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An example of a situation in which bulk flow and diffusion can be differentiated is the mechanism by which oxygen enters the body during external respiration (breathing). The lungs are located in the thoracic cavity, which is expanded as the first step in external respiration. This expansion leads to an increase in volume of the alveoli in the lungs, which causes a decrease in pressure in the alveoli. This creates a pressure gradient between the air outside the body (relatively high pressure) and the alveoli (relatively low pressure). The air moves down the pressure gradient through the airways of the lungs and into the alveoli until the pressure of the air and that in the alveoli are equal (i.e., the movement of air by bulk flow stops once there is no longer a pressure gradient). The air arriving in the alveoli has a higher concentration of oxygen than the “stale” air in the alveoli. The increase in oxygen concentration creates a concentration gradient for oxygen between the air in the alveoli and the blood in the capillaries that surround the alveoli. Oxygen then moves by diffusion, down the concentration gradient, into the blood. The other consequence of the air arriving in alveoli is that the concentration of carbon dioxide in the alveoli decreases (air has a very low concentration of carbon dioxide compared to the blood in the body). This creates a concentration gradient for carbon dioxide to diffuse from the blood into the alveoli. The blood is then transported around the body by the pumping action of the heart. As the left ventricle of the heart contracts, the volume decreases, which causes the pressure in the ventricle to increase. This creates a pressure gradient between the heart and the capillaries, and blood moves through blood vessels by bulk flow (down the pressure gradient). As the thoracic cavity contracts during expiration, the volume of the alveoli decreases and creates a pressure gradient between the alveoli and the air outside the body, and air moves by bulk flow down the pressure gradient. Diffusion in the context of different disciplines The concept of diffusion is widely used in: physics (particle diffusion), chemistry, biology, sociology, economics, and finance (diffusion of people, ideas and of price values). However, in each case, the object (e.g., atom, idea, etc.) that is undergoing diffusion is “spreading out” from a point or location at which there is a higher concentration of that object. There are two ways to introduce the notion of diffusion: either a phenomenological approach starting with Fick's laws of diffusion and their mathematical consequences, or a physical and atomistic one, by considering the random walk of the diffusing particles.[1] In the phenomenological approach, diffusion is the movement of a substance from a region of high concentration to a region of low concentration without bulk motion. According to Fick's laws, the diffusion flux is proportional to the negative gradient of concentrations. It goes from regions of higher concentration to regions of lower concentration. Some time later, various generalizations of Fick's laws were developed in the frame of thermodynamics and non-equilibrium thermodynamics.[2] From the atomistic point of view, diffusion is considered as a result of the random walk of the diffusing particles. In molecular diffusion, the moving molecules are self-propelled by thermal energy. Random walk of small particles in suspension in a fluid was discovered in 1827 by Robert Brown. The theory of the Brownian motion and the atomistic backgrounds of diffusion were developed by Albert Einstein.