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Cost-effective, portable and automated platform for microplastics characterization

Award Information
Agency: Environmental Protection Agency
Branch: N/A
Contract: 68HERC20C0020
Agency Tracking Number: B191A-0004
Amount: $99,995.61
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 19-NCER-1A
Solicitation Number: 68HERC19R0052
Solicitation Year: 2019
Award Year: 2020
Award Start Date (Proposal Award Date): 2020-03-01
Award End Date (Contract End Date): 2020-08-31
Small Business Information
570 Westwood Plaza, Building 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
 Chief Executive Officer
 (858) 405-8319
Business Contact
 Maxim Batalin
Title: Chief Executive Officer
Phone: (858) 405-8319
Research Institution

An estimated 75% of litter along the shoreline globally is made of plastic. Plastic particles and debris find their way into oceans, rivers, lakes, sediment, and eventually into our bodies. Over 90% of the plastic contamination in the open ocean is attributed to microplastic particles (MP) - i.e. plastic objects with diameter <5mm. MP are even found in up to 94% of tap water in the United States. Due to their small size, it is especially difficult to accurately sample, quantify and characterize MP. Existing solutions to MP identification and characterization are mostly laboratory based and involve laborious, time-consuming processes, expensive equipment and expertise to operate.Lucendi proposes to develop a cost-effective, portable and automated platform for MP characterization, based on our lens-free computational microscopy technology coupled with machine learning and big data algorithms. The proposed effort will leverage Lucendi’s existing platform, which is built in a parallel effort, and will significantly redesign and develop a new platform for the purpose of high-throughput MP sampling, identification and characterization. Our platform will enable high-throughput monitoring (~100ml/hour, user adjustable), capable of identifying and characterizing MPs in a wider dynamic range (4µm – 1mm, can be adjusted), will be built as a portable and robust device aimed for in-field and in-lab applications alike and will be embedded with computational platform enabling it to operate autonomously for long-term unattended deployment scenarios. Furthermore, the proposed platform will be also integrated with a Machine Learning engine capable of automated MP identification and characterization.The proposed platform is envisioned to have wide applicability with primary modifications required in the types of objects it is trained to identify and characterize. Therefore, an initial beachhead market will be in general water monitoring and assessment (an estimated $4.6 billion market), with additional prospects for the platform to be easily extended into aquaculture ($242 billion market) and algae-based bioproducts research and cultivation ($5 billion market). Our initial market research and customer discovery process suggests that the initial users, i.e. first adopters, will likely be from the research and scientific community, with follow on models of the product designed and mass-produced for a wider groups of users.The proposed platform will significantly advance capabilities for cost-effective automated sampling,identification and characterization of MP in liquid samples and will provide opportunities to advance the state of the art and commercial opportunities across multiple applications and industries.

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

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