movimiento browniano


El movimiento browniano , o pedesis (del griego antiguo : πήδησις / pɛ̌ːdɛːsis / "saltar"), es el movimiento aleatorio de partículas suspendidas en un medio (un líquido o un gas ). [2]

Paseo aleatorio bidimensional de un adatom plateado sobre una superficie de Ag (111) [1]
Esta es una simulación del movimiento browniano de 5 partículas (amarillas) que chocan con un gran conjunto de 800 partículas. Las partículas amarillas dejan 5 rastros azules de movimiento (pseudo) aleatorio y uno de ellos tiene un vector de velocidad rojo.
Esta es una simulación del movimiento browniano de una gran partícula (partícula de polvo) que choca con un gran conjunto de partículas más pequeñas (moléculas de un gas) que se mueven con diferentes velocidades en diferentes direcciones aleatorias.

Este patrón de movimiento generalmente consiste en fluctuaciones aleatorias en la posición de una partícula dentro de un subdominio de fluido, seguido de una reubicación a otro subdominio. A cada reubicación le siguen más fluctuaciones dentro del nuevo volumen cerrado. Este patrón describe un fluido en equilibrio térmico , definido por una temperatura dada . Dentro de tal fluido, no existe una dirección preferencial de flujo (como en los fenómenos de transporte ). Más específicamente, los momentos lineales y angulares generales del fluido permanecen nulos a lo largo del tiempo. Las energías cinéticas de los movimientos brownianos moleculares, junto con las de las rotaciones y vibraciones moleculares, suman el componente calórico de la energía interna de un fluido (el teorema de equipartición ).

Este movimiento lleva el nombre del botánico Robert Brown , quien describió por primera vez el fenómeno en 1827, mientras miraba a través de un microscopio el polen de la planta Clarkia pulchella sumergida en agua. En 1905, casi ochenta años después, el físico teórico Albert Einstein publicó un artículo en el que modeló el movimiento de las partículas de polen movidas por moléculas de agua individuales , haciendo una de sus primeras contribuciones científicas importantes. [3] Esta explicación del movimiento browniano sirvió como evidencia convincente de que existen átomos y moléculas y fue verificada experimentalmente por Jean Perrin en 1908. Perrin recibió el Premio Nobel de Física en 1926 "por su trabajo sobre la estructura discontinua de la materia". [4] La dirección de la fuerza del bombardeo atómico cambia constantemente, y en diferentes momentos la partícula es golpeada más en un lado que en otro, lo que lleva a la naturaleza aparentemente aleatoria del movimiento.

Las interacciones de muchos cuerpos que producen el patrón browniano no pueden resolverse mediante un modelo que tenga en cuenta todas las moléculas involucradas. En consecuencia, solo se pueden emplear modelos probabilísticos aplicados a poblaciones moleculares para describirlo. A continuación se presentan dos de estos modelos de la mecánica estadística , debido a Einstein y Smoluchowski. Otra clase de modelos puramente probabilísticos es la clase de los modelos de proceso estocásticos . Existen secuencias de procesos estocásticos más simples y más complicados que convergen (en el límite ) al movimiento browniano (ver paseo aleatorio y teorema de Donsker ). [5] [6]

Reproducido del libro de Jean Baptiste Perrin , Les Atomes , se muestran tres trazados del movimiento de partículas coloidales de 0,53 µm de radio, como se ve al microscopio. Las posiciones sucesivas cada 30 segundos están unidas por segmentos de línea recta (el tamaño de la malla es de 3,2 µm). [7]

El poema científico del filósofo y poeta romano Lucrecio " Sobre la naturaleza de las cosas " (c. 60 a. C.) tiene una descripción notable del movimiento de las partículas de polvo en los versículos 113-140 del Libro II. Utiliza esto como prueba de la existencia de átomos:

Observe lo que sucede cuando los rayos del sol ingresan a un edificio y arrojan luz sobre sus lugares sombríos. Verá una multitud de partículas diminutas que se mezclan en una multitud de formas ... su danza es una indicación real de los movimientos subyacentes de la materia que están ocultos a nuestra vista ... Se origina en los átomos que se mueven por sí mismos [es decir, espontáneamente ]. Entonces esos pequeños cuerpos compuestos que están menos alejados del ímpetu de los átomos se ponen en movimiento por el impacto de sus golpes invisibles y a su vez cañones contra cuerpos un poco más grandes. Entonces, el movimiento asciende desde los átomos y emerge gradualmente al nivel de nuestros sentidos, de modo que esos cuerpos están en movimiento que vemos en los rayos del sol, movidos por golpes que permanecen invisibles.

