Les solvo-surfactants appartiennent à une nouvelle classe de molécules amphiphiles qui présentent à la fois les propriétés de tensioactifs et de solvants. Ils sont en effet capables de former des agrégats et peuvent ainsi solubiliser des composés hydrophobes. De plus, ces molécules présentent une volatilité importante, ce qui les rend particulièrement intéressantes pour des applications où cette propriété est décisive, notamment au cours de la solubilisation aqueuse de parfums. Dans un système solvo-surfactant/huile/eau (SHE), le comportement de phase est fortement influencé par l'hydrophobicité de l'huile. Le nombre équivalent de carbones d'alcane (EACN) de différentes huiles polaires est ainsi étudié. La diminution de l'EACN en comparaison avec les n-alcanes est reliée à leur fonctionnalisation et elle est rationnalisée grâce au paramètre d'empilement effectif. Les EACN de 94 huiles différentes ont été utilisés dans une analyse de régression multilinéaire basée sur les sigma moments de COSMO-RS, dans le but d'établir un modèle QSPR capable de prédire l'EACN d'hydrocarbones. Enfin, l'influence synergique de tensioactifs ioniques sur un système SHE est déterminée avec plusieurs huiles d'EACN différents. Il est montré que le tensioactif ionique augmente fortement la température de stabilité du pseudo système ternaire de même que l'efficacité de solubilisation de l'huile. Cependant, cette efficacité atteint un maximum à un certain ratio molaire en tensioactif ionique car ce dernier empêche le système de s'inverser. Ainsi, une microémulsion bicontinue, connue pour solubiliser une grande quantité d'huile et d'eau, ne peut pas être formée. / Solvo-surfactants are a relatively new class of amphiphiles, which exhibit properties of both, surfactants and solvents. They are able to form aggregates, wherein they can solubilise hydrophobic compounds. Furthermore they exhibit volatile characteristics, which make them interesting for applications where volatility is a key factor, such as aqueous fragrance solubilisations. In a solvo-surfactant/oil/water (SOW) system, the phase behaviour is strongly influenced by the hydrophobicity of the oil. Therefore the equivalent alkane carbon number (EACN) of several polar oils, such as dialkylethers, 2-alkanones, 1-chloroalkanes etc. was determined and the decrease in EACN with respect to n-alkanes was related to its functionalization, as well as rationalised with the effective packing parameter for each corresponding type of oil. The EACN of all 94 oils were then used in a multilinear regression analysis, based on COSMO-RS -moments, in order to establish a QSPR model, which is able to predict the EACN of any hydrocarbon oil. The influence of ionic surfactants was finally investigated in a SOW system, with various oils of different EACN. It was found that the ionic surfactant increases strongly the temperature stability of the (pseudo-)ternary system, as well as the efficiency to solubilise the oil. However the efficiency undergoes a maximum for a certain molar fraction of ionic surfactant, since the latter prevents the system to inverse. Thus a bicontinuous microemulsion cannot be formed, which is known to solubilise high amounts of oil and water.

Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.

Incorporating consideration for safety issues while designing solvent processes has become crucial in light of the chemical process incidents involving solvents that have taken place in recent years. The implementation of inherently safer design concepts is considered beneficial to avoid hazards during early stages of design. The application of existing process design and modeling techniques that aid the concepts of ‘substitution’, ‘intensification’ and ‘attenuation’ has been shown in this work. For ‘substitution’, computer aided molecular design (CAMD) technique has been applied to select inherently safer solvents for a solvent operation. For ‘intensification’ and ‘attenuation’, consequence models and regulatory guidance from EPA RMP have been integrated into process simulation. Combining existing techniques provides a design team with a higher level of information to make decisions based on process safety. CAMD is a methodology used for designing compounds with desired target properties. An important aspect of this methodology concerns the prediction of properties given the structure of the molecule. This work also investigates the applicability of Quantitative Structure Property Relationship (QSPR) and topological indices to CAMD. The evaluation was based on models developed to predict flash point properties of different classes of solvents. Multiple linear regression and neural network analysis were used to develop QSPR models, but there are certain limitations associated with using QSPR in CAMD which have been discussed and need further work. Practical application of molecular design and process design techniques have been demonstrated in a case study on liquid-liquid extraction of acetic acid-water mixture. Suitable inherently safer solvents were identified using ICAS-ProCAMD, and consequence models were integrated into Aspen Plus simulator using a calculator sheet. Upon integrating flammable and toxic hazard modeling, solvents such as 5-nonanone, 2-nonanone and 5-methyl-2-hexanone provide inherently safer options, while conventionally-used solvent, ethyl acetate, provides higher degree of separation capability. A conclusive decision regarding feasible solvents and operating conditions would depend on design requirements, regulatory guidance, and safety criteria specified for the process. Inherent safety has always been an important consideration to be implemented during early design steps, and this research presents a methodology to incorporate the principles and obtain inherently safer alternatives.

