Editing Fick's laws of diffusion (section)

== Applications ==
Equations based on Fick's law have been commonly used to model [[Passive transport|transport processes]] in foods, [[neuron]]s, [[biopolymer]]s, [[Pharmacology|pharmaceuticals]], [[porous]] [[soil]]s, [[population dynamics]], nuclear materials, [[plasma physics]], and [[Doping (semiconductor)|semiconductor doping]] processes. The theory of [[Voltammetry|voltammetric]] methods is based on solutions of Fick's equation. On the other hand, in some cases a "Fickian (another common approximation of the transport equation is that of the diffusion theory)" description is inadequate. For example, in [[polymer]] science and food science a more general approach is required to describe transport of components in materials undergoing a [[glass transition]]. One more general framework is the [[Maxwell–Stefan diffusion]] equations<ref>{{cite book | vauthors = Taylor R, Krishna R | title = Multicomponent mass transfer | volume = 2 | series =Wiley Series in Chemical Engineering | publisher = John Wiley & Sons | year = 1993 | isbn = 978-0-471-57417-0 }}{{pn|date=September 2024}}</ref>
of multi-component [[mass transfer]], from which Fick's law can be obtained as a limiting case, when the mixture is extremely dilute and every chemical species is interacting only with the bulk mixture and not with other species. To account for the presence of multiple species in a non-dilute mixture, several variations of the Maxwell–Stefan equations are used. See also non-diagonal coupled transport processes ([[Onsager reciprocal relations|Onsager]] relationship). <!-- Onsager = important point to be still developed -->

=== Fick's flow in liquids ===
When two [[miscibility|miscible]] liquids are brought into contact, and diffusion takes place, the macroscopic (or average) concentration evolves following Fick's law. On a mesoscopic scale, that is, between the macroscopic scale described by Fick's law and molecular scale, where molecular [[random walk]]s take place, fluctuations cannot be neglected. Such situations can be successfully modeled with Landau-Lifshitz fluctuating hydrodynamics. In this theoretical framework, diffusion is due to fluctuations whose dimensions range from the molecular scale to the macroscopic scale.<ref>{{cite journal | vauthors = Brogioli D, Vailati A | title = Diffusive mass transfer by nonequilibrium fluctuations: Fick's law revisited | journal = Physical Review E | volume = 63 | issue = 1 Pt 1 | pages = 012105 | date = January 2001 | pmid = 11304296 | doi = 10.1103/PhysRevE.63.012105 | bibcode = 2000PhRvE..63a2105B | arxiv = cond-mat/0006163 | s2cid = 1302913 }}</ref>

In particular, fluctuating hydrodynamic equations include a Fick's flow term, with a given diffusion coefficient, along with hydrodynamics equations and stochastic terms describing fluctuations. When calculating the fluctuations with a perturbative approach, the zero order approximation is Fick's law. The first order gives the fluctuations, and it comes out that fluctuations contribute to diffusion. This represents somehow a [[tautology (logic)|tautology]], since the phenomena described by a lower order approximation is the result of a higher approximation: this problem is solved only by [[renormalization|renormalizing]] the fluctuating hydrodynamics equations.

=== Sorption rate and collision frequency of diluted solute ===
Adsorption, absorption, and collision of molecules, particles, and surfaces are important problems in many fields. These fundamental processes regulate chemical, biological, and environmental reactions. Their rate can be calculated using the diffusion constant and Fick's laws of diffusion especially when these interactions happen in diluted solutions.

