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For two observed layers $z_2>z_1$, define the positive logarithmic count slopeSince $k_B=R/N_A$, where $R$ is the molar gas constant, the sedimentation scale height givesSeveral heights check whether one slope fits the whole profile. A mixture of grain radii generally gives a sum of exponential functions, with the larger grains depleted faster aloft; one apparent radius need not describe every height.

An independent readout uses horizontal displacements at lag $\tau$, avoiding the vertical gravitational drift. Define the one-coordinate mean squared displacement $M_x(\tau )=⟨[x(t+\tau )-x(t){]}^2⟩$. In a stationary liquid without horizontal advection, translational diffusion predictsA camera sees two horizontal coordinates, so its squared radial displacement has twice the one-coordinate mean. Neither observable is path length or instantaneous velocity. The bulk Stokes–Einstein relation, with dynamic viscosity $\eta$, then gives

The 1926 lecture compares displacement-based and equilibrium determinations of the Avogadro constant.^[ref]

Rotational diffusion supplies a further observable when an identifiable grain orientation can be followed; centre positions alone cannot measure it.

For the chosen model above and $\eta =1.00\times 10^{-3}\mathrm{Pa}\mathrm{s}$, the bulk prediction is $D\simeq 0.8731\mu {\mathrm{m}}^2{\mathrm{s}}^{-1}$. At $\tau =10\mathrm{s}$, $M_x\simeq 17.46\mu {\mathrm{m}}^2$ and the planar value is $34.92\mu {\mathrm{m}}^2$. Reconstructing $N_A$ from these stipulated model outputs returns $6.02214076\times 10^{23}\mathrm{mo}{\mathrm{l}}^{-1}$ by construction; this arithmetic consistency check is not experimental confirmation. Historical force comes from comparing separately measured profiles and displacements using independently checked material parameters.

The diffusive flux and gravitational particle flux provide another useful connection. With constant drag coefficient $\zeta$, zero vertical net flux readswhere $v_s$ is the downward settling velocity magnitude. With $\zeta =6\pi \eta a$, the chosen model gives $v_s\simeq 0.02725\mu \mathrm{m}{\mathrm{s}}^{-1}$. The equilibrium profile forgets how rapidly it was established, while displacement measurements retain that rate information.

Dialysis exchanges small solutes between a sample and an external bath through a semipermeable membrane, while retaining suitably large macromolecules, such as proteins. The transport is passive diffusion: random motion carries permeant molecules both ways, with net transport down their concentration gradient. The bath is the dialysate. At equal permeant solute concentrations, microscopic exchange continues but net exchange stops.^[ref]

For example, a protein sample containing excess sodium chloride can lose the small ions to a low-salt dialysate without deliberately transferring the protein. Conversely, components of a new buffer solution can enter from the bath. This is useful before studying enzyme kinetics: changing the surrounding chemical solution can remove an interfering small solute while keeping the enzyme available for the next experiment.

The striking feature is that the retained protein does not have to be captured and released again to change its surroundings. However, retention is conditional on the semipermeable membrane and the particular macromolecule, and dialysis is poorly suited to separating proteins of nearly the same size. The molecular weight cut-off is a nominal retention rating, not a sharp geometrical boundary.

Diffusion is net transport associated with random microscopic motion. A localized patch of a dilute solute can spread through a stationary solvent: individual molecules move in both directions, but more cross from the crowded side of an imaginary surface than from the less crowded side. This imbalance gives a net diffusive flux down the concentration gradient; the molecules do not need to sense which direction has lower concentration. At uniform concentration, microscopic exchanges continue even though the net diffusive flux vanishes. ^[ref]

The distinction from advection is easiest to see in a dye patch. Uniform bulk motion carries its centre downstream; diffusion broadens it relative to that centre. Both can happen together. A moving or spreading patch by itself does not identify the mechanism: a nonuniform velocity field can also stretch a patch. ^[ref]

For a dilute nonreacting solute in a uniform medium at fixed temperature, Fick's first law models the relative diffusive flux as $\mathbf{j}=-D\nabla C$, with scalar diffusion coefficient $D>0$ of units ${\mathrm{m}}^2{\mathrm{s}}^{-1}$. Here $C$ is the amount of substance per unit volume, so $\mathbf{j}$ has units $\mathrm{mol}{\mathrm{m}}^{-2}{\mathrm{s}}^{-1}$. With carrier velocity field $\mathbf{u}$, the total flux is $C\mathbf{u}+\mathbf{j}$. The continuity equation, with no sources or chemical reactions, givesFor constant $D$ and incompressible flow, this reduces to $C_t+\mathbf{u}\cdot \nabla C=D{\nabla }^{2}C$, where ${\nabla }^2$ is the Laplacian. In a stationary carrier it is the diffusion equation. These assumptions matter: variable $D$ must remain inside the divergence, and changing sources require extra terms. ^[ref]