[3] The concept of diffusion is typically applied to any subject matter involving random walks in ensembles of individuals. In biology, the terms "net movement" or "net diffusion" are often used when considering the movement of ions or molecules by diffusion. For example, oxygen can diffuse through cell membranes and if there is a higher concentration of oxygen outside the cell than inside, oxygen molecules will diffuse into the cell. However, because the movement of molecules is random, occasionally oxygen molecules will move out of the cell (against the concentration gradient). Because there are more oxygen molecules outside the cell, the probability that oxygen molecules will enter the cell is higher than the probability that oxygen molecules will leave the cell. Therefore, the "net" movement of oxygen molecules (the difference between the number of molecules either entering or leaving the cell) will be into the cell. In other words, there will be a net movement of oxygen molecules down the concentration gradient. Random walk (random motion) File:Random motion.webm The apparent random motion of atoms, ions or molecules explained. Substances appear to move randomly due to collisions with other substances. From the iBook "Cell Membrane Transport", free license granted by IS3D, LLC, 2014. One common misconception is that individual atoms, ions or molecules move “randomly”, which they do not. In the animation on the right, the ion on in the left panel has a “random” motion, but this motion is not random as it is the result of “collisions” with other ions. As such, the movement of a single atom, ion, or molecule within a mixture just appears to be random when viewed in isolation. The movement of a substance within a mixture by “random walk” is governed by the kinetic energy within the system that can be affected by changes in concentration, pressure or temperature. History of diffusion in physics In the scope of time, diffusion in solids was used long before the theory of diffusion was created. For example, Pliny the Elder had previously described the cementation process which produces steel from the element iron (Fe) through carbon diffusion. Another example is well known for many centuries, the diffusion of colours of stained glass or earthenware and Chinese ceramics. In modern science, the first systematic experimental study of diffusion was performed by Thomas Graham. He studied diffusion in gases, and the main phenomenon was described by him in 1831–1833:[4] "...gases of different nature, when brought into contact, do not arrange themselves according to their density, the heaviest undermost, and the lighter uppermost, but they spontaneously diffuse, mutually and equally, through each other, and so remain in the intimate state of mixture for any length of time.” The measurements of Graham contributed to James Clerk Maxwell deriving, in 1867, the coefficient of diffusion for CO2 in air. The error rate is less than 5%. In 1855, Adolf Fick, the 26-year old anatomy demonstrator from Zürich, proposed his law of diffusion. He used Graham's research, stating his goal as "the development of a fundamental law, for the operation of diffusion in a single element of space". He asserted a deep analogy between diffusion and conduction of heat or electricity, creating a formalism that is similar to Fourier's law for heat conduction (1822) and Ohm's law for electrical current (1827). Robert Boyle demonstrated diffusion in solids in the 17th century[5] by penetration of Zinc into a copper coin. Nevertheless, diffusion in solids was not systematically studied till the second part of the 19th century. William Chandler Roberts-Austen, the well-known British metallurgist, and former assistant of Thomas Graham, studied systematically solid state diffusion on the example of gold in lead in 1896. :[6] "... My long connection with Graham's researches made it almost a duty to attempt to extend his work on liquid diffusion to metals." In 1858, Rudolf Clausius introduced the concept of the mean free path. In the same year, James Clerk Maxwell developed the first atomistic theory of transport processes in gases. The modern atomistic theory of diffusion and Brownian motion was developed by Albert Einstein, Marian Smoluchowski and Jean-Baptiste Perrin. Ludwig Boltzmann, in the development of the atomistic backgrounds of the macroscopic transport processes, introduced the Boltzmann equation, which has served mathematics and physics with a source of transport process ideas and concerns for more than 140 years.