Typically, the diffusion constant of molecules and particles defined by Fick's equation can be calculated using the [[Stokes–Einstein equation]]. In the ultrashort time limit, in the order of the diffusion time ''a''<sup>2</sup>/''D'', where ''a'' is the particle radius, the diffusion is described by the [[Langevin equation]]. At a longer time, the [[Langevin equation]] merges into the [[Stokes–Einstein equation]]. The latter is appropriate for the condition of the diluted solution, where long-range diffusion is considered. According to the [[fluctuation-dissipation theorem]] based on the [[Langevin equation]] in the long-time limit and when the particle is significantly denser than the surrounding fluid, the time-dependent diffusion constant is:<ref>{{cite journal | vauthors = Bian X, Kim C, Karniadakis GE | title = 111 years of Brownian motion | journal = Soft Matter | volume = 12 | issue = 30 | pages = 6331–6346 | date = August 2016 | pmid = 27396746 | pmc = 5476231 | doi = 10.1039/c6sm01153e | bibcode = 2016SMat...12.6331B }}</ref>
: <math> D(t) = \mu \, k_{\rm B} T\left(1-e^{-t/(m\mu)}\right) , </math>
where (all in SI units)
* ''k''<sub>B</sub> is the [[Boltzmann constant]],
* ''T'' is the [[absolute temperature]],
* ''μ'' is the mobility of the particle in the fluid or gas, which can be calculated using the [[Einstein relation (kinetic theory)]],
* ''m'' is the mass of the particle,
* ''t'' is time.
For a single molecule such as organic molecules or [[biomolecule]]s (e.g. proteins) in water, the exponential term is negligible due to the small product of ''mμ'' in the ultrafast picosecond region, thus irrelevant to the relatively slower adsorption of diluted solute.

[[File:Diffusive sorption probability.png|300px|thumb|right|Scheme of molecular diffusion in the solution. Orange dots are solute molecules, solvent molecules are not drawn, black arrow is an example random walk trajectory, and the red curve is the diffusive Gaussian broadening probability function from the Fick's law of diffusion.<ref name = "Pyle-BJNano">{{cite journal | vauthors = Pyle JR, Chen J | title = Photobleaching of YOYO-1 in super-resolution single DNA fluorescence imaging | journal = Beilstein Journal of Nanotechnology | volume = 8 | pages = 2296–2306 | date = 2017-11-02 | pmid = 29181286 | pmc = 5687005 | doi = 10.3762/bjnano.8.229 }}</ref><sup>:Fig. 9</sup>]]

The [[adsorption]] or [[Absorption (chemistry)|absorption]] rate of a dilute solute to a surface or interface in a (gas or liquid) solution can be calculated using Fick's laws of diffusion. The accumulated number of molecules adsorbed on the surface is expressed by the Langmuir-Schaefer equation by integrating the diffusion flux equation over time as shown in the simulated molecular diffusion in the first section of this page:<ref name = "LangmuirSchaefer1937JACS">{{Cite journal| vauthors = Langmuir I, Schaefer VJ | date = 1937 | title = The Effect of Dissolved Salts on Insoluble Monolayers| journal = Journal of the American Chemical Society | volume = 29 | issue = 11 | pages = 2400–2414 | doi = 10.1021/ja01290a091| bibcode = 1937JAChS..59.2400L }}</ref>
: <math> \Gamma= 2AC_b\sqrt{\frac{Dt}{\pi}}.</math>
* {{mvar|A}} is the surface area (m<sup>2</sup>).
* <math>C_b</math> is the number concentration of the adsorber molecules (solute) in the bulk solution (#/m<sup>3</sup>).
* {{mvar|D}} is diffusion coefficient of the adsorber (m<sup>2</sup>/s).
* {{mvar|t}} is elapsed time (s).
* <math> \Gamma </math> is the accumulated number of molecules in unit # molecules adsorbed during the time <math>t</math>.
The equation is named after American chemists [[Irving Langmuir]] and [[Vincent Schaefer]].