Aunque el movimiento entremezclado de las partículas de polvo es causado en gran parte por las corrientes de aire, el movimiento brillante y giratorio de las pequeñas partículas de polvo es causado principalmente por la verdadera dinámica browniana; Lucrecio "describe y explica perfectamente el movimiento browniano con un ejemplo equivocado". [8]

Mientras que Jan Ingenhousz describió el movimiento irregular de las partículas de polvo de carbón en la superficie del alcohol en 1785, el descubrimiento de este fenómeno se atribuye a menudo al botánico Robert Brown en 1827. Brown estaba estudiando los granos de polen de la planta Clarkia pulchella suspendidos en agua bajo un microscopio cuando observó partículas diminutas, expulsadas por los granos de polen, ejecutando un movimiento de nerviosismo. Al repetir el experimento con partículas de materia inorgánica, pudo descartar que el movimiento estuviera relacionado con la vida, aunque su origen aún no se había explicado.

La primera persona en describir las matemáticas detrás del movimiento browniano fue Thorvald N. Thiele en un artículo sobre el método de mínimos cuadrados publicado en 1880. Esto fue seguido de forma independiente por Louis Bachelier en 1900 en su tesis doctoral "La teoría de la especulación", en la que presentó un análisis estocástico de los mercados de acciones y opciones. El modelo de movimiento browniano del mercado de valores se cita a menudo, pero Benoit Mandelbrot rechazó su aplicabilidad a los movimientos del precio de las acciones en parte porque estos son discontinuos. [9]

Albert Einstein (en uno de sus artículos de 1905 ) y Marian Smoluchowski (1906) llamaron la atención de los físicos sobre la solución del problema y lo presentaron como una forma de confirmar indirectamente la existencia de átomos y moléculas. Sus ecuaciones que describen el movimiento browniano fueron posteriormente verificadas por el trabajo experimental de Jean Baptiste Perrin en 1908.

La teoría de Einstein

La teoría de Einstein consta de dos partes: la primera parte consiste en la formulación de una ecuación de difusión para partículas brownianas, en la que el coeficiente de difusión está relacionado con el desplazamiento cuadrático medio de una partícula browniana, mientras que la segunda parte consiste en relacionar el coeficiente de difusión a cantidades físicas mensurables. [10] De esta manera, Einstein pudo determinar el tamaño de los átomos y cuántos átomos hay en un mol, o el peso molecular en gramos, de un gas. [11] De acuerdo con la ley de Avogadro, este volumen es el mismo para todos los gases ideales, que es de 22,414 litros a temperatura y presión estándar. El número de átomos contenidos en este volumen se denomina número de Avogadro , y la determinación de este número equivale al conocimiento de la masa de un átomo, ya que esta última se obtiene dividiendo la masa de un mol de gas por la masa de un átomo. Constante de Avogadro .

Las características curvas en forma de campana de la difusión de partículas brownianas. La distribución comienza como una función delta de Dirac , lo que indica que todas las partículas están ubicadas en el origen en el tiempo t = 0. A medida que t aumenta, la distribución se aplana (aunque permanece en forma de campana) y finalmente se vuelve uniforme en el límite que pasa el tiempo. hasta el infinito.

La primera parte del argumento de Einstein fue determinar qué tan lejos viaja una partícula browniana en un intervalo de tiempo dado. [3] La mecánica clásica es incapaz de determinar esta distancia debido a la enorme cantidad de bombardeos que sufrirá una partícula browniana, aproximadamente del orden de 10 14 colisiones por segundo. [2]

Consideró el incremento de las posiciones de las partículas en el tiempo en un espacio unidimensional ( x ) (con las coordenadas elegidas para que el origen se encuentre en la posición inicial de la partícula) como una variable aleatoria () con alguna función de densidad de probabilidad (es decir, es la probabilidad de un salto de magnitud , por ejemplo, de a ). Además, asumiendo la conservación del número de partículas, amplió la densidad (número de partículas por unidad de volumen) en el momentoen una serie de Taylor ,

donde la segunda igualdad en la primera línea es por definición de . La integral en el primer término es igual a uno por la definición de probabilidad, y el segundo y otros términos pares (es decir, el primer y otros momentos impares ) desaparecen debido a la simetría espacial. Lo que queda da lugar a la siguiente relación:

Where the coefficient after the Laplacian, the second moment of probability of displacement , is interpreted as mass diffusivity D:

