No abstract

Isothermal calorimetric titrations of aqueous solutions of poly(ethylene glycol) (PEG) with sodium dodecyl sulfate (SDS) are known to exhibit a peculiar trend consisting of endothermic and exothermic effects. This behavior was explained with the formation of two different mixed micellar aggregates, one characterized by hydrophobic interactions and the second by ion-dipole association. Present NMR measurements on 13C, 1H, and 23Na nuclei do not support the formation of a number of PEG-SDS aggregates characterized by interactions of different nature. Our data are rather in accordance with the initial formation, at low surfactant concentration, of a polymer-surfactant aggregate in which the polymeric chain assumes a strained conformation in order to bind a small micellar cluster. The subsequent growing of the aggregate with increasing surfactant concentration allows the polymer to relax to a more expanded, energetically favored, conformation. Further calorimetric titrations with a set of PEG samples of different molecular weight (200 to 20000 Daltons) allowed to establish a few points so far unclear. The minimum molecular weights necessary for observing the onset and the settling, respectively, of polymer-surfactant interaction were identified and the characteristic multiple peak curve of the titration of the polymer with molecular weight of 8000 Dalton was found related to the discrete binding of two successive SDS micellar clusters on the same polymeric chain

The interaction of cesium perfluorooctanoate (CsPFO) with poly(ethylene glycol) (PEG) of different molecular weight (300 < or = MW < or = 20000 Da) has been investigated at 298.15 K by isothermal titration calorimetry (ITC), density, viscosity, and conductivity measurements. Calorimetric titrations exhibited peculiar trends analogous to those already observed for sodium dodecyl sulfate (SDS). Micelles of the perfluorosurfactant, as compared to those of SDS, yield complexes with the polymer of similar thermodynamic stability but are able to interact with shorter PEG oligomers. The average number of surfactant molecules bonded per polymer chain at the saturation is about twice that observed for SDS. ITC data at 308.15 K indicate a larger thermodynamic stability of the aggregates but an almost constant stoichiometry. The peculiar thermal effects and the viscosity trend observed during the titration of an aqueous PEG solution with the surfactant appear consistent with a conformational change of the polymer. The PEG chain would evolve from a strained to an expanded conformation, induced by the growing of the surfactant micellar clusters bonded to the polymer, as suggested in a previous study of the PEG/SDS/H2O system.

In this paper, we report on the potential of a recently developed neural network for structures applied to the prediction of physical chemical properties of compounds. The proposed recursive neural network (RecNN) model is able to directly take as input a structured representation of the molecule and to model a direct and adaptive relationship between the molecular structure and target property. Therefore, it combines in a learning system the flexibility and general advantages of a neural network model with the representational power of a structured domain. As a result, a completely new approach to quantitative structure-activity relationship/ quantitative structure-property relationship (QSPR/QSAR) analysis is obtained. An original representation of the molecular structures has been developed accounting for both the occurrence of specific atoms/groups and the topological relationships among them. Gibbs free energy of solvation in water, ∆ solv G°, has been chosen as a benchmark for the model. The different approaches proposed in the literature for the prediction of this property have been reconsidered from a general perspective. The advantages of RecNN as a suitable tool for the automatization of fundamental parts of the QSPR/QSAR analysis have been highlighted. The RecNN model has been applied to the analysis of the ∆ solv G°in water of 138 monofunctional acyclic organic compounds and tested on an external data set of 33 compounds. As a result of the statistical analysis, we obtained, for the predictive accuracy estimated on the test set, correlation coefficient R ) 0.9985, standard deviation S ) 0.68 kJ mol -1 , and mean absolute error MAE ) 0.46 kJ mol -1 . The inherent ability of RecNN to abstract chemical knowledge through the adaptive learning process has been investigated by principal components analysis of the internal representations computed by the network. It has been found that the model recognizes the chemical compounds on the basis of a nontrivial combination of their chemical structure and target property.