2D companion to the 3D weathering-chamber model: the identical stochastic fragmentation cascade — a microplastic particle embrittles under UV photo-oxidation and mechanical stress until it cracks into smaller daughters — read off a flat particle field, a log-binned size-distribution histogram and a running surface-area trace instead of a rotating 3D scene.
accumulated dose: D(t) = k_polymer · UV · t
fragmentation rate: λ(r) = λ0 · D + λ_mech · (r_ref / r)
per-step probability: P = 1 − exp(−λ(r) · Δt)
on fragmentation, area is conserved (2D disc analogue):
parent area A splits into N ∈ {2,3,4} daughters
each daughter gets a random area share fᵢ, Σfᵢ = 1
new radius rᵢ = r · √fᵢ
- UV intensity — scales the photo-oxidation dose rate D(t); higher dose embrittles polymer chains faster, raising λ.
- Polymer type — sets k_polymer: PET fibers crack fastest, PE resists oxidation longest, matching real weathering-chamber measurements.
- Mechanical abrasion — an independent term λ_mech that scales as 1/r, since surf and sediment grinding disproportionately shatters particles that are already thin/small.
- Nanoplastic fraction — the share of particles below 1 µm diameter, the size class that can cross cell membranes and is hardest to filter or detect.
- Perimeter (2D surface proxy) — conserved area but shrinking radius means total perimeter grows every fragmentation event, the 2D analogue of why nanoplastics leach additives and adsorb pollutants far faster than the parent particle.
- Size-distribution histogram — log-binned particle diameters, redrawn live; watch the distribution's mode march from micron to nanometre scale as the cascade proceeds.
This is a simplified but mechanistically real model of the process the article's "Origins" section describes as "degradation of microplastic" — the same cascade behind Py-GC/MS and Raman/FTIR analysis workflows used to characterise environmental nanoplastic samples.