Rapid Microplastic Analysis by Microparticle Radars

Project Information

RAMP-UP

Grant agreement ID: 101113438

DOI

10.3030/101113438

Project closed

EC signature date 10 March 2023

Start date 1 August 2023

End date 31 January 2025

Funded under

• European Research Council (ERC)

Total cost

No data

EU contribution

€ 150 000,00

Coordinated by

BILKENT UNIVERSITESI VAKIF
Türkiye

Periodic Reporting for period 1 - RAMP-UP (Rapid Microplastic Analysis by Microparticle Radars)

Reporting period: 2023-08-01 to 2025-01-31

Summary of the context and overall objectives of the project

Microplastics pollution is increasingly seen as a big challenge due to its involvement in a myriad of health problems and its effects on natural wildlife. Water agencies across the globe are progressively implementing screening policies for drinking water to address this issue. However, this is proving to be challenging since the existing monitoring technologies are labor intensive and time-consuming with low throughput. To address the issue, we have developed a new flow-through sensor. The sensor is based on conducting two electronic measurements on single particles as they flow through the active region. By combining the information content in the electronic measurements, we can obtain the permittivity of microparticles in a rapid and flow-through manner. Plastic particles typically occupy a certain region in the permittivity values compared to other common microscale contaminants, allowing for their differentiation. With this technique, we were able to achieve differentiation between different microplastic classes within the critical regime of 10-24 micrometers. The project has also developed a machine learning model to deal with deviations of particles from spherical shape.

Work performed from the beginning of the project to the end of the period covered by the report and main results achieved so far

We have worked on different microparticle samples including microplastics, microglass and cells.

We have recently demonstrated classification between two different microplastic particles (composed of polystyrene and polyethylene) using our system enhanced with 3D electrodes in the sensing region. We analyzed particles in the 14-20 micrometer range which is relevant for microplastic pollution, as these particles are not filtered out by conventional filters in drinking systems. This is the first time a flow-through electronic system was demonstrated to distinguish between different microplastic types.

We have conducted experiments with ellipsoid microparticles as well, which to our knowledge addressed in this manner for the first time. By using these measurement results, we have developed a machine learning model which can predict the shape properties (e.g. major and minor axes lengths) of microparticles using only electronic sensor waveforms. We have aslo conducted multimode measurements on the microparticles, but in our experience conducting only a single mode (single frequency) measurement is already sufficient for classification.

We have conducted several outreach activities, such as participating in the Microplastic Hackathon organized by Merck, getting included in the Plastiverse toolkits, and getting highlighted in national media. We have also contacted several stakeholders to explore the medical, environmental, and business side of this technology.

Progress beyond the state of the art and expected potential impact (including the socio-economic impact and the wider societal implications of the project so far)

We have recently demonstrated classification between two different microplastic particles (composed of polystyrene and polyethylene) using our system enhanced with 3D electrodes in the sensing region. We analyzed particles in the 14-20 micrometer range which is relevant for microplastic pollution, as these particles are not filtered out by conventional filters in drinking systems. This is the first time a flow-through electronic system was demonstrated to distinguish between different microplastic types.

We have also developed a machine-learning based approach where our sensors can determine the shape properties for ellipsoid microparticles. This way we can address the issue of non-ideal particle shape which has been a limiting factor for flow-through microplastic identification.

Microplastic differentiation by electronic flow-through sensor system developed in the project.