RESUMEN
Holographic particle characterization measures the sizes and compositions of individual colloidal particles dispersed in fluid media and rapidly amasses statistics on the distributions of these properties, even for complex heterogeneous dispersions. This information is useful for analyzing and optimizing protocols for synthesizing colloidal particles. We illustrate how holographic characterization can guide process design through a case study on a particularly versatile model system composed of an aqueous dispersion of micrometer-scale spheres synthesized from the organosilane monomer 3-(trimethoxysilyl)propyl methacrylate.
RESUMEN
Holograms of colloidal particles can be analyzed with the Lorenz-Mie theory of light scattering to measure individual particles' three-dimensional positions with nanometer precision while simultaneously estimating their sizes and refractive indexes. Extracting this wealth of information begins by detecting and localizing features of interest within individual holograms. Conventionally approached with heuristic algorithms, this image analysis problem can be solved faster and more generally with machine-learning techniques. We demonstrate that two popular machine-learning algorithms, cascade classifiers and deep convolutional neural networks (CNN), can solve the feature-localization problem orders of magnitude faster than current state-of-the-art techniques. Our CNN implementation localizes holographic features precisely enough to bootstrap more detailed analyses based on the Lorenz-Mie theory of light scattering. The wavelet-based Haar cascade proves to be less precise, but is so computationally efficient that it creates new opportunities for applications that emphasize speed and low cost. We demonstrate its use as a real-time targeting system for holographic optical trapping.
