Computer simulation for the prediction of separation as a function of pH for reversed-phase high-performance liquid chromatography. I. Accuracy of a theory-based model.

Computer simulation software (DryLab I/mp) is described for predicting high-performance liquid chromatographic separation as a function of changes in mobile phase pH. Three experimental runs with pH (only) varied are used to derive values of pKa plus capacity factors (k') for the ionized and non-ionized form of each ionizable solute. Various tests of the experimental data then allow classification of each solute as acidic, basic, neutral (including strong or weak acids or bases) and amphoteric. Experimental data are reported for the separation of several substituted anilines as a function of pH and solvent composition (%B). Experimental requirements for the accurate prediction of separation (ca. +/- 2-4% in alpha) as a function of pH are discussed. The reliability of the software is demonstrated for three different samples: mixtures of (a) substituted benzoic acids, (b) substituted anilines and (c) catecholamine-related compounds.     

Activation energies for nucleation and growth and critical cluster size dependence in JMAK analyses of kinetic Monte-Carlo simulations of precipitation

Kinetic Monte-Carlo (KMC) methods are used as an approach to simulate precipitation in Cu-alloyed bcc Fe. In order to characterize the process, transformed fractions, that is, the precipitated atoms, are related to Johnson-Mehl-Avrami-Kolmogorov theory. However, simulated data often deviate from corresponding fit curves and so does the resulting growth exponent when compared to theoretical expectations. Furthermore, some data may suggest the development of a metastable phase. In our study, we show that the characteristics of the transformed fraction and, as a consequence, the derived growth exponents sensitively depend on the number of atoms that are considered to form a particle and hence contribute to the transformed fraction. With a temperature dependence of the critical cluster size and additionally accounting for severe impingement of the particles, we obtain growth exponents which lie close to the expected range between n = 1.5 and n = 2.5 for pre-existing nuclei or continuous nucleation, respectively. From these, we obtain activation energies for nucleation and growth of precipitates. In this way, atomistic KMC simulations yield thermodynamical quantities, which can be valuable input parameters for larger length scale simulation methods, for example, for Phase Field Method simulations.