Briefly as explained in,<ref name = "WardTordai1946">{{Cite journal| vauthors = Ward AF, Tordai L |date=1946| title = Time-dependence of Boundary Tensions of Solutions I. The Role of Diffusion in Time-effects| journal = Journal of Chemical Physics | volume = 14 | issue = 7| pages = 453–461 | doi = 10.1063/1.1724167| bibcode = 1946JChPh..14..453W}}</ref>
the concentration gradient profile near a newly created (from <math>t=0</math>) absorptive surface (placed at <math>x=0</math>) in a once uniform bulk solution is solved in the above sections from Fick's equation,
:<math> \frac{\partial C}{\partial x} = \frac{C_b}{\sqrt{\pi Dt}}\text{exp} \left (-\frac{x^2}{4Dt} \right ) , </math>
where {{mvar|C}} is the number concentration of adsorber molecules at <math> x, t </math> (#/m<sup>3</sup>).

The concentration gradient at the subsurface at <math>x = 0</math> is simplified to the pre-exponential factor of the distribution
:<math> \left (\frac{\partial C}{\partial x} \right ) _{x = 0} = \frac{C_b}{\sqrt{\pi Dt}} . </math>
And the rate of diffusion (flux) across area <math>A . </math> of the plane is
:<math> \left (\frac{\partial \Gamma }{\partial t} \right ) _{x = 0} = -\frac{DAC_b}{\sqrt{\pi Dt}} . </math>
Integrating over time,
: <math> \Gamma = \int_0^t \left( \frac{\partial \Gamma}{\partial t} \right) _{x = 0} = 2AC_b\sqrt{\frac{Dt}{\pi}} . </math>

The Langmuir–Schaefer equation can be extended to the Ward–Tordai Equation to account for the "back-diffusion" of rejected molecules from the surface:<ref name = "WardTordai1946" /> 
: <math> \Gamma= 2A{C_\text{b}}\sqrt{\frac{Dt}{\pi}} - A\sqrt{\frac{D}{\pi}}\int_0^\sqrt{t}\frac{C(\tau)}{\sqrt{t-\tau}} \, d\tau , </math> 
where <math>C_b</math> is the bulk concentration, <math>C</math> is the sub-surface concentration (which is a function of time depending on the reaction model of the adsorption), and <math>\tau</math> is a dummy variable.

Monte Carlo simulations show that these two equations work to predict the adsorption rate of systems that form predictable concentration gradients near the surface but have troubles for systems without or with unpredictable concentration gradients, such as typical biosensing systems or when flow and convection are significant.<ref name=JixinMCSimuAdsorption/>

[[File:DiffusiveAdsorptionHistory.jpg|thumb|A brief history of the theories on diffusive adsorption.<ref name=JixinMCSimuAdsorption/>]]

A brief history of diffusive adsorption is shown in the right figure.<ref name=JixinMCSimuAdsorption/> A noticeable challenge of understanding the diffusive adsorption at the single-molecule level is the [[fractal]] nature of diffusion. Most computer simulations pick a time step for diffusion which ignores the fact that there are self-similar finer diffusion events (fractal) within each step. Simulating the fractal diffusion shows that a factor of two corrections should be introduced for the result of a fixed time-step adsorption simulation, bringing it to be consistent with the above two equations.<ref name=JixinMCSimuAdsorption/>

