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Original research — experimental, observational, or case-control study. Direct primary evidence.
Environmental Sources
Nanoplastics
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Low-cost, multispectral machine learning classification of simulated airborne micro/nanoplastics
Journal of Hazardous Materials2025
4 citations
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Citation count from OpenAlex, updated daily. May differ slightly from the publisher's own count.
Score: 48
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0–100 AI score estimating relevance to the microplastics field. Papers below 30 are filtered from public browse.
Sang Hee Hong,
Safiyah Abdessalam,
Sang Hee Hong,
Safiyah Abdessalam,
Yansong Tang,
Jeong‐Yeol Yoon
Sang Hee Hong,
Sang Hee Hong,
Darya Pershina,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Darya Pershina,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Safiyah Abdessalam,
Darya Pershina,
Jeong‐Yeol Yoon
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Safiyah Abdessalam,
Darya Pershina,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Jeong‐Yeol Yoon
L. Falk,
L. Falk,
Sang Hee Hong,
Sang Hee Hong,
Un Hyuk Yim,
Sang Hee Hong,
Sang Hee Hong,
Un Hyuk Yim,
Un Hyuk Yim,
Un Hyuk Yim,
Yan Liang,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Jeong‐Yeol Yoon
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Un Hyuk Yim,
Jeong‐Yeol Yoon
Sang Hee Hong,
Un Hyuk Yim,
Sang Hee Hong,
Sang Hee Hong,
Sang Hee Hong,
Jeong‐Yeol Yoon
Summary
Researchers built a low-cost smartphone-based multispectral classification system using XGBoost machine learning to identify airborne micro- and nanoplastics, using real-world cryoground plastic samples rather than commercial microspheres. The system distinguished plastics from clay with 89–99% accuracy across dry and wet sample conditions without requiring morphological analysis, demonstrating an accessible monitoring approach.
This study presents a novel smartphone-based, machine-learning-assisted multispectral classification method for identifying airborne micro- and nanoplastics (MNPs). Instead of commercial polymeric microspheres, coffee grinder-based cryogrinding generated nonuniform MNPs from real-world plastic products with highly irregular shapes and heterogenous size distributions. The low-cost handheld device comprises a smartphone, a spectral mask array made from plastic color films, and a discrete multiplexed illumination device. A stack of images was captured across multiple wavelength ranges, and the RGB ratios were extracted without using morphological information. An XGBoost model was trained on two datasets: dry and wet MNP samples passively collected on a glass slide, simulating two types of airborne MNPs. The model successfully distinguished plastics from clay with 89-99 % accuracy and classified six plastic types with 79-87 % accuracy for dry and wet MNPs. This method offers a promising toolkit for airborne MNP monitoring.