Linear solvation energy relationship approach

The Boehm and potentiometric titration methods provide useful information about surface species that behave as acids in aqueous solutions. Nevertheless, a significant number of other oxygenated functionalities—such as carbonyls, esters, and ethers—are not taken into consideration even though they can play a significant role in adsorption processes by virtue of their polar properties and hydrogen-bonding abilities. Other methods described below (e.g., TPD, XPS, FTIR, as well as linear solvation energy relationship approaches) are therefore useful complements. 

Correlation methods discussed include basic mathematical and numerical techniques, and approaches based on reference substances, empirical equations, nomographs, group contributions, linear solvation energy relationships, molecular connectivity indexes, and graph theory. Chemical data correlation foundations in classical, molecular, and statistical thermodynamics are introduced. 

A linear solvation energy relationship (LSER) has been developed to predict the water-supercritical CO2 partition coefficients for a published collection of data. The independent variables in the model are empirically determined descriptors of the solute and solvent molecules. The LSER approach provides an average absolute relative deviation of 22% in the prediction of the water-supercritical CO2 partition coefficients for the six solutes considered. Results suggest that other types of equilibrium processes in supercritical fluids may be modeled using a LSER approach (Lagalante and Bruno, 1998). 

A variety of different approaches to the prediction of toxicity have been developed under the sponsorship of the Predictive Toxicology Evalnation project of the National Institnte of Environmental Health Sciences. The widespread application of compnta-tional techniqnes to stndies in biology, chemistry, and environmental sciences has led to a qnest for important, characteristic molecnlar parameters that may be directly derived from these compntational methods. Theoretical linear solvation energy relationships combine compntational molecular orbital parameters with the linear solvation energy relationship of Kamlet and Taft to characterize, nnderstand, and predict biological, chemical, and physical properties of chemical componnds (Eamini and Wilson, 1997). 

It would appear from these observations that the solvation capability might be better characterized using a linear Gibbs energy relationship approach than functions of relative permittivity. There are now numerous examples known, for which the correlation between the rates of different reactions and the solvation capability of the solvent can be satisfactorily described with the help of semiempirical parameters of solvent polarity [cf. Chapter 7). 

Another important treatment of multiple interacting solvent effects, in principle analogous to Eq. (7-50) but more precisely elaborated and more generally applicable, has been proposed by Kamlet, Abboud, and Taft (KAT) [84a, 224, 226], Theirs and Koppel and Palm s approaches have much in common, i.e. that it is necessary to consider non-specific and specific solute/solvent interactions separately, and that the latter should be subdivided into solvent Lewis-acidity interactions (HBA solute/HBD solvent) and solvent Lewis-basicity interactions (HBD solute/HBA solvent). Using the solvato-chromic solvent parameters a, and n, which have already been introduced in Section 7.4 cf. Table 7-4), the multiparameter equation (7-53) has been proposed for use in so-called linear solvation energy relationships (LSER). 

Many attempts to correlate the analyte structure with its HPLC behavior have been made in the past [4-6], The Quantitative structure-retention relationships (QSRR) theory was introduced as a theoretical approach for the prediction of HPLC retention in combination with the Abraham and co-workers adaptation of the linear solvation energy relationship (LSER) theory to chromatographic retention [7,8],... 

Quantum-chemical descriptors are used in several QSAR approaches, such as, for example, theoretical linear solvation energy relationships (TLSERs), - Mezey 3D shape analysis, - GIPF approach, - CODESSA method, -> ADAPT approach. 

The term classical QSAR is often used to denote the - Hansch analysis, -> Free-Wilson analysis, -> Linear Free Energy Relationships (LFER) and -> Linear Solvation Energy Relationships (LSER), i.e. those SRC approaches developed between 1960 and 1980 that can be considered the beginning of the modern QSAR/QSPR methods. 

Once a decision of the chemical functionality or host structure is made and a sensing film is included in a sensor device, the next goal would be to model the sensor response of the film in the device. Sensor response to an analyte is a complex function of the partitioning of the target analytes based on the interactions within the film as well as the transport properties of the analyte in the sensor. The sensor responses for polymer-based sensors have been modeled by various approaches using (1) first principles techniques such as Hansen solubilities, (2) multivariate techniques such as QSAR to correlate sensor response with molecular descriptors, and (3) simulations and empirical formulations used to calculate the partition coefficient, such as linear solvation energy relationships, to provide a measure of selectivity and sensitivity of the material under consideration. 

