Specific-retention-volume data from gas-liquid chromatographic measurements can be used to obtain activity coefficients at infinite dilution. There are numerous such data in the analytical chemistry literature for a variety of binary systems containing common volatile organic fluids and high-boiling, multifunctional organic substrates. A data-reduction method has been established wherein chromatographic specific-retention-volume data may be used to estimate group-interaction parameters for the UNIFAC correlation to predict activity coefficients. New UNIFAC parameters are reported for 30 group interactions.
Limited freezedrying leaves a substantial, predetermined and uniform moisture content, such as is required for compression of the product. The process can be accomplished by modification of ordinary freezedryers. It is shown that the time required for limited freeze-drying can be reduced by as much as a factor of two through the use of programmed platen temperatures, starting with higher temperatures which are then reduced as drying proceeds. l-cm cubes of cooked beef can be dried to a uniform average moisture, content of 10% in 7 hr in this way. The observed temperature histories and drying times agree well with the predictions of a quantitative model for asymmetric freeze-drying.
This paper describes what is meant by asymmetric freezedrying and reports experimental observations of the phenomenon. Current mathematical models of freeze-drying cannot account for asymmetry. This theoretical deficiency is corrected by the development and solution of a freezedrying theory termed the asymmetrically-retreating-ice-front (ARIF) model. An extension of the well-known uniformly-retreating-ice-front (URIF) model, the ARIF theory accounts for heat flux through the frozen region and for unequal heat fluxes to different sample faces. The ARIF theory is able to predict temperatures and moisture fractions during drying, total drying times, and degrees of ice-core asymmetry which agree reasonably well with experimental results.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.