A more problematic result of the above equations is they predict the lower limit of adsorption under ideal situations but is very difficult to predict the actual adsorption rates. The equations are derived at the long-time-limit condition when a stable concentration gradient has been formed near the surface. But real adsorption is often done much faster than this infinite time limit i.e. the concentration gradient, decay of concentration at the sub-surface, is only partially formed before the surface has been saturated or flow is on to maintain a certain gradient, thus the adsorption rate measured is almost always faster than the equations have predicted for low or none energy barrier adsorption (unless there is a significant adsorption energy barrier that slows down the absorption significantly), for example, thousands to millions time faster in the self-assembly of monolayers at the water-air or water-substrate interfaces.<ref name = LangmuirSchaefer1937JACS/> As such, it is necessary to calculate the evolution of the concentration gradient near the surface and find out a proper time to stop the imagined infinite evolution for practical applications. While it is hard to predict when to stop but it is reasonably easy to calculate the shortest time that matters, the critical time when the first nearest neighbor from the substrate surface feels the building-up of the concentration gradient. This yields the upper limit of the adsorption rate under an ideal situation when there are no other factors than diffusion that affect the absorber dynamics:<ref name=JixinMCSimuAdsorption/>
: <math> \langle r \rangle = \frac{4}{\pi}A C_b^{4/3}D , </math>
where:
* <math> \langle r \rangle </math> is the adsorption rate assuming under adsorption energy barrier-free situation, in unit #/s,
* <math> A </math> is the area of the surface of interest on an "infinite and flat" substrate (m<sup>2</sup>),
* <math> C_b </math> is the concentration of the absorber molecule in the bulk solution (#/m<sup>3</sup>),
* <math> D </math> is the diffusion constant of the absorber (solute) in the solution (m<sup>2</sup>/s) defined with Fick's law.

This equation can be used to predict the initial adsorption rate of any system; It can be used to predict the steady-state adsorption rate of a typical biosensing system when the binding site is just a very small fraction of the substrate surface and a near-surface concentration gradient is never formed; It can also be used to predict the adsorption rate of molecules on the surface when there is a significant flow to push the concentration gradient very shallowly in the sub-surface.

This critical time is significantly different from the first passenger arriving time or the mean free-path time. Using the average first-passenger time and Fick's law of diffusion to estimate the average binding rate will significantly over-estimate the concentration gradient because the first passenger usually comes from many layers of neighbors away from the target, thus its arriving time is significantly longer than the nearest neighbor diffusion time. Using the mean free path time plus the Langmuir equation will cause an artificial concentration gradient between the initial location of the first passenger and the target surface because the other neighbor layers have no change yet, thus significantly lower estimate the actual binding time, i.e., the actual first passenger arriving time itself, the inverse of the above rate, is difficult to calculate. If the system can be simplified to 1D diffusion, then the average first passenger time can be calculated using the same nearest neighbor critical diffusion time for the first neighbor distance to be the MSD,<ref name = "Pandey-JPCB2024">{{cite journal | vauthors = Pandey S, Gautam D, Chen J | title = Measuring the Adsorption Cross Section of YOYO-1 to Immobilized DNA Molecules | journal = Journal of Physical Chemistry B | volume =  128| pages = 7254–7262
 | date = 2024-07-16 | issue = 29 | pmid =  39014882| pmc = 11286311| doi = 10.1021/acs.jpcb.4c03359 | pmc-embargo-date = July 25, 2025 }}</ref> 
:<math>L = \sqrt{2Dt} , </math> 
where:
*<math>L~=C_b^{-1/3} </math> (unit m) is the average nearest neighbor distance approximated as cubic packing, where <math>C_b</math> is the solute concentration in the bulk solution (unit # molecule / m<sup>3</sup>),
*<math>D</math> is the diffusion coefficient defined by Fick's equation (unit m<sup>2</sup>/s),
*<math>t</math> is the critical time (unit s).
In this critical time, it is unlikely the first passenger has arrived and adsorbed. But it sets the speed of the layers of neighbors to arrive. At this speed with a concentration gradient that stops around the first neighbor layer, the gradient does not project virtually in the longer time when the actual first passenger arrives. Thus, the average first passenger coming rate (unit # molecule/s) for this 3D diffusion simplified in 1D problem, 
:<math> <r> = \frac{a}{t} = 2aC_b^{2/3}D , </math>
where <math> a</math> is a factor of converting the 3D diffusive adsorption problem into a 1D diffusion problem whose value depends on the system, e.g., a fraction of adsorption area <math>A</math> over solute nearest neighbor sphere surface area <math>4 \pi L^2 /4</math> assuming cubic packing each unit has 8 neighbors shared with other units. This example fraction converges the result to the 3D diffusive adsorption solution shown above with a slight difference in pre-factor due to different packing assumptions and ignoring other neighbors.