[7] In 1920–1921 George de Hevesy measured self-diffusion using radioisotopes. He studied self-diffusion of radioactive isotopes of lead in liquid and solid lead. Yakov Frenkel (sometimes, Jakov/Jacov Frenkel) proposed, and elaborated in 1926, the idea of diffusion in crystals through local defects (vacancies and interstitial atoms). He concluded, the diffusion process in condensed matter is an ensemble of elementary jumps and quasichemical interactions of particles and defects. He introduced several mechanisms of diffusion and found rate constants from experimental data. Some time later, Carl Wagner and Walter H. Schottky developed Frenkel's ideas about mechanisms of diffusion further. Presently, it is universally recognized that atomic defects are necessary to mediate diffusion in crystals.[6] Henry Eyring, with co-authors, applied his theory of absolute reaction rates to Frenkel's quasichemical model of diffusion.[8] The analogy between reaction kinetics and diffusion leads to various nonlinear versions of Fick's law.[9] Basic models of diffusion Diffusion flux Each model of diffusion expresses the diffusion flux through concentrations, densities and their derivatives. Flux is a vector \mathbf{J}. The transfer of a physical quantity N through a small area \Delta S with normal \nu per time \Delta t is \Delta N = (\mathbf{J},\nu) \Delta S \Delta t +o(\Delta S \Delta t)\, , where (\mathbf{J},\nu) is the inner product and o(...) is the little-o notation. If we use the notation of vector area \Delta \mathbf{S}=\nu \Delta S then \Delta N = (\mathbf{J}, \Delta \mathbf{S}) \Delta t +o(\Delta \mathbf{S} \Delta t)\, . The dimension of the diffusion flux is [flux]=[quantity]/([time]·[area]). The diffusing physical quantity N may be the number of particles, mass, energy, electric charge, or any other scalar extensive quantity. For its density, n, the diffusion equation has the form \frac{\partial n}{\partial t}= - \nabla \cdot \mathbf{J} +W \, , where W is intensity of any local source of this quantity (the rate of a chemical reaction, for example). For the diffusion equation, the no-flux boundary conditions can be formulated as (\mathbf{J}(x),\nu(x))=0 on the boundary, where \nu is the normal to the boundary at point x. Fick's law and equations Main article: Fick's laws of diffusion Fick's first law: the diffusion flux is proportional to the negative of the concentration gradient: \mathbf{J}=-D \nabla n \ , \;\; J_i=-D \frac{\partial n}{\partial x_i} \ . The corresponding diffusion equation (Fick's second law) is \frac{\partial n(x,t)}{\partial t}=\nabla\cdot( D \nabla n(x,t))=D \Delta n(x,t)\ , where \Delta is the Laplace operator, \Delta n(x,t) = \sum_i \frac{\partial^2 n(x,t)}{\partial x_i^2} \ . Onsager's equations for multicomponent diffusion and thermodiffusion Fick's law describes diffusion of an admixture in a medium. The concentration of this admixture should be small and the gradient of this concentration should be also small. The driving force of diffusion in Fick's law is the antigradient of concentration, -\nabla n. In 1931, Lars Onsager[10] included the multicomponent transport processes in the general context of linear non-equilibrium thermodynamics. For multi-component transport, \mathbf{J}_i=\sum_j L_{ij} X_j \, , where \mathbf{J}_i is the flux of the ith physical quantity (component) and X_j is the jth thermodynamic force. The thermodynamic forces for the transport processes were introduced by Onsager as the space gradients of the derivatives of the entropy density s (he used the term "force" in quotation marks or "driving force"): X_i= {\rm grad} \frac {\partial s(n)}{\partial n_i}\ , where n_i are the "thermodynamic coordinates". For the heat and mass transfer one can take n_0=u (the density of internal energy) and n_i is the concentration of the ith component. The corresponding driving forces are the space vectors X_0= {\rm grad} \frac{1}{T}\ , \;\;\; X_i= - {\rm grad} \frac{\mu_i}{T}\; (i >0) , because {\rm d}s=\frac{1}{T}{\rm d}u-\sum_{i \geq 1}\frac{\mu_i}{T} {\rm d} n_i where T is the absolute temperature and \mu_i is the chemical potential of the ith component. It should be stressed that the separate diffusion equations describe the mixing or mass transport without bulk motion. Therefore, the terms with variation of the total pressure are neglected. It is possible for diffusion of small admixtures and for small gradients. For the linear Onsager equations, we must take the thermodynamic forces in the linear approximation near equilibrium: X_i= \sum_{k \geq 0} \left.