Speaker
Description
Nanoplastics (NP) are ubiquitous, and their interactions with agricultural and food systems are shown to be associated with health concerns for plants, animals and humans. However, fast and accurate detection and characterization of NP in biosystems remains a challenge; and what governs the interactions between NP and biosystems are still largely unknown. Data driven techniques utilizing AI/ML as tools can greatly help with finding the “missing link” in today’s knowledge of NP: what factors matter in determining the characteristics of how NP interact with biomolecules? Using such knowledge, sensor platforms can be developed that support fast detection of NPs. One such platform is presented in this talk utilizing velocity profile analysis of lateral flow in multi-channel microfluidic sensors to detect and characterize NP in samples with high throughput and fidelity, yet easy to use and field-deployable. Furthermore, machine learning (ML)-enabled data analysis is used to understand NP-molecular interactions which enables material discovery for better design and development of paper-based microfluidic chips (pMFC) for NP detection with high-throughput and fidelity. The data-driven techniques will help solving problems associated with the presence of nanoplastics in various systems.