Then the density of Brownian particles ρ at point x at time t satisfies the diffusion equation:

Assuming that N particles start from the origin at the initial time t = 0, the diffusion equation has the solution

This expression (which is a normal distribution with the mean and variance usually called Brownian motion ) allowed Einstein to calculate the moments directly. The first moment is seen to vanish, meaning that the Brownian particle is equally likely to move to the left as it is to move to the right. The second moment is, however, non-vanishing, being given by

This equation expresses the mean squared displacement in terms of the time elapsed and the diffusivity. From this expression Einstein argued that the displacement of a Brownian particle is not proportional to the elapsed time, but rather to its square root.[10] His argument is based on a conceptual switch from the "ensemble" of Brownian particles to the "single" Brownian particle: we can speak of the relative number of particles at a single instant just as well as of the time it takes a Brownian particle to reach a given point.[12]

The second part of Einstein's theory relates the diffusion constant to physically measurable quantities, such as the mean squared displacement of a particle in a given time interval. This result enables the experimental determination of Avogadro's number and therefore the size of molecules. Einstein analyzed a dynamic equilibrium being established between opposing forces. The beauty of his argument is that the final result does not depend upon which forces are involved in setting up the dynamic equilibrium.

In his original treatment, Einstein considered an osmotic pressure experiment, but the same conclusion can be reached in other ways.

Consider, for instance, particles suspended in a viscous fluid in a gravitational field. Gravity tends to make the particles settle, whereas diffusion acts to homogenize them, driving them into regions of smaller concentration. Under the action of gravity, a particle acquires a downward speed of v = μmg, where m is the mass of the particle, g is the acceleration due to gravity, and μ is the particle's mobility in the fluid. George Stokes had shown that the mobility for a spherical particle with radius r is , where η is the dynamic viscosity of the fluid. In a state of dynamic equilibrium, and under the hypothesis of isothermal fluid, the particles are distributed according to the barometric distribution

where ρρ0 is the difference in density of particles separated by a height difference of h, kB is the Boltzmann constant (the ratio of the universal gas constant, R, to the Avogadro constant, NA), and T is the absolute temperature.

The equilibrium distribution for particles of gamboge shows the tendency for granules to move to regions of lower concentration when affected by gravity.

Dynamic equilibrium is established because the more that particles are pulled down by gravity, the greater the tendency for the particles to migrate to regions of lower concentration. The flux is given by Fick's law,

where J = ρv. Introducing the formula for ρ, we find that

In a state of dynamical equilibrium, this speed must also be equal to v = μmg. Both expressions for v are proportional to mg, reflecting that the derivation is independent of the type of forces considered. Similarly, one can derive an equivalent formula for identical charged particles of charge q in a uniform electric field of magnitude E, where mg is replaced with the electrostatic force qE. Equating these two expressions yields a formula for the diffusivity, independent of mg or qE or other such forces:

Here the first equality follows from the first part of Einstein's theory, the third equality follows from the definition of Boltzmann's constant as kB = R / NA, and the fourth equality follows from Stokes's formula for the mobility. By measuring the mean squared displacement over a time interval along with the universal gas constant R, the temperature T, the viscosity η, and the particle radius r, the Avogadro constant NA can be determined.

The type of dynamical equilibrium proposed by Einstein was not new. It had been pointed out previously by J. J. Thomson[13] in his series of lectures at Yale University in May 1903 that the dynamic equilibrium between the velocity generated by a concentration gradient given by Fick's law and the velocity due to the variation of the partial pressure caused when ions are set in motion "gives us a method of determining Avogadro's Constant which is independent of any hypothesis as to the shape or size of molecules, or of the way in which they act upon each other".[13]

An identical expression to Einstein's formula for the diffusion coefficient was also found by Walther Nernst in 1888[14] in which he expressed the diffusion coefficient as the ratio of the osmotic pressure to the ratio of the frictional force and the velocity to which it gives rise. The former was equated to the law of van 't Hoff while the latter was given by Stokes's law. He writes for the diffusion coefficient k', where is the osmotic pressure and k is the ratio of the frictional force to the molecular viscosity which he assumes is given by Stokes's formula for the viscosity. Introducing the ideal gas law per unit volume for the osmotic pressure, the formula becomes identical to that of Einstein's.[15] The use of Stokes's law in Nernst's case, as well as in Einstein and Smoluchowski, is not strictly applicable since it does not apply to the case where the radius of the sphere is small in comparison with the mean free path.[16]

