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Machine learning-designed bifunctional nanoprobes enable self-calibrated spatiotemporal tracking of nanoplastics
Summary
Scientists have long struggled to accurately track tiny plastic particles (nanoplastics) as they move through living organisms, because current tracking methods lose accuracy over time. Using a new AI-designed tracking tool that combines light-based imaging with a metal marker for built-in correction, researchers found that these plastic particles build up in cells, stick around longer, and even pass to offspring more than previously thought. This suggests that past studies may have significantly underestimated how much nanoplastic exposure and risk we're actually dealing with, an important wake-up call as these particles are increasingly found in our food, water, and bodies.
Nanoplastics (NPs) are pervasive environmental contaminants that can cross biological barriers and accumulate in living organisms, yet their analysis is limited by a trade-off between spatial resolution and quantitative accuracy, hindering long-term studies in complex systems. Here we present a machine learning-guided strategy to design bifunctional, internally calibrated nanoprobes that integrate fluorescence imaging with metal-based quantification. By combining aggregation-induced emission fluorescence with stable metal dopants, these probes establish a dual-signal system in which a time-invariant metal signal dynamically corrects fluorescence decay. This enables a four-dimensional quantitative framework that links fluorescence, metal, time and concentration. We show that the nanoprobes achieve >90% recovery in complex matrices and markedly improve measurement accuracy, revealing higher cellular uptake, long-term retention and transgenerational transfer of NPs than detected by fluorescence alone. These findings establish a generalizable platform for quantitative nano–bio interactions and suggest that current assessments may underestimate NP risks in environmental and biological systems. A machine learning-designed bifunctional nanoprobe integrates fluorescence imaging with metal calibration to enable accurate, long-term spatiotemporal tracking of nanoplastics, revealing underestimated accumulation, retention and transgenerational transfer in complex living biological systems.