Identifying Local Interfaces.

While interfacial regions often occupy a relatively small portion of a system, physical and chemical processes often proceed differently within them. It is therefore useful to identify interfacial regions to answer many questions in physical chemistry. Thermodynamic phases are often described by their density and local structure; therefore, interfacial regions can then be defined as regions with densities and structures that deviate from the properties of the neighboring phases.

Non-isoplethic measurement on the solid-liquid-vapor equilibrium of binary mixtures at cryogenic temperatures.

We measured the solid-liquid-vapor (SLV) equilibrium of binary mixtures during experiments that alternated between cooling the mixture and injecting the more-volatile component into the sample chamber; thus, the composition of the mixture changed (non-isoplethic) throughout the experiment. Four binary mixtures were used in the experiments to represent mixtures with miscible solid phases (N/CO) and barely miscible solid solutions (N/CH), as well as mixtures with intermediate solid miscibility (N/CH and CO/CH). We measured new SLV pressure data for the binary mixtures, except for N/CH, which are also available in the literature for verification in this work.

Stratification Dynamics of Titan's Lakes via Methane Evaporation.

Saturn's moon Titan is the only extraterrestrial body known to host stable lakes and a hydrological cycle. Titan's lakes predominantly contain liquid methane, ethane, and nitrogen, with methane evaporation driving its hydrological cycle. Molecular interactions between these three species lead to non-ideal behavior that causes Titan's lakes to behave differently than Earth's lakes.

Estimating dataset size requirements for classifying DNA microarray data.

A statistical methodology for estimating dataset size requirements for classifying microarray data using learning curves is introduced. The goal is to use existing classification results to estimate dataset size requirements for future classification experiments and to evaluate the gain in accuracy and significance of classifiers built with additional data. The method is based on fitting inverse power-law models to construct empirical learning curves.