Surrogate fuels are mixtures of pure hydrocarbons that mimic specific properties of a real fuel. The use of a small number of pure compounds in their formulation ensures that chemical composition is well controlled, helping increase reproducibility of experiments and reduce the computational cost associated with numerical modeling. In this work, surrogate mixtures were developed for Jet A fuel based on correlations between fuel properties (cetane number, smoke point, threshold sooting index (TSI), density, viscosity, boiling point and freezing point) and the nuclear magnetic resonance (NMR) spectra of the fuel as a measure of the fuel's chemical composition. Comparison of the chemical composition and target fuel properties of the surrogate fuels developed in this work to a Jet A fuel sample and other surrogate fuels proposed in the literature revealed the superiority of these surrogate fuels in mimicking the fuel properties of interest.

Surrogate fuels are mixtures of pure hydrocarbons that mimic specific properties of a real fuel. The use of a small number of pure compounds in their formulation ensures that chemical composition is well controlled, helping increase reproducibility of experiments and reduce the computational cost associated with numerical modeling. In this work, surrogate mixtures were developed for Jet A fuel based on correlations between fuel properties (cetane number, smoke point, threshold sooting index (TSI), density, viscosity, boiling point and freezing point) and the nuclear magnetic resonance (NMR) spectra of the fuel as a measure of the fuel's chemical composition. Comparison of the chemical composition and target fuel properties of the surrogate fuels developed in this work to a Jet A fuel sample and other surrogate fuels proposed in the literature revealed the superiority of these surrogate fuels in mimicking the fuel properties of interest.

Généralement les modèles QSPR ne sont utilisés que pour prédire des propriétés des corps purs. Dans cette thèse nous avons développé une approche QSPR permettant de prédire des propriétés non additives de mélanges binaires, plus précisément leur caractère azéotropique/zéotropique. Pour parvenir à ce résultat, plusieurs types de modèles quantitatifs et qualitatifs ont été développés. L'approche est originale pour deux raisons. Premièrement, peu de travaux de recherche ont été publiés sur des mélanges dont les propriétés sont non-additives. Deuxièmement, plusieurs nouveaux aspects méthodologiques ont été introduits dans ce travail. Tout d'abord des descripteurs "spéciaux", capables de décrire des mélanges ont été proposés. De plus, un protocole robuste d'obtention et de validation des modèles a été utilisé, et un domaine d'applicabilité des modèles fiable a été proposé. La méthodologie développée pendant cette thèse démontre la fiabilité d'un nouveau concept - les modèles QSPR pour les mélanges. Elle est comparable à d'autres méthodes classiques, quoique n'utilisant qu'un faible nombre de données en comparaison.

