Parameter-Free Determination of Au Nanorod Dimensions Using Depolarized DLS and Genetic Optimization

Gold nanorods (AuNRs) have received considerable attention for their distinctive optical properties and well-defined, low-polydispersity dimensions. These characteristics position them as promising candidates for diverse applications in imaging, sensing, and treating diseases. However, accurate characterization of AuNRs in their native solution state, which is crucial to many applications, presents many challenges─especially if AuNRs are coated with surface layers (e.g., surfactants or grafted polymers). When applied to AuNRs with functionalized surfaces, common techniques such as transmission electron microscopy (TEM), small-angle scattering, and dynamic light scattering (DLS) can present limitations such as small sample sizes, the inability to detect light elements, a lack of a comprehensive analytical framework, and/or a dependence on a priori information about the particle dimensions. In this work, we focus on multiangle depolarized DLS (DDLS) measurements of three distinct, surfactant-coated AuNRs samples in solution. DDLS data was analyzed using two analytical approaches and compared with a genetic algorithm analysis that optimizes the dimensions of the particles to best match relaxation rates obtained from DDLS. For samples that produced high-quality DDLS data, all three approaches yielded length estimates that were highly consistent (within 10–20%) with dimensions obtained from TEM/SEM. In contrast, noisy DDLS data posed challenges for direct analysis, and the genetic algorithm approach emerged as particularly advantageous, providing dimensions that more closely aligned with TEM/SEM values than the analytical methods. Our results suggest that the genetic algorithm can accurately capture the dimensions of the AuNRs from their rotational and translational relaxation rates alone, without the need for additional information (e.g., aspect ratio). Looking to the future, this approach to analyzing DDLS measurements will allow the technique to capture important structural information on more complex, anisotropic nanoparticle systems to enable their use in a wide range of applications.