP) fuels, and jet fuels up to high temperatures and high pressures (HTHP). The technique is based upon entropy scaling using the group contribution method coupled with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state. The mixture number averaged molecular weight and hydrogen to carbon ratio are used to define a single pseudo-component to represent the compounds in a well-characterized hydrocarbon mixture or fuel. With these two input parameters, thermal conductivity predictions are less accurate when the mixture contains significant amounts of iso-alkanes, but the predictions improve when a single thermal conductivity data point at a reference condition is used to fit one model parameter. For eleven binary mixtures and three ternary mixtures at conditions from 288 to 360 K and up to 4,500 bar, thermal conductivities are predicted with mean absolute percent deviations (MAPDs) of 16.0 and 3.0% using the two-parameter and three-parameter models, respectively. Thermal conductivities are predicted for three RP fuels and three jet fuels at conditions from 293 to 598 K and up to 700 bar with MAPDs of 14.3 and 2.0% using the two-parameter and three-parameter models, respectively

Phase Behavior and Densities of Propylene + Hexane Binary Mixtures to 585 K and 70 MPa

In this study, we report phase behavior data for propylene + hexane mixtures at temperatures of 295 to 468 K and pressures to 5.5 MPa and high-pressure mixture density data at temperatures of 295 to 584 K and pressures to 70 MPa. Both the phase behavior and density data are simultaneously determined using a variable volume, high-pressure view cell that is coupled with a linear variable differential transformer. The phase behavior and mixture density data are modeled with the Soave–Redlich–Kwong (SRK), Peng–Robinson (PR), modified Sanchez–Lacombe (MSL), and perturbed-chain statistical associating fluid theory (PC-SAFT) equations of state (EoS). The PC-SAFT and MSL EoS provide the best fit of the phase behavior data with a nonzero value of 0.028 for <i>k</i>_<i>ij</i>. Likewise, the PC-SAFT EoS provides the best fit of the high-pressure mixture density data, though the PC-SAFT equation slightly overpredicts the solution density and the calculated densities are relatively insensitive to changes in <i>k</i>_<i>ij</i> from zero to 0.028