\frac{\partial^2 s(n)}{\partial n_i \partial n_k}\right|_{n=n^*} {\rm grad} n_k \ , where the derivatives of s are calculated at equilibrium n*. The matrix of the kinetic coefficients L_{ij} should be symmetric (Onsager reciprocal relations) and positive definite (for the entropy growth). The transport equations are \frac{\partial n_i}{\partial t}= - {\rm div} \mathbf{J}_i =- \sum_{j\geq 0} L_{ij}{\rm div} X_j = \sum_{k\geq 0} \left[-\sum_{j\geq 0} L_{ij} \left.\frac{\partial^2 s(n)}{\partial n_j \partial n_k}\right|_{n=n^*}\right] \Delta n_k\ . Here, all the indexes i, j, k=0,1,2,... are related to the internal energy (0) and various components. The expression in the square brackets is the matrix D_{ik}of the diffusion (i,k>0), thermodiffusion (i>0, k=0 or k>0, i=0) and thermal conductivity (i=k=0) coefficients. Under isothermal conditions T=const. The relevant thermodynamic potential is the free energy (or the free entropy). The thermodynamic driving forces for the isothermal diffusion are antigradients of chemical potentials, -(1/T)\nabla\mu_j, and the matrix of diffusion coefficients is D_{ik}=\frac{1}{T}\sum_{j\geq 1} L_{ij} \left.\frac{\partial \mu_j(n,T)}{ \partial n_k}\right|_{n=n^*} (i,k>0). There is intrinsic arbitrariness in the definition of the thermodynamic forces and kinetic coefficients because they are not measurable separately and only their combinations \sum_j L_{ij}X_j can be measured. For example, in the original work of Onsager[10] the thermodynamic forces include additional multiplier T, whereas in the Course of Theoretical Physics[11] this multiplier is omitted but the sign of the thermodynamic forces is opposite. All these changes are supplemented by the corresponding changes in the coefficients and do not effect the measurable quantities. Nondiagonal diffusion must be nonlinear The formalism of linear irreversible thermodynamics (Onsager) generates the systems of linear diffusion equations in the form \frac{\partial n_i}{\partial t} =\sum_j D_{ij} \Delta c_j \, . If the matrix of diffusion coefficients is diagonal then this system of equations is just a collection of decoupled Fick's equations for various components. Assume that diffusion is non-diagonal, for example, D_{12}\neq 0, and consider the state with c_2= \ldots = c_n=0. At this state, \partial n_2/ \partial t = D_{12} \Delta n_1. If D_{12} \Delta n_1(x) < 0 at some points then n_2(x) becomes negative at these points in a short time. Therefore, linear non-diagonal diffusion does not preserve positivity of concentrations. Non-diagonal equations of multicomponent diffusion must be non-linear.[9] Einstein's mobility and Teorell formula The Einstein relation (kinetic theory) connects the diffusion coefficient and the mobility (the ratio of the particle's terminal drift velocity to an applied force)[12] D = \mu \, k_B T where D is the diffusion constant; μ is the "mobility"; kB is Boltzmann's constant; T is the absolute temperature. Below, to combine in the same formula the chemical potential μ and the mobility, we use for mobility the notation \mathfrak{m}. The mobility—based approach was further applied by T. Teorell.