When the area of interest is the size of a molecule (specifically, a ''long cylindrical molecule'' such as DNA), the adsorption rate equation represents the collision frequency of two molecules in a diluted solution, with one molecule a specific side and the other no steric dependence, i.e., a molecule (random orientation) hit one side of the other. The diffusion constant need to be updated to the relative diffusion constant between two diffusing molecules. This estimation is especially useful in studying the interaction between a small molecule and a larger molecule such as a protein. The effective diffusion constant is dominated by the smaller one whose diffusion constant can be used instead.

The above hitting rate equation is also useful to predict the kinetics of molecular [[self-assembly]] on a surface. Molecules are randomly oriented in the bulk solution. Assuming 1/6 of the molecules has the right orientation to the surface binding sites, i.e. 1/2 of the z-direction in x, y, z three dimensions, thus the concentration of interest is just 1/6 of the bulk concentration. Put this value into the equation one should be able to calculate the theoretical adsorption kinetic curve using the [[Langmuir adsorption model]]. In a more rigid picture, 1/6 can be replaced by the steric factor of the binding geometry.

[[File:JChen2022JPCA.png|thumb|Comparing collision theory and diffusive collision theory.<ref name=JChen2022JPCA/>]]

The bimolecular collision frequency related to many reactions including protein coagulation/aggregation is initially described by [[Smoluchowski coagulation equation]] proposed by [[Marian Smoluchowski]] in a seminal 1916 publication,<ref name=Smoluchowski1916>{{cite journal | vauthors = Smoluchowski M | title = Drei Vorträge über Diffusion, Brownsche Molekularbewegung und Koagulation von Kolloidteilchen | journal =  Zeitschrift für Physik| year = 1916 | volume = 17 | pages = 557–571, 585–599 | language = German | bibcode = 1916ZPhy...17..557S }}</ref> derived from [[Brownian motion]] and Fick's laws of diffusion. Under an idealized reaction condition for A + B → product in a diluted solution, Smoluchovski suggested that the molecular flux at the infinite time limit can be calculated from Fick's laws of diffusion yielding a fixed/stable concentration gradient from the target molecule, e.g. B is the target molecule holding fixed relatively, and A is the moving molecule that creates a concentration gradient near the target molecule B due to the coagulation reaction between A and B. Smoluchowski calculated the collision frequency between A and B in the solution with unit #/s/m<sup>3</sup>:
: <math> Z_{AB} = 4{\pi}RD_rC_AC_B,</math>
where:
* <math>R</math> is the radius of the collision,
* <math>D_r = D_A + D_B</math> is the relative diffusion constant between A and B (m<sup>2</sup>/s),
* <math>C_A</math> and <math>C_B</math> are number concentrations of A and B respectively (#/m<sup>3</sup>).

The reaction order of this bimolecular reaction is 2 which is the analogy to the result from [[collision theory]] by replacing the moving speed of the molecule with diffusive flux. In the collision theory, the traveling time between A and B is proportional to the distance which is a similar relationship for the diffusion case if the flux is fixed.

However, under a practical condition, the concentration gradient near the target molecule is evolving over time with the molecular flux evolving as well,<ref name=JixinMCSimuAdsorption/> and on average the flux is much bigger than the infinite time limit flux Smoluchowski has proposed. Before the first passenger arrival time, Fick's equation predicts a concentration gradient over time which does not build up yet in reality. Thus, this Smoluchowski frequency represents the lower limit of the real collision frequency.

