Gavin Moore
Graduate Student
← Research Overview
Research Topic
Microplastics & AI for Polymer Research
Field-informed spectroscopy, machine learning, and environmental analysis for identifying microplastic particles and interpreting polymer data.
Identifying Particles in Complex Samples
Microplastics research combines environmental sampling, microFTIR focal plane array imaging, and computational classification to identify microplastic particles in environmental and biological contexts such as sediments and tissues.
These measurements feed directly into the machine-learning workflows described below, pairing field-informed sampling with data-driven classification of particles in complex samples.
Data-Guided Polymer and Particle Analysis
AI for polymer research uses computational tools to support pattern recognition, spectral classification, and more efficient interpretation of polymer and microplastic data.
- Machine learning for spectral classification
- Data workflows for polymer characterization
Topic Team
People Working in Microplastics & AI
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Justin Smolen
Assistant Director and Research Specialist, Laboratory for Synthetic-Biologic Interactions