## Abstract

Sedimentation experiments can provide a large amount of information about the composition of a sample, and the properties of each component contained in the sample. To extract the details of the composition and the component properties, experimental data can be described by a mathematical model, which can then be fitted to the data. If the model is nonlinear in the parameters, the parameter adjustments are typically performed by a nonlinear least squares optimization algorithm. For models with many parameters, the error surface of this optimization often becomes very complex, the parameter solution tends to become trapped in a local minimum and the method may fail to converge. We introduce here a stochastic optimization approach for sedimentation velocity experiments utilizing genetic algorithms which is immune to such convergence traps and allows high-resolution fitting of nonlinear multi-component sedimentation models to yield distributions for sedimentation and diffusion coefficients, molecular weights, and partial concentrations.

Original language | English (US) |
---|---|

Pages (from-to) | 33-40 |

Number of pages | 8 |

Journal | Progress in Colloid and Polymer Science |

Volume | 131 |

DOIs | |

State | Published - Dec 1 2006 |

## Keywords

- Analytical ultracentrifugation
- Genetic algorithms
- Sedimentation velocity analysis

## ASJC Scopus subject areas

- Physical and Theoretical Chemistry
- Organic Chemistry
- Polymers and Plastics
- Colloid and Surface Chemistry
- Materials Chemistry