In 2022, Chen calculates the upper limit of the collision frequency between A and B in a solution assuming the bulk concentration of the moving molecule is fixed after the first nearest neighbor of the target molecule.<ref name=JChen2022JPCA>{{cite journal | vauthors = Chen J | title = Why Should the Reaction Order of a Bimolecular Reaction be 2.33 Instead of 2? | journal = The Journal of Physical Chemistry A | volume = 126 | issue = 51 | pages = 9719–9725 | date = December 2022 | pmid = 36520427 | pmc = 9805503 | doi = 10.1021/acs.jpca.2c07500 | bibcode = 2022JPCA..126.9719C }}</ref> Thus the concentration gradient evolution stops at the first nearest neighbor layer given a stop-time to calculate the actual flux. He named this the critical time and derived the diffusive collision frequency in unit #/s/m<sup>3</sup>:<ref name=JChen2022JPCA/>
: <math> Z_{AB} = \frac{8}{\pi}{\sigma} D_rC_AC_B\sqrt[3]{C_A+C_B} , </math>
where:
* <math>{\sigma}</math> is the area of the cross-section of the collision (m<sup>2</sup>),
* <math>D_r = D_A + D_B</math> is the relative diffusion constant between A and B (m<sup>2</sup>/s),
* <math>C_A</math> and <math>C_B</math> are number concentrations of A and B respectively (#/m<sup>3</sup>),
* <math>\sqrt[3]{C_A+C_B} </math> represents 1/<d>, where d is the average distance between two molecules.

This equation assumes the upper limit of a diffusive collision frequency between A and B is when the first neighbor layer starts to feel the evolution of the concentration gradient, whose reaction order is {{sfrac|2|1|3}} instead of 2. Both the Smoluchowski equation and the JChen equation satisfy dimensional checks with SI units. But the former is dependent on the radius and the latter is on the area of the collision sphere. From dimensional analysis, there will be an equation dependent on the volume of the collision sphere but eventually, all equations should converge to the same numerical rate of the collision that can be measured experimentally. The actual reaction order for a bimolecular unit reaction could be between 2 and {{sfrac|2|1|3}}, which makes sense because the diffusive collision time is squarely dependent on the distance between the two molecules.

These new equations also avoid the singularity on the adsorption rate at time zero for the Langmuir-Schaefer equation. The infinity rate is justifiable under ideal conditions because when you introduce target molecules magically in a solution of probe molecule or vice versa, there always be a probability of them overlapping at time zero, thus the rate of that two molecules association is infinity. It does not matter that other millions of molecules have to wait for their first mate to diffuse and arrive. The average rate is thus infinity. But statistically this argument is meaningless. The maximum rate of a molecule in a period of time larger than zero is 1, either meet or not, thus the infinite rate at time zero for that molecule pair really should just be one, making the average rate 1/millions or more and statistically negligible. This does not even count in reality no two molecules can magically meet at time zero.

=== Biological perspective ===
The first law gives rise to the following formula:<ref>{{cite book| title= Essentials of Human Physiology|  vauthors = Nosek TM | chapter=Section 3/3ch9/s3ch9_2 |chapter-url=http://humanphysiology.tuars.com/program/section3/3ch9/s3ch9_2.htm |archive-url=https://web.archive.org/web/20160324124828/http://humanphysiology.tuars.com/program/section3/3ch9/s3ch9_2.htm|archive-date=2016-03-24}}</ref>
: <math>\text{flux} = {-P \left(c_2 - c_1\right)} , </math>
where
* {{mvar|P}} is the permeability, an experimentally determined membrane "[[Electrical conductance|conductance]]" for a given gas at a given temperature,
* {{math|''c''<sub>2</sub> − ''c''<sub>1</sub>}} is the difference in [[concentration]] of the gas across the [[Artificial membrane|membrane]] for the direction of flow (from {{math|''c''<sub>1</sub>}} to {{math|''c''<sub>2</sub>}}).

Fick's first law is also important in radiation transfer equations.  However, in this context, it becomes inaccurate when the diffusion constant is low and the radiation becomes limited by the speed of light rather than by the resistance of the material the radiation is flowing through.  In this situation, one can use a [[flux limiter]].

