New York University

Aaron Yevick

David G Grier

Mark Hannel

David B Ruffner

Bhaskar Jyoti Krishnatreya

Bhaskar Jyoti Krishnatrya

Systems and methods for uniquely identifying fluid-phase products by endowing them with fingerprints composed of dispersed colloidal particles, and by reading out those fingerprints on demand using Total Holographic Characterization. A library of chemically inert colloidal particles is developed that can be dispersed into soft materials, the stoichiometry of the mixture encoding user-specified information, including information about the host material. Encoded information then can be recovered by high-speed analysis of holographic microscopy images of the dispersed particles. Specifically, holograms of individual colloidal spheres are analyzed with predictions of the theory of light scattering to measure each sphere's radius and refractive index, thereby building up the distribution of particle properties one particle at a time. A complete analysis of a colloidal fingerprint requires several thousand single-particle holograms and can be completed in ten minutes.

Colloidal fingerprints for soft materials using total holographic characterization

A method and system for identification of holographic tracking and identification of features of an object. A holograph is created from scattering off the object, intensity gradients are established for a plurality of pixels in the holograms, the direction of the intensity gradient is determined and those directions analyzed to identify features of the object and enables tracking of the object. Machine learning devices can be trained to estimate particle properties from holographic information.

Fast feature identification for holographic tracking and characterization of colloidal particles

Holograms of colloidal dispersions encode comprehensive information about individual particles' three-dimensional positions, sizes and optical properties. Extracting that information typically is computation-ally intensive, and thus slow. Machine-learning techniques based on support vector machines (SVMs) can analyze holographic video microscopy data in real time on low-power computers. The resulting stream of precise particle-resolved tracking and characterization data provides unparalleled insights into the composition and dynamics of colloidal dispersions and enables applications ranging from basic research to process control and quality assurance.

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New York, NY, United States of America

Aaron Yevick