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Machine Learning Combined with Spectral Imaging for Inferring the Toxicity of Micro- and Nanoplastics

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Machine Learning Combined with Spectral Imaging for Inferring the Toxicity of Micro- and Nanoplastics The project aims to advance our understanding of potential risks posed by micro- and nanoplastics (MNPs) to human gastrointestinal health through a combination of quantitative, experimental, and computational approaches, leveraging powerful machine learning (ML) algorithms and versatile spectral imaging techniques. Towards this goal, the project will first deliver a framework to extensively characterise MNPs using multiple spectral imaging techniques covering from micro- to nanoscale coupled with complementary instruments. The fused characterisation data will be combined with experimental in vitro bioassays to develop ML models, enabling the prediction of toxicity patterns and unveiling key drivers of MNP toxicity. Harnessing the broad literature data, a knowledge-based deep learning app

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recordType

award

status

SIGNED

region

EU

value

1499949

unit

EUR