3D Nanocapsule Pore Diffusion Simulator

3D Nanocapsule — Pore Diffusion

Pore density 8

Temperature 37 °C

Payload molecules 220

Escaped

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Released

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Mean escape time

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Elapsed

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How it works

Instead of the smooth analytic release curve of a bulk-diffusion model, this view simulates individual drug molecules as independent random walkers (Brownian motion) confined inside the shell. Each molecule wanders until it happens to reach one of the shell's discrete pores — and only then can it cross to the exterior:

Δx = √(2·D(T)·Δt)·N(0,1)   (per axis, per step)
escape if |x_molecule − x_pore| < pore_radius
D(T) = D₀·exp[−Ea/R·(1/T − 1/Tref)]

Pore density — the number of discrete channels through the polymer shell; more pores mean a molecule doing a random walk is statistically more likely to stumble on an exit soon.
Temperature — sets the step size of each molecule's random walk via the same Arrhenius diffusion coefficient as bulk release, so warmer capsules empty faster molecule-by-molecule too.
Environment (pH) — an acidic trigger widens every pore (simulating polymer swelling/degradation), so a molecule near the shell has a much larger capture radius and escapes sooner.
Payload molecules — the population size being tracked; individual escape times vary a lot even though the population-average curve looks smooth, which is the point of the particle view.

Real-world relevance: this stochastic, pore-level picture is what dominates once a shell is thin or sparsely porous — nanoscale pores mean single-molecule escape events, not a continuous gradient, and that discreteness is invisible in a bulk Fickian curve.

Drug molecule Shell pore Escaped (outside)

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