The useful surprise is the common mathematics: thermal conduction smooths temperature through the analogous heat equation, while molecular diffusion smooths composition. This analogy already motivated the original formulation of Fick's laws. ^[ref] However, the thermal diffusivity and the solutal diffusivity need not be equal. A diffusive time scale $t_D\sim L^2/D$ means that crossing ten times the distance requires roughly one hundred times the time. This is why molecular diffusion is especially effective across thin layers, and why chaotic advection can assist mixing by making those layers thinner. Physical diffusion is a transport mechanism, distinct from the broader probabilistic class of Markov diffusion.

For positive diffusivity $D(x)$, a diffusion equation with potential-driven drift has diffusive flux $J=-D(c_x+\beta V^{\prime }c)$. On a finite interval with zero flux at both endpoints, the total amount is conserved. The substitution $c=e^{-\lambda t}{e}^{-\beta V}y$ converts the decay eigenvalue problem to the Sturm-Liouville problemThe Rayleigh quotient is $\lambda =\int p|y^{\prime }{|}^2/\int w|y{|}^2\ge 0$. Its zero eigenfunction is constant, giving the stationary solution $c_{\mathrm{eq}}=C{e}^{-\beta V}$. This is a spatial Boltzmann distribution; $D$ changes relaxation rates but cancels out of the zero-flux equilibrium profile. For a sufficiently regular positive $D$ and bounded $V$, the constant eigenfunction is the unique zero mode up to scale.

Fick's laws describe diffusion through a flux law and conservation of mass. The first law says that diffusive flux points down the concentration gradient:Combining it with local conservation gives
$C_t=\nabla \cdot (D(C)\nabla C)$.

Take $x=0$ at chamber A and $x=L$ at chamber B. At steady state, the diffusion equation with constant positive diffusion coefficient givesThe two Dirichlet boundary conditions give $A=c_0$ and $B=-c_0/L$. ThusThe concentration gradient is constant, $dc/dx=-c_0/L$. By Fick's first law, the diffusive flux is $J=-Ddc/dx=D{c}_0/L$, directed from A towards B. The profile is linear independently of $D$, while the flux and the time needed to approach it depend on $D$.

This result assumes no significant bulk flow, chemoattractant loss to the walls, degradation or cellular uptake. Experimentally, a uniform-concentration control with matched reservoir concentrations tests for directional cell migration caused by flow or apparatus asymmetry; reversing the reservoir concentrations tests whether the directional response follows the chemical gradient. Keeping both reservoirs well mixed and measuring the concentration profile checks the assumed boundary concentrations and whether uptake has distorted it. These controls distinguish the assumed passive diffusion field from other directional cues; the derived profile alone does not establish the mechanism of chemotaxis.

The diffusive flux is $\mathbf{J}=-D\nabla C$ by Fick's first law. Conservation in any fixed control volume $V$ gives $\frac{d}{dt}{\int }_{V}C=-{\int }_{\partial V}\mathbf{J}\cdot \mathbf{n}+{\int }_{V}F$. The divergence theorem and arbitrariness of $V$ yieldAfter the initial point injection, $F=0$ and $D=kC$ with $k>0$, so radial symmetry gives $C_t=k{r}^{-2}(r^{2}C{C}_r{)}_r$. Total mass is $4\pi {\int }_0^{\infty }{r}^{2}Cdr=4\pi M$.

A spreading radius $R$ and typical concentration obey $C{R}^3\sim M$ and $R^2\sim kCt$. Thus $R^5\sim Mkt$. In the proposed ansatz this givesDirect substitution, including the time derivative of $\xi$, givesMultiplying by ${\xi }^2$ makes the left side $-({\xi }^{3}f{)}^{\prime }/5$. Regularity and zero radial flux at the origin set the integration constant to zero, so $f{f}^{\prime }=-\xi f/5$. On the positive region $f^{\prime }=-\xi /5$. Nonnegativity, compact support and continuity at its edge giveIts normalization is $1={\int }_0^{{\xi }_0}{\xi }^{2}fd\xi ={\xi }_0^5/75$. Therefore the spherical nonlinear-diffusion source profile isThe concentration and flux vanish at the free boundary, so there is no distributional flux jump despite the derivative kink. The solution satisfies the diffusion equation weakly across that boundary and classically inside its positive region. Its support shrinks to the origin as $t\downarrow 0$, and testing against a continuous compactly supported function gives the limit $4\pi M$ times its value at zero. This verifies the prescribed point injection as well as the mass normalization.

