Les milieux biologiques et alimentaires sont généralement des mélanges contenant un nombre élevé de constituants (eau, solvants organiques, solides dissous, gaz dissous, espèces ioniques, macromolécules). La prédiction des propriétés d’équilibre de tels milieux requiert l’utilisation d’un modèle thermodynamique entièrement prédictif. Ce modèle doit également permettre d’assurer la cohérence entre des données expérimentales et garantir la robustesse de la représentation simultanée des équilibres physiques (liquide-vapeur, solubilité, etc.) et chimiques (dissociation, oxydo-réduction, complexation, etc.). Le potentiel chimique est une donnée indispensable pour modéliser ces équilibres. Sa connaissance dépend de la prédiction de deux variables : l’enthalpie libre de formation dans un état de référence choisi, et le coefficient d’activité qui dépend aussi de l’état de référence choisi. Le modèle COSMO-RS est un excellent modèle de prédiction des coefficients d’activité très largement utilisé dans le domaine du génie chimique où on s’intéresse essentiellement à des molécules neutres. Ce travail de thèse a permis d’étendre les performances du modèle COSMO-RS au traitement des milieux biologiques et alimentaires dans lesquels on trouve systématiquement des électrolytes en solution (en plus des molécules neutres). Un nouvel outil utilisant les récentes avancées de la mécanique quantique a été développé pour prédire les propriétés de formation à l’état gaz. En combinant des concepts de la thermodynamique, de la mécanique quantique, de l’électrostatique, et de la physique statistique, il a été démontré qu’il est possible d’utiliser le modèle COSMO-RS pour faire la transition entre l’état gaz et la phase condensée. Partant de là, ce travail démontre qu’il est maintenant possible de traiter simultanément les équilibres physiques et chimiques et donc de prédire les propriétés physico-chimiques (aW, pH, Eh) dans les milieux biologiques et alimentaires par le modèle COSMO-RS. / Food and biological systems are generally multicomponent mixtures (including water, organic solvents, dissolved solids, dissolved gases, ionic species, macromolecules). The prediction of the equilibrium properties of such environments requires the use of a fully predictive thermodynamic model. This model must be able to ensure the consistency between experimental data and to ensure the robustness of the simultaneous representation of physical equilibria (liquid-vapour, solubility, etc.) and chemical equilibria (dissociation, redox, complexation, etc.). The chemical potential is an essential property for modelling such equilibria. Its determination depends on two variables: the Gibbs free energy of formation in a chosen reference state, and the prediction of the activity coefficient which also depends on the chosen reference state. The COSMO-RS model is an excellent model for predicting activity coefficients that is widely used in chemical engineering where the studied molecules are generally neutral. This PhD study enabled to extend the performance of the COSMO-RS model toward the treatment of food and biological systems where there are systematically electrolytes in solution (in addition to neutral molecules). A new tool based on the recent advances of quantum mechanics has been developed in order to predict gas phase formation properties. By combining concepts of thermodynamics, quantum physics, electrostatics and statistical physics, it has been demonstrated that it is possible to use the COSMO-RS model to ensure the transition between the gas phase and the condensed phase. In this context, this work demonstrates that it is possible to treat simultaneously physical and chemical equilibria and thus to predict physico-chemical properties (aW, pH, Eh) in food and biological systems using the COSMO-RS model.

Polymers is a class of molecules which can have many different structures due to a large number of degrees of freedom. Many biopolymers, e.g. DNA, but also synthetic macromolecules have special structural features due to their backbone stiffness. Since such structural properties are important for e.g. the biological function, a lot of effort has been put into the investigation of the configurational properties of semi-stiff molecules. A theoretical treatment of these systems is often accompanied by computer simulations. The main idea is to compare theoretically derived models with experimental results for real polymers. Using Monte Carlo simulations, I have investigated how this computational technique can build a bridge between theoretical models and experimentally observed phenomena. The effort was mainly directed to develop sampling techniques, for efficiently exploring the configurational space of semi-stiff chains in a wide range of structures. The work was concentrated on compact conformations, since they, as is well known from previous studies, are difficult to sample using conventional methods. In my studies I have shown that the simple and, at a first glance, time consuming method of bead-by-bead regrow as a way of changing the configuration of a semi-stiff chain gave very promising and encouraging results when combined with modern simulation techniques, like Entropic Sampling with the Wang-Landau algorithm. The resulting simulation package was also suitable for parallelization which resulted in a further speed-up of the calculations. In addition to the more elaborate sampling methods, I also investigated external conditions to induce compaction of a semi-stiff polymer. In the case of a polyampholyte the condensing agent could be a multivalent salt, creating effective attraction between the loops of the chain, while for neutral polymers, an external field and the geometry of the confining volume can induce a compaction.