in Wiley InterScience (www.interscience.wiley.com).A hybrid experimental/computer-aided methodology for the design of solvents for reactions is presented. It is based on the use of a few reaction rate measurements to build a reaction model, followed by the formulation and solution of an optimal computer-aided molecular design problem (CAMD) in which the reaction rate under given conditions is maximized. In order to verify the suitability of the solvent candidates identified in the CAMD step, and to assess the reliability of the model used, feedback can be introduced. When the reliability of the model is found to be insufficient, experimental rate data for the candidate solvents are obtained and added to the original data set to create an updated reaction model, which can be used to find new candidate solvents. Since very few measurements are used to build the reaction model, we perform a sensitivity analysis on the model to assess the impact of uncertainty. Using this information to generate scenarios, we then solve a stochastic optimization problem, which aims to determine the solvents that give the best average performance. The final output consists of a list of candidate solvents which can be targeted for experimentation. This methodology is illustrated, step by step, through application to a solvolysis reaction.
A hybrid experimental/computer-aided methodology for the design of solvents for reactions, recently proposed by the authors [Folić et al., AIChE J. 2007, 53, 1240–1256], is extended. The methodology is based on the use of a few reaction rate measurements to build a reaction model, followed by the formulation and solution of an optimal computer-aided molecular design (CAMD) problem. The treatment of complex reaction systems, such as competing or consecutive reactions, is considered through the incorporation of a simple reactor model in the problem formulation. This approach is applied to two model reaction schemes, and it is shown that, in principle, it is possible to identify solvents that maximize product formation by enhancing the main reaction and suppressing byproduct formation. Since very few measurements are used to build the reaction model, the effect of uncertainty is tackled explicitly in a stochastic formulation of the CAMD problem. An approach to sensitivity analysis for the identification of the key model parameters is discussed. Using this information to generate scenarios, a stochastic optimization problem (whose objective is to determine the solvents with the best expected performance) is then solved. The final output consists of a list of candidate solvents that can be targeted for experimentation. The methodology is demonstrated on a Menschutkin reaction, which is a representative SN2 reaction. This shows that the uncertainty in the reaction model has little impact on the types of solvent molecules that have the best performance. Dinitrates are found to be a promising class of solvents, with regard to maximizing the reaction rate constant.
a b s t r a c tWe report experimental results for the formation of ammonia from nitric oxide and hydrogen, and from nitric oxide, water and carbon monoxide over silica, alumina and titania supported platinum and palladium catalysts. Temperature programmed reaction experiments in gas flow reactor show a considerable formation of ammonia in the temperature range 200-450 • C, which is suppressed by the presence of excess oxygen. However, oxygen sweep experiments show that for the titania supported catalysts minor amounts of oxygen promotes the ammonia formation at low temperatures. In situ DRIFT spectroscopy measurements indicate that cyanate species on the support play an important role in the ammonia formation mechanism. This work shows that alumina supported palladium is a promising system for passive selective catalytic reduction applications, exhibiting low-temperature activity during the water-gas-shift assisted ammonia formation reaction. Conversely, titania supported samples are less active for ammonia formation as a result of the poor thermal stability of the titania support.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.