Drug development is a long and expensive process. It is often not until potential drug candidates are administered to humans that accurate quantification of their pharmacokinetic characteristics is achieved. The goal of developing quantitative structure-pharmacokinetic relationships (QSPkRs) is to relate the molecular structure of a chemical entity with its pharmacokinetic characteristics. In this thesis artificial neural networks (ANNs) were used to construct in silico predictive QSPkRs for various pharmacokinetic parameters using different drug data sets. Drug pharmacokinetic data for all studies were taken from the literature. Information for model construction was extracted from drug molecular structure. Numerous theoretical descriptors were generated from drug structure ranging from simple constitutional and functional group counts to complex 3D quantum chemical numbers. Subsets of descriptors were selected which best modeled the target pharmacokinetic parameter(s). Using manual selective pruning, QSPkRs for physiological clearances, volumes of distribution, and fraction bound to plasma proteins were developed for a series of beta-adrenoceptor antagonists. All optimum ANN models had training and cross-validation correlations close to unity, while testing was performed with an independent set of compounds. In most cases the ANN models developed performed better than other published ANN models for the same drug data set. The ability of ANNs to develop QSPkRs with multiple target outputs was investigated for a series of cephalosporins. Multilayer perceptron ANN models were constructed for prediction of half life, volume of distribution, clearances (whole body and renal), fraction excreted in the urine, and fraction bound to plasma proteins. The optimum model was well able to differentiate compounds in a qualitative manner while quantitative predictions were mostly in agreement with observed literature values. The ability to make simultaneous predictions of important pharmacokinetic properties of a compound made this a valuable model. A radial-basis function ANN was employed to construct a quantitative structure-bioavailability relationship for a large, structurally diverse series of compounds. The optimum model contained descriptors encoding constitutional through to conformation dependent solubility characteristics. Prediction of bioavailability for the independent testing set were generally close to observed values. Furthermore, the optimum model provided a good qualitative tool for differentiating between drugs with either low or high experimental bioavailability. QSPkR models constructed with ANNs were compared with multilinear regression models. ANN models were shown to be more effective at selecting a suitable subset of descriptors to model a given pharmacokinetic parameter. They also gave more accurate predictions than multilinear regression equations. This thesis presents work which supports the use of ANNs in pharmacokinetic modeling. Successful QSPkRs were constructed using different combinations of theoretically-derived descriptors and model optimisation techniques. The results demonstrate that ANNs provide a valuable modeling tool that may be useful in drug discovery and development.

Als Modellsystem für Oxide mit Perowskitstruktur ist Strontiumtitanat besonders geeignet, um generalisierbare Erkenntnisse über die Auswirkungen von Defekten zu gewinnen und ausgehend davon Struktur-Eigenschafts-Korrelationen zu diskutieren. Durch den Einsatz verschiedener oberflächensensitiver Methoden lässt sich im Ausgangszustand eine erhöhte Konzentration von Liniendefekten an der Oberfläche nachweisen, die sich durch Temperaturbehandlung verkleinert. Die Defektchemie bei hohen Temperaturen wird zur Simulation der elektrischen Leitfähigkeit in Abhängigkeit vom umgebenden Sauerstoff-Partialdruck genutzt. Die Dotierung des oxidischen Halbleitermaterials ist von Eigendefekten abhängig, wobei Sauerstoff-Leerstellen Donatorniveaus bilden und Strontium-Leerstellen Akzeptorcharakter besitzen. Neben der Diffusionsbewegung dieser Eigendefekte bei hohen Temperaturen kann bei niedrigen Temperaturen ein elektrisches Feld deren Umverteilung bewirken. Damit zeigt sich die Leitfähigkeit abhängig von externen elektrischen Feldern, aber auch weitere Eigenschaften sind auf diesem Wege modifizierbar. Im Rahmen der Arbeit werden strukturelle Änderungen, Valenz-Änderungen und veränderte mechanische Eigenschaften nachgewiesen, die jeweils abhängig vom elektrischen Feld schaltbar sind. Schließlich wird das gezielte Ausnutzen struktureller Defekte für Speicherzellen, die den schaltbaren Widerstand von Metall-SrTiO3-Kontakten zur Grundlage haben, vorgestellt. Die Anwendbarkeit des oxidischen Halbleiters als resistives Speicherelement beruht wiederum auf der Kopplung von Sauerstoff-Leerstellen an das elektrische Feld. / Being a model system for oxides with pervovskite-type of structure, strontium titanate can be used to gain generalizable insights into the consequences of defects and to discuss resulting structure-property relationships. By employing different surface sensitive methods, an increased concentration of line defects is found at the surface that reduces on temperature treatment. The defect chemistry at elevated temperatures is used to simulate the electric conductivity depending on the oxygen partial pressure during annealing. Doping of the oxidic semiconductor depends on intrinsic defects, whereby oxygen vacancies form donor states and strontium vacancies have acceptor character. Beside the diffusion movement of these intrinsic defects at elevated temperatures, at low temperatures an electric field may cause their redistribution. Hence, the conductivity becomes dependent on external electric fields but also other properties can be altered in this way. Within this work, structural changes, valence changes and changing mechanical properties are shown to be switchable by the electric field. Finally, the dedicated usage of structural defects is demonstrated on memory cells that employ the switchable resistance of metal-SrTiO3 junctions. The applicability of the oxidic semiconductor as a resistive memory element is again based on the coupling between oxygen vacancies and the electric field.

