Grantee Research Project Results
Final Report: Cost-Effective, Portable and Automated Platform for Microplastics Characterization
EPA Contract Number: 68HERC20C0020Title: Cost-Effective, Portable and Automated Platform for Microplastics Characterization
Investigators: Batalin, Maxim
Small Business: Lucendi, Inc.
EPA Contact: Richards, April
Phase: I
Project Period: March 1, 2020 through August 31, 2020
Project Amount: $99,996
RFA: Small Business Innovation Research (SBIR) - Phase I (2020) RFA Text | Recipients Lists
Research Category: Small Business Innovation Research (SBIR) , SBIR - Clean and Safe Water
Description:
Our society generates large quantities of plastic waste, which has quickly become one of the most significant environmental pollutants, especially due to its long bio-degradation lifecycle. Large quantities of plastic waste find its way into aquatic environments, including lakes, rivers, underground streams and oceans. Every year over 8 million tons of plastic flows into the ocean causing an estimated $13 Billion damage to marine ecosystems. Microplastic particles (plastic particles and debris with diameters of less than 5mm) constitute by far the largest class of plastic pollution in the open ocean with recent estimates accounting for as much as 90% of all plastic litter.
In 2017, the EPA Trash Free Waters program has conducted a Microplastics Expert Workshop focusing on prioritizing scientific goals and objectives required to understand the harmful impacts of microplastics to human and ecological health. This report concludes with a summary including the identified method needs. Some of such key sought after methods include:
− Methods for microplastics characterization, i.e. by size, shape and chemical composition;
− Methods for microplastics quantification, particularly for particles in the microns scale (i.e. ≥1 µm and ≤1 mm in size) for which information is limited and which are relevant to human and ecological exposure risks.
Because microplastics objects are small, they are difficult to detect, characterize and quantify in water samples, let alone in the open aquatic environment conditions. Generally, multiple steps are required to first find micro plastic particles, isolate them for analysis, identify and classify in terms of the type of plastics or its chemical composition. For particles smaller than 1mm, different types of microscopy characterization is typically used (e.g. stereo microscopy, Scanning Electron Microscopy, etc.). Once the microplastic particles are detected and counted, the specific type of plastic they are made of can be identified with the use of spectroscopy, atomic force microscopy or fluorescence microscopy methods. However, all of these techniques are extremely low throughput, laborious, expensive, and require expertise to perform and operate. Furthermore, such devices are bulky and not portable, limiting their application to analyzing samples in the laboratory conditions. This creates significant limitations to the use of this microplastics characterization technology and to the types of applications or contexts in which they can be applied.
Summary/Accomplishments (Outputs/Outcomes):
In this EPA SBIR Phase I program we have developed aqusens-MP: a cost-effective high-throughput platform for automated identification and characterization of microplastics. The proposed platform is based on an aqusens platform prototype we have developed in an effort parallel with this program. The aqusens-MP is a color in-line holographic imaging flow cytometer that is capable to automatically detect and provide color phase contrast images inside a continuously flowing water sample at a throughput of ~100ml/h and above. The system is designed to be field portable, cost effective (under $1000 in high volume), and requires minimum maintenance. However, unlike aqusens, in this Phase I program the aqusens-MP platform was integrated with an on-board embedded and cost effective GPU-based computing platform, i.e. NVIDIA Jetson Xavier. This innovation is one of the key elements of this program and has involved a substantial revision of the system's software architecture, required system tuning and overall integration. This reimagined design will allow aqusens-MP to be deployed independently of user's computational infrastructure, will be able to support long-term unattended operations and will further reduce the overall cost.
Another focus of the program has been on enhancing image processing and automated identification capabilities of the platform. We have significantly advanced capabilities of aqusens-MP to accurately identify microobjects in flowing water samples and to characterize them by various features, such as size, morphology and color. Furthermore, we have started our work on developing innovative features based on phase information aqusens-MP collects. These phase metrics are used to further differentiate and characterize microplastic from other particles in the water. As another approach for identifying microplastic particles, we have applied and trained a convolutional neural network aimed at performing automated classification of particles, including microplastics vs. organic particles. Our early results demonstrate capabilities to differentiate microplastics from other objects, such as algae, and pave the way for extended R&D work in this direction in Phase II.
Conclusions:
We have performed initial experimental evaluation demonstrating ability of the aqusens-MP prototype to perform continuous monitoring of water samples and processing of the data on the integrated embedded computing platform. Furthermore, we have successfully performed experiments to demonstrate platform's ability to identify microobjects and accurately size them. We have also demonstrated performance of the trained neural network to accurately classifying algae objects from microplastic samples and other plastic samples collected by our academic colleagues in Hawaii. Finally, we have also demonstrated potential phase metrics that can be extracted and how they can be used to differentiate plastic particles from others, such as algae.
These results have proven the feasibility of the platform to accomplish the tasks of Phase I and have paved the way for completing the final product prototype development in Phase II. During Phase I we have also initiated contacts with prospective industrial partners.
SBIR Phase II:
Cost-effective, portable and automated platform for microplastics characterization | Final ReportThe perspectives, information and conclusions conveyed in research project abstracts, progress reports, final reports, journal abstracts and journal publications convey the viewpoints of the principal investigator and may not represent the views and policies of ORD and EPA. Conclusions drawn by the principal investigators have not been reviewed by the Agency.