Articles | Volume 4, issue 2
https://doi.org/10.5194/ar-4-345-2026
https://doi.org/10.5194/ar-4-345-2026
Research article
 | 
20 Jul 2026
Research article |  | 20 Jul 2026

From seeding to detachment: leveraging deep learning to quantify the transport of tyre wear microplastics in a wind tunnel

Bashir Olasunkanmi Ayinde, Wolfgang Babel, Johannes Olesch, Daniel Wagner, Seema Agarwal, Christian Laforsch, Julian Brehm, Anke Nölscher, and Christoph Karl Thomas

Data sets

Dataset and script used for the publication: From seeding to detachment: leveraging deep learning to quantify the transport of tyre wear microplastics in a wind tunnel Bashir Olasunkanmi Ayinde et al. https://doi.org/10.5281/zenodo.20589718

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Short summary
The dynamics of how tyre wear particles behave prior to their entrainment are still poorly understood. In wind tunnel experiments, the particle detachment from an idealized glass substrate was monitored. For particle sizes above 80 μm, smaller and more rounded particles were mobilized by wind shear first, whereas larger and more angular particles require stronger wind shear, highlighting strong surface adhesion and particle morphology as the major factors influencing microplastic detachment.
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