The equation is a conservation law $n_t+J_x=0$ with diffusive flux $J=-D(n_0/n)n_x$. For positive density, insects move down the density gradient, with effective diffusion coefficient $D{n}_0/n$: dispersal is stronger in low-density regions. This is logarithmic diffusion, since the equation is $n_t=D{n}_0(\log n{)}_{xx}$.

Put $\tau =Dt$ and $\xi =x/{\tau }^{\beta }$. A self-similar solution $n=n_0{\tau }^{-\beta }g(\xi )$ conserves total number when ${\int }_{\mathbb{R}}g=1$. Its width shrinks to zero as $t\downarrow 0$; for an integrable nonnegative $g$, changing variables in the integral against a bounded continuous function proves convergence to $n_0$ times the Dirac delta distribution at the origin.

Substitution givesMatching powers for a nonstationary profile forces $\beta =1$. The profile equation integrates to $g^{\prime }/g=-\xi g+C$. For a centred even profile, $C=0$, and integration of $g^{\prime }=-\xi g^2$ gives $g=2/({\xi }^2+a^2)$. Normalization requires $2\pi /a=1$, henceThis is $n_0$ times a Cauchy distribution density with scale $2\pi Dt$. It is positive, has total mass $n_0$, and directly satisfies the nonlinear diffusion equation for $t>0$.

Substitution into the diffusive flux givesThe continuity equation for this drift-diffusion equation with a potential therefore reduces, after cancelling the nonzero exponential, toThe reflecting boundary conditions become $D(u^{\prime }+\beta V^{\prime }u)=0$ at both endpoints. Thus each solution of this eigenvalue problem supplies a separated solution of the original diffusion equation.

For sufficiently smooth functions satisfying the same boundary conditions, integration by parts gives the boundary formThe stated endpoint relation annihilates this expression. This proves the required self-adjoint differential operator identity; for the usual regular Sturm-Liouville problem, separated real boundary conditions give a self-adjoint realization on its domain.

In this problem the transformed diffusive flux is $J=-e^{-\lambda t}p{y}^{\prime }$. Thus the physical boundary conditions areequivalently $y^{\prime }(\pm L)=0$ when the endpoint values of $p$ are positive. In particular $p{f}^{\prime }=p{g}^{\prime }=0$ at the endpoints, which makes the boundary form vanish directly. The inner product used here is the ordinary integral; the weight $w$ enters the generalized eigenvalue problem, not this boundary-form calculation.

For an admissible nonzero real trial function, integration by parts and the zero-flux boundary conditions reduce the Rayleigh quotient toA nonzero constant trial function attains zero. Positivity of $p$ also shows that equality requires $y^{\prime }=0$. HenceIf the conserved total amount is $M={\int }_{-L}^{L}cdx$, the normalized stationary solution isIts diffusive flux vanishes everywhere. The Boltzmann distribution fixes the spatial profile, while the diffusivity affects the relaxation toward it. Boundedness of $V$ makes the normalization finite on this finite interval.

In diffusion-controlled aggregation, particles meet through Brownian motion and stick with probability close to one on encounter. Diffusion-limited cluster aggregation allows already formed clusters to diffuse and join; diffusion-limited aggregation in its usual single-seed model instead adds diffusing individual particles to one growing cluster. First-contact sticking shields inner regions from later arrivals, favouring open branched structures. A reaction-limited cluster aggregation model has a small sticking probability and permits many failed encounters before attachment.

For two dilute spherical species, the Smoluchowski diffusion-controlled encounter rate is $k_{12}=4\pi (D_1+D_2)(a_1+a_2)$. In relative coordinates, solve the steady diffusion equation with an absorbing surface at $R=a_1+a_2$ and concentration $c_{\infty }$ at infinity: $c(r)=c_{\infty }(1-R/r)$. The inward diffusive flux is $4\pi (D_1+D_2)R{c}_{\infty }$, proving the rate coefficient. Interaction barriers and non-spherical aggregates require changes to this ideal encounter calculation.

Cluster growth can be tested by microscopy and light scattering. A mass fractal dimension $d_f$ relates particle number to cluster size through $N\propto R^{d_f}$; a scattering regime between the monomer and cluster length scales can reveal that scaling. The measured morphology depends on whether whole clusters move, on the sticking rule, and on possible bond rearrangement.