Rapid Microplastic Analysis by Microparticle Radars

Informazioni relative al progetto

RAMP-UP

ID dell’accordo di sovvenzione: 101113438

DOI

10.3030/101113438

Progetto chiuso

Data della firma CE 10 Marzo 2023

Data di avvio 1 Agosto 2023

Data di completamento 31 Gennaio 2025

Finanziato da

• European Research Council (ERC)

Costo totale

Nessun dato

Contributo UE

€ 150 000,00

Coordinato da

BILKENT UNIVERSITESI VAKIF
Türkiye

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

Periodo di rendicontazione: 2023-08-01 al 2025-01-31

Sintesi del contesto e degli obiettivi generali del progetto

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.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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.