The exchange rate of a gas across a fluid membrane can be determined by using this law together with [[Graham's law]].

Under the condition of a diluted solution when diffusion takes control, the membrane permeability mentioned in the above section can be theoretically calculated for the solute using the equation mentioned in the last section (use with particular care because the equation is derived for dense solutes, while biological molecules are not denser than water. Also, this equation assumes ideal concentration gradient forms near the membrane and evolves):<ref name = "Pyle-BJNano" />
: <math> P= 2A_p\eta_{tm} \sqrt{ \frac{D}{\pi t}} , </math>
where:
* <math>A_P</math> is the total area of the pores on the membrane (unit m<sup>2</sup>),
* <math>\eta_{tm}</math> transmembrane efficiency (unitless), which can be calculated from the stochastic theory of [[chromatography]],
* ''D'' is the diffusion constant of the solute unit m<sup>2</sup>⋅s<sup>−1</sup>,
* ''t'' is time unit s,
* ''c''<sub>2</sub>, ''c''<sub>1</sub> concentration should use unit mol m<sup>−3</sup>, so flux unit becomes mol s<sup>−1</sup>.

The flux is decay over the square root of time because a concentration gradient builds up near the membrane over time under ideal conditions. When there is flow and convection, the flux can be significantly different than the equation predicts and show an effective time t with a fixed value,<ref name=JixinMCSimuAdsorption/> which makes the flux stable instead of decay over time. A critical time has been estimated under idealized flow conditions when there is no gradient formed.<ref name=JixinMCSimuAdsorption/><ref name=JChen2022JPCA/> This strategy is adopted in biology such as blood circulation.

=== Semiconductor fabrication applications ===
The [[semiconductor]] is a collective term for a series of devices. It mainly includes three categories：two-terminal devices, three-terminal devices, and four-terminal devices. The combination of the semiconductors is called an integrated circuit.

The relationship between Fick's law and semiconductors: the principle of the semiconductor is transferring chemicals or dopants from a layer to a layer. Fick's law can be used to control and predict the diffusion by knowing how much the concentration of the dopants or chemicals move per meter and second through mathematics.

Therefore, different types and levels of semiconductors can be fabricated.

[[Integrated circuit]] fabrication technologies, model processes like CVD, thermal oxidation, wet oxidation, doping, etc. use diffusion equations obtained from Fick's law.

==== CVD method of fabricate semiconductor ====
The wafer is a kind of semiconductor whose silicon substrate is coated with a layer of CVD-created polymer chain and films. This film contains n-type and p-type dopants and takes responsibility for dopant conductions. The principle of CVD relies on the gas phase and gas-solid chemical reaction to create thin films.

The viscous flow regime of CVD is driven by a pressure gradient. CVD also includes a diffusion component distinct from the surface diffusion of adatoms. In CVD, reactants and products must also diffuse through a boundary layer of stagnant gas that exists next to the substrate. The total number of steps required for CVD film growth are gas phase diffusion of reactants through the boundary layer, adsorption and surface diffusion of adatoms, reactions on the substrate, and gas phase diffusion of products away through the boundary layer.

The velocity profile for gas flow is:
<math display="block">\delta(x) = \left( \frac{5x}{\mathrm{Re}^{1/2}} \right) \mathrm{Re}=\frac{v\rho L}{\eta}, </math>
where:
* <math>\delta</math> is the thickness,
* <math>\mathrm{Re}</math> is the Reynolds number,
* {{mvar|x}} is the length of the substrate,
* {{math|1=''v'' = 0}} at any surface,
* <math>\eta</math> is viscosity,
* <math>\rho</math> is density.

Integrated the {{mvar|x}} from {{math|0}} to {{mvar|L}}, it gives the average thickness:
<math display="block">\delta = \frac{10L}{3\mathrm{Re}^{1/2}} . </math>

To keep the reaction balanced, reactants must diffuse through the stagnant boundary layer to reach the substrate. So a thin boundary layer is desirable. According to the equations, increasing vo would result in more wasted reactants. The reactants will not reach the substrate uniformly if the flow becomes turbulent. Another option is to switch to a new carrier gas with lower viscosity or density.