[13] In 1935, he studied the diffusion of ions through a membrane. He formulated the essence of his approach in the formula: the flux is equal to mobility×concentration×force per gram ion. This is the so-called Teorell formula. The force under isothermal conditions consists of two parts: Diffusion force caused by concentration gradient: - RT \frac{1}{n}\nabla n=- RT \nabla (\ln(n/n^{\rm eq})). Electrostatic force caused by electric potential gradient: q \nabla \varphi. Here R is the gas constant, T is the absolute temperature, n is the concentration, the equilibrium concentration is marked by a superscript "eq", q is the charge and φ is the electric potential. The simple but crucial difference between the Teorell formula and the Onsager laws is the concentration factor in the Teorell expression for the flux. In the Einstein – Teorell approach, If for the finite force the concentration tends to zero then the flux also tends to zero, whereas the Onsager equations violate this simple and physically obvious rule. The general formulation of the Teorell formula for non-perfect systems under isothermal conditions is[9] \mathbf{J}=\mathfrak{m} \exp\left(\frac{\mu-\mu_0}{RT}\right)(-\nabla \mu + (\mbox{external force per gram particle}))\, , where μ is the chemical potential, μ0 is the standard value of the chemical potential. The expression a=\exp\left(\frac{\mu-\mu_0}{RT}\right) is the so-called activity. It measures the "effective concentration" of a species in a non-ideal mixture. In this notation, the Teorell formula for the flux has a very simple form[9] \mathbf{J}=\mathfrak{m} a (-\nabla \mu + (\mbox{external force per gram particle}))\, . The standard derivation of the activity includes a normalization factor and for small concentrations a=n/n^{\ominus} + o(n/n^{\ominus}), where n^{\ominus} is the standard concentration. Therefore this formula for the flux describes the flux of the normalized dimensionless quantity, n/n^{\ominus}, \frac{\partial (n/n^{\ominus})}{\partial t}= \nabla \cdot [\mathfrak{m} a (\nabla \mu - (\mbox{external force per gram particle}))] Teorell formula for multicomponent diffusion The Teorell formula with combination of Onsager's definition of the diffusion force gives \mathbf{J}_i=\mathfrak{m_i} a_i \sum_j L_{ij} X_j\, , where \mathfrak{m_i} is the mobility of the ith component, a_i is its activity, L_{ij} is the matrix of the coefficients, X_j is the themodynamic diffusion force, X_j= - {\rm \nabla} \frac{\mu_j}{T} . For the isothermal perfect systems, X_j= - R \frac{{\rm \nabla} n_j}{n_j}. Therefore, the Einstein-Teorell approach gives the following multicomponent generalization of the Fick's law for multicomponent diffusion: \frac{\partial n_i}{\partial t} =\sum_j \nabla \cdot \left(D_{ij}\frac{n_i}{n_j} \nabla n_j\right) \, . where D_{ij} is the matrix of coefficients. The Chapman-Enskog formulas for diffusion in gases include exactly the same terms. Earlier, such terms were introduced in the Maxwell–Stefan diffusion equation. Jumps on the surface and in solids Diffusion in the monolayer: oscillations near temporary equilibrium positions and jumps to the nearest free places. Diffusion of reagents on the surface of a catalyst may play an important role in heterogeneous catalysis. The model of diffusion in the ideal monolayer is based on the jumps of the reagents on the nearest free places. This model was used for CO on Pt oxidation under low gas pressure. The system includes several reagents A_1,A_2,\ldots A_m on the surface. Their surface concentrations are c_1,c_2,\ldots c_m. The surface is a lattice of the adsorption places. Each reagent molecule fills a place on the surface. Some of the places are free. The concentration of the free paces is z=c_0. The sum of all c_i (including free places) is constant, the density of adsorption places b. The jump model gives for the diffusion flux of A_i (i=1,...,n): \mathbf{J}_i=-D_i[z \nabla c_i - c_i \nabla z]\, . The corresponding diffusion equation is:[9] \frac{\partial c_i}{\partial t}=- \mathrm{div}\mathbf{J}_i=D_i[z \Delta c_i - c_i \Delta z] \, . Due to the conservation law, z=b-\sum_{i=1}^n c_i \, , and we have the system of m diffusion equations. For one component we get Fick's law and linear equations because (b-c) \nabla c- c\nabla(b-c)=b\nabla c. For two and more components the equations are nonlinear. If all particles can exchange their positions with their closest neighbours then a simple generalization gives \mathbf{J}_i=-\sum_j D_{ij}[c_j \nabla c_i - c_i \nabla