Computational Lens-free Holography for Rapid Monitoring and Characterization of Airborne Particles

Air pollution is a major environmental risk to human health, and air quality has become an increasing concern in the industrialized world. Rapid and accurate detection and characterization of airborne particulates is crucial for monitoring and improving air quality. In this proposal, we develop a compact, cost-effective, computational lens-free holography platform for high-throughput characterization of airborne particulates. Such a platform can be easily constructed by using a partial-coherent light source and an aperture for the illumination of the particle samples, and an optoelectronic sensor array for registering the hologram of each particle. The hologram contains both the phase and amplitude information, which represents the fingerprint signatures of particle including the size, shape, refractive index, etc. By developing a sophisticated algorithm, we can reconstruct the microscopic image of each particle and, at the same time, obtain particulate statistics including size, count, shape, and distribution. TO BE CONT’D

Faculty Supervisor:

Shuo Tang

Student:

Jingwen Li

Partner:

Nanozen Inc

Discipline:

Engineering - computer / electrical

Sector:

Automotive and transportation

University:

Program: