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High-throughput, cost effective and automated platform for label free monitoring and characterization of algae, their lipid content, and other micro-objects

Award Information
Agency: Department of Energy
Branch: N/A
Contract: DE-SC0019817
Agency Tracking Number: 245799
Amount: $199,961.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 08c
Solicitation Number: DE-FOA-0001941
Solicitation Year: 2019
Award Year: 2019
Award Start Date (Proposal Award Date): 2019-07-01
Award End Date (Contract End Date): 2020-04-30
Small Business Information
570 Westwood Plaza, Bldg 114, Rm 6350
Los Angeles, CA 90095-8352
United States
DUNS: 080369315
HUBZone Owned: No
Woman Owned: No
Socially and Economically Disadvantaged: No
Principal Investigator
 Maxim Batalin
 (858) 405-8319
Business Contact
 Maxim Batalin
Phone: (858) 405-8319
Research Institution

Algae are rapidly growing in importance as an abundant and renewable source of many valuable components that are currently cultivated for applications in biofuels and other bioproducts, such as additives for food, nutrition supplements, pharmaceuticals, chemicals, etc. Growing algae is the largest cost in algal products today, and algae crop loss due to predators or other invasive microorganisms represent one of the major reasons for this cost. This necessitates development of cost-efficient and effective technologies to empower methodologies for algae crop protection, as well as to optimize algae research and cultivation processes for biofuels and other bioproducts applications. An innovative platform is proposed for automated high-throughput monitoring and characterization of microalgae during cultivation process for biofuel and other bioproducts production in-field or for laboratory research. Specifically, the proposed platform will be able to 1) monitor and characterize the cultivated microalgae population with resolution down to individual alga (including classification of different types of algae in case of a polyculture community); 2) automatically estimate in a label-free way the individual alga characteristics, such as shape, size, color and lipid content, empowering precise decision making to adjust cultivation process or to initiate harvesting; 3) empower crop protection methodologies by detecting invasive or predatory microorganisms and provide advanced warning to enable the corresponding risk-mitigation strategies. During Phase I, the following objectives will be accomplished: 1) develop an initial prototype version of the proposed platform adapted for monitoring and characterization of flowing dense samples (samples from microalgae cultivation process), 2) develop deep learning enabled image processing algorithms to characterize identified microalgae and detect contaminating micro-objects to empower crop protection methodologies, 3) determine feasibility of individual algae lipid content estimation via phase measurement, 4) perform proof of concept evaluation of the platform based on samples obtained from algae cultivated in the laboratory and in-field during two deployment campaigns at real-world algae production facilities.The proposed platform will have significant commercial applications for the intended market of algae-based biofuel and bioproducts commercial producers, research laboratories and government organizations. The market demand will be primarily driven by platform’s capabilities to empower crop protection methodologies and, therefore, significantly reducing crop loss. Furthermore, algae characterization capabilities of the platform will empower precise cultivation approaches that are envisioned to further improve the economics of algae cultivation and research. Furthermore, fundamental technology behind the proposed platform will also be applicable to secondary markets, such as identification of harmful algal blooms and other dangerous microorganisms in natural and artificial bodies of water. These capabilities will be valuable for ensuring health and safety of aquaculture operations and public health. Furthermore, low cost of the proposed fundamental technology and high performance characteristics in micro-object detection enable application of the proposed platform to drinking water monitoring and characterization market.

* Information listed above is at the time of submission. *

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