At first, the predictions of Einstein's formula were seemingly refuted by a series of experiments by Svedberg in 1906 and 1907, which gave displacements of the particles as 4 to 6 times the predicted value, and by Henri in 1908 who found displacements 3 times greater than Einstein's formula predicted.[17] But Einstein's predictions were finally confirmed in a series of experiments carried out by Chaudesaigues in 1908 and Perrin in 1909. The confirmation of Einstein's theory constituted empirical progress for the kinetic theory of heat. In essence, Einstein showed that the motion can be predicted directly from the kinetic model of thermal equilibrium. The importance of the theory lay in the fact that it confirmed the kinetic theory's account of the second law of thermodynamics as being an essentially statistical law.[18]

Brownian motion model of the trajectory of a particle of dye in water.

Smoluchowski model

Smoluchowski's theory of Brownian motion[19] starts from the same premise as that of Einstein and derives the same probability distribution ρ(x, t) for the displacement of a Brownian particle along the x in time t. He therefore gets the same expression for the mean squared displacement: . However, when he relates it to a particle of mass m moving at a velocity which is the result of a frictional force governed by Stokes's law, he finds

where μ is the viscosity coefficient, and is the radius of the particle. Associating the kinetic energy with the thermal energy RT/N, the expression for the mean squared displacement is 64/27 times that found by Einstein. The fraction 27/64 was commented on by Arnold Sommerfeld in his necrology on Smoluchowski: "The numerical coefficient of Einstein, which differs from Smoluchowski by 27/64 can only be put in doubt."[20]

Smoluchowski[21] attempts to answer the question of why a Brownian particle should be displaced by bombardments of smaller particles when the probabilities for striking it in the forward and rear directions are equal. If the probability of m gains and n − m losses follows a binomial distribution,

with equal a priori probabilities of 1/2, the mean total gain is

If n is large enough so that Stirling's approximation can be used in the form

then the expected total gain will be[citation needed]

showing that it increases as the square root of the total population.

Suppose that a Brownian particle of mass M is surrounded by lighter particles of mass m which are traveling at a speed u. Then, reasons Smoluchowski, in any collision between a surrounding and Brownian particles, the velocity transmitted to the latter will be mu/M. This ratio is of the order of 10−7 cm/s. But we also have to take into consideration that in a gas there will be more than 1016 collisions in a second, and even greater in a liquid where we expect that there will be 1020 collision in one second. Some of these collisions will tend to accelerate the Brownian particle; others will tend to decelerate it. If there is a mean excess of one kind of collision or the other to be of the order of 108 to 1010 collisions in one second, then velocity of the Brownian particle may be anywhere between 10 and 1000 cm/s. Thus, even though there are equal probabilities for forward and backward collisions there will be a net tendency to keep the Brownian particle in motion, just as the ballot theorem predicts.

These orders of magnitude are not exact because they don't take into consideration the velocity of the Brownian particle, U, which depends on the collisions that tend to accelerate and decelerate it. The larger U is, the greater will be the collisions that will retard it so that the velocity of a Brownian particle can never increase without limit. Could such a process occur, it would be tantamount to a perpetual motion of the second type. And since equipartition of energy applies, the kinetic energy of the Brownian particle, , will be equal, on the average, to the kinetic energy of the surrounding fluid particle, .

In 1906 Smoluchowski published a one-dimensional model to describe a particle undergoing Brownian motion.[22] The model assumes collisions with M ≫ m where M is the test particle's mass and m the mass of one of the individual particles composing the fluid. It is assumed that the particle collisions are confined to one dimension and that it is equally probable for the test particle to be hit from the left as from the right. It is also assumed that every collision always imparts the same magnitude of ΔV. If NR is the number of collisions from the right and NL the number of collisions from the left then after N collisions the particle's velocity will have changed by ΔV(2NR − N). The multiplicity is then simply given by:

and the total number of possible states is given by 2N. Therefore, the probability of the particle being hit from the right NR times is:

As a result of its simplicity, Smoluchowski's 1D model can only qualitatively describe Brownian motion. For a realistic particle undergoing Brownian motion in a fluid, many of the assumptions don't apply. For example, the assumption that on average occurs an equal number of collisions from the right as from the left falls apart once the particle is in motion. Also, there would be a distribution of different possible ΔVs instead of always just one in a realistic situation.

Other physics models using partial differential equations

The diffusion equation yields an approximation of the time evolution of the probability density function associated to the position of the particle going under a Brownian movement under the physical definition. The approximation is valid on short timescales.

