SBIR Phase I: Lensfree computational flow cytometer for portable, fast and automated characterization of aquatic microorganisms and other particles

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project is the development of a cost-effective high-performance platform to monitor and characterize phytoplankton and other microorganisms in water. Some of these aquatic microorganisms may be toxic or even fatal resulting in significant public health concerns (such as recent "red tide" events in Florida, USA) and drastic economic consequences (i.e. deadly effects of harmful algal blooms on the aquaculture industry). The current state of the art in monitoring technology includes laborious and expensive manual sample collection and evaluation using a benchtop microscope or optical digital imaging system. In contrast, the proposed platform will be transformative by enabling low-cost, portable and fast monitoring and automated characterization of aquatic microorganisms. Therefore, this platform will revolutionize monitoring of aquatic microorganisms and, being low cost, it will enable a much wider application of the technology to other markets. Some of these future applications include more efficient biofuels research and development (via algae monitoring), marine biology science and education, general monitoring of particles and pathogens at the water treatment facilities, production algae monitoring. Therefore, the potential societal impact and commercial potential of the proposed technology is transformative. The proposed project aims to develop and evaluate a portable, rapid, durable and environmentally-stable imaging flow-cytometry technology that will automatically monitor microorganisms, such as phytoplankton and pathogens present in the flowing water, and will be capable of specific classification. Existing optics-based flow-cytometry solutions are expensive and not durable for field use. Unlike lab-based flow cytometers or hand-held assays, the proposed system will not rely on reagents or labeling, and therefore will not need an expert/professional, and will keep the evaluated water unchanged. Thus, it can be installed for continual unattended operation and will be much more cost-effective per test due to elimination of an expert's time, costly reagents and fluorophores. Furthermore, the proposed system will also be integrated with a machine learning engine capable of automated identification of microorganisms as well as other micro-objects of interest. Finally, the proposed system will be evaluated on pre-collected samples of local coastal ocean water to determine the presence of three example types of phytoplankton microorganisms and to provide statistical distribution of all of the detected micro-objects. Therefore, the proposed system will be transformative and provide an innovative and unique capability to expediently survey samples of water and automatically characterize identified micro objects. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.