DETERMINATION OF SELECTIVITY AND MASS TRANSFER IN LIQUID-PHASE EXTRACTION FOR BUTYL GLYCOL-WATER-ISOPROPYL ETHER SYSTEM

The problems of phase equilibrium, the equilibrium distribution of a component between phases are considered, and the distribution coefficient is determined in two versions. A formula for determining the selectivity coefficient is obtained, equations relating the compositions of coexisting phases, by equating the activities in these phases, Margules constants are found. Based on the given content of the components, the numerical values of (Margules constant for A component in solvent S) and (Margules constant for S component in solvent A) were found Using the Margules equation for ternary systems, the activity coefficients of components A and B are determined in two phases. The experimentally obtained values of the molar fractions of each of the three components, responsible to different points of the binodal curve, the corresponding activity coefficients of component B, and also calculated on these coefficients of the activity values

Insight into the role of piperazine in the thermodynamics and nucleation kinetics of the triethylenediamine–methyl tertiary butyl ether system

Considering the role of piperazine in the TEDA–MTBE system, we investigate how to control the crystal size distribution.

Adverse Event extraction from Structured Product Labels using the Event-based Text-mining of Health Electronic Records (ETHER) system

Structured Product Labels follow an XML-based document markup standard approved by the Health Level Seven organization and adopted by the US Food and Drug Administration as a mechanism for exchanging medical products information. Their current organization makes their secondary use rather challenging. We used the Side Effect Resource database and DailyMed to generate a comparison dataset of 1159 Structured Product Labels. We processed the Adverse Reaction section of these Structured Product Labels with the Event-based Text-mining of Health Electronic Records system and evaluated its ability to extract and encode Adverse Event terms to Medical Dictionary for Regulatory Activities Preferred Terms. A small sample of 100 labels was then selected for further analysis. Of the 100 labels, Event-based Text-mining of Health Electronic Records achieved a precision and recall of 81 percent and 92 percent, respectively. This study demonstrated Event-based Text-mining of Health Electronic Record’s ability to extract and encode Adverse Event terms from Structured Product Labels which may potentially support multiple pharmacoepidemiological tasks.