c_j] \frac{\partial c_i}{\partial t}=\sum_j D_{ij}[c_j \Delta c_i - c_i \Delta c_j] where D_{ij} = D_{ji} \geq 0 is a symmetric matrix of coefficients which characterize the intensities of jumps. The free places (vacancies) should be considered as special "particles" with concentration c_0. Various versions of these jump models are also suitable for simple diffusion mechanisms in solids. Diffusion in porous media For diffusion in porous media the basic equations are:[14] \mathbf{J}=- D \nabla n^m \frac{\partial n}{\partial t}=D \Delta n^m \, , where D is the diffusion coefficient, n is the concentration, m>0 (usually m>1, the case m=1 corresponds to Fick's law). For diffusion of gases in porous media this equation is the formalisation of Darcy's law: the velocity of a gas in the porous media is v=-\frac{k}{\mu}\nabla p where k is the permeability of the medium, μ is the viscosity and p is the pressure. The flux J=nv and for p \sim n^{\gamma} Darcy's law gives the equation of diffusion in porous media with m=γ+1. For underground water infiltration the Boussinesq approximation gives the same equation with m=2. For plasma with the high level of radiation the Zeldovich-Raizer equation gives m>4 for the heat transfer. Diffusion in physics Elementary theory of diffusion coefficient in gases Random collisions of particles in a gas. The diffusion coefficient D is the coefficient in the Fick's first law J=- D {\partial n}/{\partial x}, where J is the diffusion flux (amount of substance) per unit area per unit time, n (for ideal mixtures) is the concentration, x is the position [length]. Let us consider two gases with molecules of the same diameter d and mass m (self-diffusion). In this case, the elementary mean free path theory of diffusion gives for the diffusion coefficient D=\frac{1}{3} \ell v_T = \frac{2}{3}\sqrt{\frac{k_{\rm B}^3}{\pi^3 m}}\frac{T^{3/2}}{Pd^2}\, , where kB is the Boltzmann constant, T is the temperature, P is the pressure, \ell is the mean free path, and vT is the mean thermal speed: \ell = \frac{k_{\rm B}T}{\sqrt 2 \pi d^2 P}\, , \;\;\; v_T=\sqrt{\frac{8k_{\rm B}T}{\pi m}}\, . We can see that the diffusion coefficient in the mean free path approximation grows with T as T3/2 and decreases with P as 1/P. If we use for P the ideal gas law P=RnT with the total concentration n, then we can see that for given concentration n the diffusion coefficient grows with T as T1/2 and for given temperature it decreases with the total concentration as 1/n. For two different gases, A and B, with molecular masses mA, mB and molecular diameters dA, dB, the mean free path estimate of the diffusion coefficient of A in B and B in A is: D_{\rm AB}=\frac{2}{3}\sqrt{\frac{k_{\rm B}^3}{\pi^3}}\sqrt{\frac{1}{2m_{\rm A}}+\frac{1}{2m_{\rm B}}}\frac{4T^{3/2}}{P(d_{\rm A}+d_{\rm B})^2}\, , The theory of diffusion in gases based on Boltzmann's equation In Boltzmann's kinetics of the mixture of gases, each gas has its own distribution function, f_i(x,c,t), where t is the time moment, x is position and c is velocity of molecule of the ith component of the mixture. Each component has its mean velocity C_i(x,t)=\frac{1}{n_i}\int_c c f(x,c,t) \, dc. If the velocities C_i(x,t) do not coincide then there exists diffusion. In the Chapman-Enskog approximation, all the distribution functions are expressed through the densities of the conserved quantities:[7] individual concentrations of particles, n_i(x,t)=\int_c f_i(x,c,t)\, dc (particles per volume), density of moment \sum_i m_i n_i C_i(x,t) (mi is the ith particle mass), density of kinetic energy \sum_i \left( n_i\frac{m_i C^2_i(x,t)}{2} + \int_c \frac{m_i (c_i-C_i(x,t))^2}{2} f_i(x,c,t)\, dc \right). The kinetic temperature T and pressure P are defined in 3D space as \frac{3}{2}k_{\rm B}T=\frac{1}{n} \int_c \frac{m_i (c_i-C_i(x,t))^2}{2} f_i(x,c,t)\, dc; P=k_{\rm B}nT, where n=\sum_i n_i is the total density. For two gases, the difference between velocities, C_1-C_2 is given by the expression:[7] C_1-C_2=-\frac{n^2}{n_1n_2}D_{12}\left\{ \nabla \left(\frac{n_1}{n}\right)+ \frac{n_1n_2 (m_2-m_1)}{n (m_1n_1+m_2n_2)}\nabla P- \frac{m_1n_1m_2n_2}{P(m_1n_1+m_2n_2)}(F_1-F_2)+k_T \frac{1}{T}\nabla T\right\}, where F_{i} is the force applied to the molecules of the ith component and k_T is the thermodiffusion ratio. The coefficient D12 is positive. This is the diffusion coefficient. Four terms in the formula for C1-C2 describe four main effects in the diffusion of gases: \nabla \left(\frac{n_1}{n}\right) describes the flux of the first component from the areas with the high ratio n1/n to the areas with lower values of this ratio (and, analogously the flux of the second component from high n2/n to low n2/n because n2/n=1-n1/n); \frac{n_1n_2 (m_2-m_1)}{n (m_1n_1+m_2n_2)}\nabla P describes the flux of the heavier molecules to the areas with higher pressure and the lighter molecules to the areas with lower pressure, this is barodiffusion; \frac{m_1n_1m_2n_2}{P(m_1n_1+m_2n_2)}(F_1-F_2) describes diffusion caused by the difference of the forces applied to molecules of different types. For example, in the Earth's gravitational field, the heavier molecules should go down, or in electric field the charged molecules should move, until this effect is not equilibrated by the sum of other terms. This effect should not be confused with barodiffusion caused by the pressure gradient. k_T \frac{1}{T}\nabla T describes thermodiffusion, the diffusion flux caused by the temperature gradient. All these effects are called diffusion because they describe the differences between velocities of different components in the mixture. Therefore, these effects cannot be described as a bulk transport and differ from advection or convection. In the first approximation,[7] D_{12}=\frac{3}{2n(d_1+d_2)^2}\left[\frac{kT(m_1+m_2)}{2\pi m_1m_2}\right]^{1/2} for rigid spheres; D_{12}=\frac{3}{8nA_1({\nu})\Gamma(3-\frac{2}{\nu-1})}\left[\frac{kT(m_1+m_2)}{2\pi m_1m_2}\right]^{1/2} \left(\frac{2kT}{\kappa_{12}}\right)^{\frac{2}{\nu-1}} for repulsing force \kappa_{12}r^{-\nu}. The number A_1({\nu}) is defined by quadratures (formulas (3.7), (3.9), Ch. 10 of the classical Chapman and Cowling book[7]) We can see that the dependence on T for the rigid spheres is the same as for the simple mean free path theory but for the power repulsion laws the exponent is different. Dependence on a total concentration n for a given temperature has always the same character, 1/n. In applications to gas dynamics, the diffusion flux and the bulk flow should be joined in one system of transport equations. The bulk flow describes the mass transfer. Its velocity V is the mass average velocity. It is defined through the momentum density and the mass concentrations: V=\frac{\sum_i \rho_i C_i}{\rho}\, . where \rho_i =m_i n_i is the mass concentration of the ith species, \rho=\sum_i \rho_i is the mass density. By definition, the diffusion velocity of the ith component is v_i=C_i-V, \sum_i \rho_i v_i=0. The mass transfer of the ith component is described by the continuity equation \frac{\partial \rho_i}{\partial t}+\nabla(\rho_i V) + \nabla (\rho_i v_i)=W_i \, , where W_i is the net mass production rate in chemical reactions, \sum_i W_i= 0. In these equations, the term \nabla(\rho_i V) describes advection of the ith component and the term \nabla (\rho_i v_i) represents diffusion of this component. In 1948, Wendell H. Furry proposed to use the form of the diffusion rates found in kinetic theory as a framework for the new phenomenological approach to diffusion in gases. This approach was developed further by F.A. Williams and S.H. Lam.