Some amphiphilic molecules in particular environments may self-assemble and originate chemical entities, such as vesicles, which are relevant in technological applications. Experimentation in this field is difficult because of the high dimensionality of the search space and the high cost of each experiment. To tackle the problem of designing a relatively small number of experiments to achieve the relevant information on the problem, we propose an evolutionary design of experiments based on a genetic algorithm. We built a particular algorithm where design and laboratory experimentation interact leading the search toward the optimality region of the space. To get insight in the process we then modelled the experimental results with different classes of regression models; from modelling we could identify the special role played by some molecules and the relevance of their relative weight in the composition. With modelling we “virtually” explored the experimental space and predicted compositions likely to generate very high yields. Models then provide valuable information for the redesign of the experiments and can be considered as an essential addition to the evolutionary approach.
|Data di pubblicazione:||2008|
|Titolo:||Evolutionary experiments for self-assembling amphiphilic systems|
|Rivista:||CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS|
|Appare nelle tipologie:||2.1 Articolo su rivista |
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