Structure/Response Correlations, Hansch analysis, Hammett equation, Free-Wilson analysis. Linear Solvation Energy Relationships, Linear Free Energy Relationships, group contribution methods, substituent descriptors, extrathermodynamic approach, and biological activity indices. 

In the preceding section, factors that were theoretically connected with ion-transfer processes included ion radius, solvent dielectric constant, and solvent molar volume. Failure to account totally for the standard molar Gibbs transfer energies led to the suggestion that donor-acceptor properties of the solvent could be used to augment the electrostatic approach. In this section, we briefly describe statistical approaches that have been employed to elucidate the major factors governing ion transfer. The approaches that will be treated here rely on linear solvation energy relationships of the form... 

General hnear free-energy relationships (LFERs) or linear solvation-energy relationships have been developed by Abrahams and coworkers for a number of years. The philosophy and approach was well described by Abraham et al. (1999). Briefly, the general LEER is... 

The above IGC approaches permit determination of ys add base parameters for solid surfaces, however, without the possibility of dedudng them from one single equation. The linear solvation energy relationship (LSER) [75] permits connection of a measured value (e.g., partition coefficient) to the physicochemical parameters of the solute and the solvent (e.g., polymers in the liquid or viscous state) by a five-parameter equation ... 

Abraham et al. (1994) have more recently applied the linear solvation energy relationships (LSER) approach to the aqueous solubility of a large number of organic... 

Welton et al. [127] have used the linear solvation energy relationships (LSERs) approach to study the effects of ionic liquids on the kinetics of various Sfj2 nucleophilic substitution reactions. These take several forms, with different forms of charge distribution behavior (Table 2.1) [90]. 

With this in mind, the Welton group reasoned that the same approach could be taken to the study of the effects of ionic liquids on the rates of reactions. They used the Kamlet-Taft polarity scales to develop Linear Solvation Energy Relationships (LSERs) to describe the effects of solvents on the reaction kinetics of various 8 2 nucleophilic substitution reactions f Schemes 10.1-10.4). 8 2 reactions occur in a concerted step in which the nucleophile replaces the nucleofuge or leaving group as it dissociates from the subsmate. Their reaction profiles have the form of that in Figure 10.1. 

Lee also proposes to use the LSER (linear solvation energy relationship) concept in its full context and he identifies that the polarity parameter of LSER is related to the spreading pressure. The remaining parameters of Kamlet-Taft LSER (acid, base and solubility parameter) have already their analogue in the van Oss-Good theory (acid, base and LW contributions to the surface tension). Thus, Lee believes that the spreading pressure should be included in new developments and in a proper evaluation of the van Oss-Good approach. 

Since the sum of the forward and reverse rates = kf + k. ) determines the measured rate, as indicated in Eq. (1.1), whichever is the faster wiU dominate the process. With the exception noted above, k i j > lO s when kf and k are similar in magnitude and rises toward the relative diffusion limit as the imbalance between them increases that is, as Kj 1 or Kj 1 in Eq. (1.2) is approached. At such speeds, there is simply no hope of freezing the process, and worse, no way of isolating a minor tautomer, as on attempting isolation it would instantly be transformed into the major one. The classic way around this is to use the properties, for example, of model compounds, chosen that are as close electronically as possible to those of the minor tautomer. This is described in Chapter 12, along with certain pitfalls in their use which are often neglected. Another technique that can sometimes bypass the problem is to use linear solvation energy relationship (LSER) methods, which are described in some detail in Chapter 11. The reader is referred to both these chapters for further details. 

The second aspect is more fundamental. It is related to the very nature of chemistry (quantum chemistry is physics). Chemistry deals with fuzzy objects, like solvent or substituent effects, that are of paramount importance in tautomerism. These effects can be modeled using LFER (Linear Free Energy Relationships), like the famous Hammett and Taft equations, with considerable success. Quantum calculations apply to individual molecules and perturbations remain relatively difficult to consider (an exception is general solvation using an Onsager-type approach). However, preliminary attempts have been made to treat families of compounds in a variational way [81AQ(C)105]. 

The basic requirement for the development of a more generally applicable solvent concept is the need to try to separate the various factors responsible for the solvating power of a solvent. It is important to find criteria for the solvents character that can be correlated not only to salt solubility and apparent conductivity but also to the impact of the solvents on the thermodynamics and kinetics of the electrochemical reactions. There are several approaches to defining a typical solvent property that can represent its polarity and be correlated to the thermodynamics and kinetics of reactions conducted in its solutions (i.e., a linear free-energy relationship). A comprehensive review of such approaches by Reichardt [12] divides them into three categories ... 

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