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Article
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AI-assigned paper type based on the abstract. Classification may not be perfect — flag errors using the feedback button.
Tier 2
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Original research — experimental, observational, or case-control study. Direct primary evidence.
Detection Methods
Environmental Sources
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Mid-infrared spectroscopy and machine learning for postconsumer plastics recycling
Environmental Science Advances
2023
21 citations
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Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Score: 45
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0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Nicholas Stavinski,
Vaishali Maheshkar,
Sinai Thomas,
Karthik Dantu,
Luis Velarde
Summary
Mid-infrared spectroscopy combined with machine learning was developed to sort and identify postconsumer plastics, aiming to prevent contamination and improve recycling stream purity. The approach could help close material loops and reduce the volume of plastic ultimately entering the environment.
Machine learning of the mid-infrared spectra of postconsumer plastics will help prevent, separate, and purify wastestreams contributing to global pollution.