The Fick's first law describes diffusion through the boundary layer. As a function of pressure (''p'') and temperature (''T'') in a gas, diffusion is determined.

<math display="block">D = D_0 \left(\frac{p_0}{p}\right) \left(\frac{T}{T_0}\right)^{3/2} , </math>
where:
* <math>p_0</math> is the standard pressure,
* <math>T_0</math> is the standard temperature,
* <math>D_0</math> is the standard diffusitivity.

The equation tells that increasing the temperature or decreasing the pressure can increase the diffusivity.

Fick's first law predicts the flux of the reactants to the substrate and product away from the substrate:
<math display="block">J = -D_i \left ( \frac{dc_i}{dx} \right ) , </math>
where:
* <math>x</math> is the thickness <math>\delta</math>,
* <math>dc_i</math> is the first reactant's concentration.

In ideal gas law <math>pV = nRT</math>, the concentration of the gas is expressed by partial pressure.

<math display="block">J = - D_i \left ( \frac{p_i-p_0}{\delta RT} \right ) , </math>
where
* <math>R</math> is the gas constant,
* <math>\frac{p_i-p_0}{\delta}</math> is the partial pressure gradient.

As a result, Fick's first law tells us we can use a partial pressure gradient to control the diffusivity and control the growth of thin films of semiconductors.

In many realistic situations, the simple Fick's law is not an adequate formulation for the semiconductor problem. It only applies to certain conditions, for example, given the semiconductor boundary conditions: constant source concentration diffusion, limited source concentration, or moving boundary diffusion (where junction depth keeps moving into the substrate).

==== Invalidity of Fickian diffusion ====
Even though Fickian diffusion has been used to model diffusion processes in semiconductor manufacturing (including CVD reactors) in early days, it often fails to validate the diffusion in advanced semiconductor nodes (< 90&nbsp;nm). This mostly stems from the inability of Fickian diffusion to model diffusion processes accurately at molecular level and smaller. In advanced semiconductor manufacturing, it is important to understand the movement at atomic scales, which is failed by continuum diffusion. Today, most semiconductor manufacturers use [[random walk]] to study and model diffusion processes. This allows us to study the effects of diffusion in a discrete manner to understand the movement of individual atoms, molecules, plasma etc.

In such a process, the movements of diffusing species (atoms, molecules, plasma etc.) are treated as a discrete entity, following a random walk through the CVD reactor, boundary layer, material structures etc. Sometimes, the movements might follow a biased-random walk depending on the processing conditions. Statistical analysis is done to understand variation/stochasticity arising from the random walk of the species, which in-turn affects the overall process and electrical variations.

=== Food production and cooking ===
The formulation of Fick's first law can explain a variety of complex phenomena in the context of food and cooking: Diffusion of molecules such as ethylene promotes plant growth and ripening, salt and sugar molecules promotes meat brining and marinating, and water molecules promote dehydration. Fick's first law can also be used to predict the changing moisture profiles across a spaghetti noodle as it hydrates during cooking. These phenomena are all about the spontaneous movement of particles of solutes driven by the concentration gradient. In different situations, there is different diffusivity which is a constant.<ref>{{cite journal | vauthors = Zhou L, Nyberg K, Rowat AC | title = Understanding diffusion theory and Fick's law through food and cooking | journal = Advances in Physiology Education | volume = 39 | issue = 3 | pages = 192–197 | date = September 2015 | pmid = 26330037 | doi = 10.1152/advan.00133.2014 | s2cid = 3921833 | url = http://www.escholarship.org/uc/item/1t87565r }}</ref>

By controlling the concentration gradient, the cooking time, shape of the food, and salting can be controlled.