Some amphiphilic molecules in particular environments may self-assemble and originate chemical entities, such as vesicles, which are relevant11 in technological applications. Experimentation in this field is difficult because of the high dimensionality of the search space and the high cost of12 each experiment. To tackle the problem of designing a relatively small number of experiments to achieve the relevant information on the problem,13 we propose an evolutionary design of experiments based on a genetic algorithm. We built a particular algorithm where design and laboratory14 experimentation interact leading the search toward the optimality region of the space. To get insight in the process we then modelled the15 experimental results with different classes of regression models; from modelling we could identify the special role played by some molecules and16 the relevance of their relative weight in the composition. With modelling we “virtually” explored the experimental space and predicted17 compositions likely to generate very high yields. Models then provide valuable information for the redesign of the experiments and can be18 considered as an essential addition to the evolutionary approach.
Evolutionary experiments for self-assembling amphiphilic systems / M., Forlin; I., Poli; D., DE MARCH; N., Packard; G., Gazzola; Serra, Roberto. - In: CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS. - ISSN 0169-7439. - STAMPA. - 90:2(2008), pp. 153-160. [10.1016/j.chemolab.2007.09.007]
Evolutionary experiments for self-assembling amphiphilic systems
SERRA, Roberto
2008
Abstract
Some amphiphilic molecules in particular environments may self-assemble and originate chemical entities, such as vesicles, which are relevant11 in technological applications. Experimentation in this field is difficult because of the high dimensionality of the search space and the high cost of12 each experiment. To tackle the problem of designing a relatively small number of experiments to achieve the relevant information on the problem,13 we propose an evolutionary design of experiments based on a genetic algorithm. We built a particular algorithm where design and laboratory14 experimentation interact leading the search toward the optimality region of the space. To get insight in the process we then modelled the15 experimental results with different classes of regression models; from modelling we could identify the special role played by some molecules and16 the relevance of their relative weight in the composition. With modelling we “virtually” explored the experimental space and predicted17 compositions likely to generate very high yields. Models then provide valuable information for the redesign of the experiments and can be18 considered as an essential addition to the evolutionary approach.Pubblicazioni consigliate
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