SOURCE-LINKED INTELLIGENCE
In situ monitoring of the toxicological evolution of nanoplastics in living organisms
ring intracellular heterogeneity. Meanwhile, the toxicological concentration of NPLs and the type and concentration of toxicological markers will be efficiently matched, screened and quantified using machine learning, which can provide a phased and hierarchical understanding of the mechanism of toxicity evolution of NPLs in individual cells, and thus provide accurate references for the prediction of and early warning on the toxic effects of NPLs on living organisms. The development of this interdisciplinary technique is expected to not only broaden the understanding of the hazards of NPLs and reveal the evolutionary process of toxicity caused by NPLs, but will also provide an analytical platform for toxicological assessment of other types of pollutants. Moreover, it is promising to promote the commercialization of a portable device for the rapid detection of NPLs in water samples, which will provide a powerful tool for environmental protection and household water quality monitoring. Electrochemical sensing, Living organism, Toxicological concentration, Nanoplastics, Toxicological mar
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- recordType
- award
- status
- SIGNED
- region
- EU
- value
- 203464.32
- unit
- EUR
Evidence & attribution
European Commission, CORDIS Horizon Europe project dataset. Metadata adapted.
License: CORDIS reuse policy
First collected: 2026-09-20T02:21:08.944Z. This is not the publication date.