The dielectric constant or relative static permittivity of a material represents the capacitance of the material relative to a vacuum and is important in many industrial applications. Nevertheless, accurate experimental values are often unavailable and current prediction methods lack accuracy and are often unreliable. A new QSPR (quantitative structure-property relation) correlation of dielectric constant for pure organic chemicals is developed and tested. The average absolute percent error is expected to be less than 3% when applied to hydrocarbons and non-polar compounds and less than 18% when applied to polar compounds with dielectric constant values ranging from 1.0 to 50.0. A local composition model is developed for mixture dielectric constants based on the Nonrandom-Two-Liquid (NRTL) model commonly used for correlating activity coefficients in vapor-liquid equilibrium data regression. It is predictive in that no mixture dielectric constant data are used and there are no adjustable parameters. Predictions made on 16 binary and six ternary systems at various compositions and temperatures compare favorably to extant correlations data that require experimental values to fit an adjustable parameter in the mixing rule and are significantly improved over values predicted by Oster's equation that also has no adjustable parameters. In addition, molecular dynamics (MD) simulations provide an alternative to analytic relations. Results suggest that MD simulations require very accurate force field models, particularly with respect to the charge distribution within the molecules, to yield accurate pure chemical values of dielectric constant, but with the development of more accurate pure chemical force fields, it appears that mixture simulations of any number of components are likely possible. Using MD simulations, the impact of different portions of the force field on the calculated dielectric constant were examined. The results obtained suggest that rotational polarization arising from the permanent dipole moments makes the dominant contribution to dielectric constant. Changes in the dipole moment due to angle bending and bond stretching (distortion polarization) have less impact on dielectric constant than rotational polarization due to permanent dipole alignment, with angle bending being more significant than bond stretching.

yes / Experimental values of permeability coefficients, as log Kp, of chemical compounds across human skin were collected by carefully screening the literature, and adjusted to 37 °C for the effect of temperature. The values of log Kp for partially ionized acids and bases were separated into those for their neutral and ionic species, forming a total data set of 247 compounds and species (including 35 ionic species). The obtained log Kp values have been regressed against Abraham solute descriptors to yield a correlation equation with R2 = 0.866 and SD = 0.432 log units. The equation can provide valid predictions for log Kp of neutral molecules, ions and ionic species, with predictive R2 = 0.858 and predictive SD = 0.445 log units calculated by the leave-one-out statistics. The predicted log Kp values for Na+ and Et4N+ are in good agreement with the observed values. We calculated the values of log Kp of ketoprofen as a function of the pH of the donor solution, and found that log Kp markedly varies only when ketoprofen is largely ionized. This explains why models that neglect ionization of permeants still yield reasonable statistical results. The effect of skin thickness on log Kp was investigated by inclusion of two indicator variables, one for intermediate thickness skin and one for full thickness skin, into the above equation. The newly obtained equations were found to be statistically very close to the above equation. Therefore, the thickness of human skin used makes little difference to the experimental values of log Kp.