Prediction of selectivity

Methanol remains the most widely used modifier because it produces highly efficient separations, but it does not always produce the highest selectivity [8]. Recent studies have provided insight into the role of the modifier in enantioselectivity in SFC [69]. Blackwell and Stringham examined a series of phenylalanine analogues on a brush-type CSP and developed a model that allowed prediction of selectivity based on the bulk solvation parameters of various modifiers [70]. Careful choice of modifiers can be used to mask or enhance particular molecular interactions and ultimately provide control of selectivity [71]. 

A very similar QSAR approach for the modeling and prediction of selectivity of oq-AR antagonists has recently been carried out by Eric el al. It confirms the usefulness of the supermolecules approach and of the ad hoc shape descriptors in the rationalization of cq-ARs affinity and cq-AR subtype selectivity [98]. 

Abushoffa AM, Fillet M, Hubert P, Crommen J. Prediction of selectivity for enantiomeric separations of uncharged compounds by capillary electrophoresis involving dual cyclodextrin systems. J Chromatogr A 2002 948 321. 

Figure 3. Comparison of GCMC simulation and lAST predictions of selectivity derived from simulated single component isotherms. 

Introduce functional groups on the phases that will enable prediction of selectivity for different solutes. 

The prediction of selectivity for a given ion over another even in qualitative terms, let alone quantitatively, ultimately requires an understanding of the ion exchange phenomenon in terms of the fundamental properties of the system components. No single characteristic can account for observed results, and studies to date amply demonstrate that many system properties affect selectivity behaviour in ways that have assisted our understanding of the mechanism involved. 

Most of the methods developed in this book are, by themselves, only applicable to amorphous polymers and amorphous polymeric phases. (An exception with obvious relevance to the properties of multiphase materials is the development of a physically robust predictive model for the shear viscosities of dispersions in Section 13.H.) Their combination with other types of methods to predict the properties of multiphase materials from component properties and multiphase system morphology enables us to expand their applications to include the prediction of selected properties of multiphase polymeric systems where one or more of the phases are amorphous polymers. In other words, the methods developed in this book are used to predict the properties of the amorphous polymeric phases of the multiphase system. These properties are then inserted into equations of composite models and into numerical simulation schemes (along with material parameters of the other types of components, obtained from other sources such as literature tabulations) to predict the properties of the multiphase system. We use existing composite models whenever they are adequate, and develop our own otherwise. 

Application of tho Predictive Formats, in addition to the prediction of selected physical properties of envisaged blends, subsequent comparison of model calculations with experimental data allows the experimentahsts (i) to analyze the phase structure of prepared blends, (ii) to evaluate interfacial adhesion or the... 

C. E. Byrne, G. Downey, D. J. Troy, D. J. Buckley. Non-destructive prediction of selected quality attributes of beef by near-infrared reflectance spectroscopy between 750 and 1098... 

An efficient support from LFERs to speed-up method development and optimization is not yet conceivable. Besides the huge effort needed to determine solute parameters of new compoimds, even the most powerful LFER strategies do not yet offer the required accuracy in the prediction of selectivities. This vision, however, has been a focus of interest of HPLC experts for a long time. The future will show whether an LEER strategy is capable of bringing HPLC closer to the goal of computer-assisted precise prediction of retention on a physico-chemical basis. 

Chen HB, Sholl DS (2006) Predictions of selectivity and flux for CH H separations using single walled carbon nanotubes as membranes. J Membr Sci 269 (1-2) 152-160... 

As Nature offers diamondoids in large quantities from crude oil [4, 127], one ought to explore their chemistry especially in view of their potential applications in nanoelectronic devices [128]. The first challenge is to understand systematically the reactivity patterns of diamondoids, especially with respect to their selective peripheral C-H bond functionalization. This difficulty is emphasized when one considers that even triamantane (3) reacts with typical electrophiles (e.g., Br2) with very low selectivity [129]. What alternatives are there - will ionic, radical, and radical ionic C-H activation reactions eventually lead to higher C-H bond selectiv-ities These questions can, in part, be answered by computational methods when considering the very different stabilities of the cations, radicals, and radical cations of the respective diamondoids in the first step. These purely thermodynamic stabilities very often translate nicely into selectivities, at least for cationic structures. As this is often not the case for radicals, transition structures also have to be considered which makes the prediction of selectivities far more elaborate [130]. 

Loffreda D, Delbecq F, Vigne F, Sautet P. Fast prediction of selectivity in heterogeneous catalysis from extended Br0nsted-Evans-Polanyi relations A theoretical insight. Angew Chem Int Ed 2009 48 8978-8980. 

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