[15] For the diffusion velocities in multicomponent gases (N components) they used v_i=-\left(\sum_{j=1}^N D_{ij}\mathbf{d}_j + D_i^{(T)} \nabla (\ln T) \right)\, ; \mathbf{d}_j=\nabla X_j + (X_j-Y_j)\nabla (\ln P) + \mathbf{g}_j\, ; \mathbf{g}_j=\frac{\rho}{P}\left( Y_j \sum_{k=1}^N Y_k (f_k-f_j) \right)\, . Here, D_{ij} is the diffusion coefficient matrix, D_i^{(T)} is the thermal diffusion coefficient, f_i is the body force per unite mass acting on the ith species, X_i=P_i/P is the partial pressure fraction of the ith species (and P_i is the partial pressure), Y_i=\rho_i/\rho is the mass fraction of the ith species, and \sum_i X_i=\sum_i Y_i=1. The above palette shows change in excess carriers being generated (green:electrons and purple:holes) with increasing light intensity (Generation rate /cm3) at the center of an intrinsic semiconductor bar. Electrons have a higher diffusion constant than holes, leading to fewer excess electrons at the center as compared to holes. Separation of diffusion from convection in gases While Brownian motion of multi-molecular mesoscopic particles (like pollen grains studied by Brown) is observable under an optical microscope, molecular diffusion can only be probed in carefully controlled experimental conditions. Since Graham experiments, it is well known that avoiding of convection is necessary and this may be a non-trivial task. Under normal conditions, molecular diffusion dominates only on length scales between nanometer and millimeter. On larger length scales, transport in liquids and gases is normally due to another transport phenomenon, convection, and to study diffusion on the larger scale, special efforts are needed. Therefore, some often cited examples of diffusion are wrong: If cologne is sprayed in one place, it will soon be smelled in the entire room, but a simple calculation shows that this can't be due to diffusion. Convective motion persists in the room because the temperature inhomogeneity. If ink is dropped in water, one usually observes an inhomogeneous evolution of the spatial distribution, which clearly indicates convection (caused, in particular, by this dropping).[citation needed] In contrast, heat conduction through solid media is an everyday occurrence (e.g. a metal spoon partly immersed in a hot liquid). This explains why the diffusion of heat was explained mathematically before the diffusion of mass. Other types of diffusion Anisotropic diffusion, also known as the Perona-Malik equation, enhances high gradients Anomalous diffusion,[16] in porous medium Atomic diffusion, in solids Eddy diffusion, in coarse-grained description of turbulent flow Effusion of a gas through small holes Electronic diffusion, resulting in an electric current called the diffusion current Facilitated diffusion, present in some organisms Gaseous diffusion, used for isotope separation Heat equation, diffusion of thermal energy Itō diffusion, mathematisation of Brownian motion, continuous stochastic process. Knudsen diffusion of gas in long pores with frequent wall collisions Momentum diffusion ex. the diffusion of the hydrodynamic velocity field Photon diffusion Plasma diffusion Random walk,[17] model for diffusion Reverse diffusion, against the concentration gradient, in phase separation Rotational diffusion, random reorientations of molecules Surface diffusion, diffusion of adparticles on a surface Turbulent diffusion, transport of mass, heat, or momentum within a turbulent fluid Diffusion is the net movement of a substance (e.g., an atom, ion or molecule) from a region of high concentration to a region of low concentration. This is also referred to as the movement of a substance down a concentration gradient. A gradient is the change in the value of a quantity (e.g., concentration, pressure, temperature) with the change in another variable (e.g., distance). For example, a change in concentration over a distance is called a concentration gradient, a change in pressure over a distance is called a pressure gradient, and a change in temperature over a distance is a called a temperature gradient. The word diffusion is derived from the Latin word, "diffundere", which means "to spread out" (if a substance is “spreading out”, it is moving from an area of high concentration to an area of low concentration). A distinguishing feature of diffusion is that it results in mixing or mass transport, without requiring bulk motion (bulk flow). Thus, diffusion should not be confused with convection, or advection, which are other transport phenomena that utilize bulk motion to move particles from one place to another. |
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