The time evolution of the position of the Brownian particle itself is best described using Langevin equation, an equation which involves a random force field representing the effect of the thermal fluctuations of the solvent on the particle.

The displacement of a particle undergoing Brownian motion is obtained by solving the diffusion equation under appropriate boundary conditions and finding the rms of the solution. This shows that the displacement varies as the square root of the time (not linearly), which explains why previous experimental results concerning the velocity of Brownian particles gave nonsensical results. A linear time dependence was incorrectly assumed.

At very short time scales, however, the motion of a particle is dominated by its inertia and its displacement will be linearly dependent on time: Δx = vΔt. So the instantaneous velocity of the Brownian motion can be measured as v = Δxt, when Δt << τ, where τ is the momentum relaxation time. In 2010, the instantaneous velocity of a Brownian particle (a glass microsphere trapped in air with optical tweezers) was measured successfully.[23] The velocity data verified the Maxwell–Boltzmann velocity distribution, and the equipartition theorem for a Brownian particle.

Astrophysics: star motion within galaxies

In stellar dynamics, a massive body (star, black hole, etc.) can experience Brownian motion as it responds to gravitational forces from surrounding stars.[24] The rms velocity V of the massive object, of mass M, is related to the rms velocity of the background stars by

where is the mass of the background stars. The gravitational force from the massive object causes nearby stars to move faster than they otherwise would, increasing both and V.[24] The Brownian velocity of Sgr A*, the supermassive black hole at the center of the Milky Way galaxy, is predicted from this formula to be less than 1 km s−1.[25]

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An animated example of a Brownian motion-like random walk on a torus. In the scaling limit, random walk approaches the Wiener process according to Donsker's theorem.

In mathematics, Brownian motion is described by the Wiener process, a continuous-time stochastic process named in honor of Norbert Wiener. It is one of the best known Lévy processes (càdlàg stochastic processes with stationary independent increments) and occurs frequently in pure and applied mathematics, economics and physics.

A single realisation of three-dimensional Brownian motion for times 0 ≤  t ≤ 2

The Wiener process Wt is characterized by four facts:[citation needed]

  1. W0 = 0
  2. Wt is almost surely continuous
  3. Wt has independent increments
  4. (for ).

denotes the normal distribution with expected value μ and variance σ2. The condition that it has independent increments means that if then and are independent random variables.

An alternative characterisation of the Wiener process is the so-called Lévy characterisation that says that the Wiener process is an almost surely continuous martingale with W0 = 0 and quadratic variation .

A third characterisation is that the Wiener process has a spectral representation as a sine series whose coefficients are independent random variables. This representation can be obtained using the Karhunen–Loève theorem.

The Wiener process can be constructed as the scaling limit of a random walk, or other discrete-time stochastic processes with stationary independent increments. This is known as Donsker's theorem. Like the random walk, the Wiener process is recurrent in one or two dimensions (meaning that it returns almost surely to any fixed neighborhood of the origin infinitely often) whereas it is not recurrent in dimensions three and higher. Unlike the random walk, it is scale invariant.

The time evolution of the position of the Brownian particle itself can be described approximately by a Langevin equation, an equation which involves a random force field representing the effect of the thermal fluctuations of the solvent on the Brownian particle. On long timescales, the mathematical Brownian motion is well described by a Langevin equation. On small timescales, inertial effects are prevalent in the Langevin equation. However the mathematical Brownian motion is exempt of such inertial effects. Inertial effects have to be considered in the Langevin equation, otherwise the equation becomes singular.[clarification needed] so that simply removing the inertia term from this equation would not yield an exact description, but rather a singular behavior in which the particle doesn't move at all.[clarification needed]

Statistics

The Brownian motion can be modeled by a random walk.[26] Random walks in porous media or fractals are anomalous.[27]

In the general case, Brownian motion is a non-Markov random process and described by stochastic integral equations.[28]

Lévy characterisation

The French mathematician Paul Lévy proved the following theorem, which gives a necessary and sufficient condition for a continuous Rn-valued stochastic process X to actually be n-dimensional Brownian motion. Hence, Lévy's condition can actually be used as an alternative definition of Brownian motion.

Let X = (X1, ..., Xn) be a continuous stochastic process on a probability space (Ω, Σ, P) taking values in Rn. Then the following are equivalent:

  1. X is a Brownian motion with respect to P, i.e., the law of X with respect to P is the same as the law of an n-dimensional Brownian motion, i.e., the push-forward measure X(P) is classical Wiener measure on C0([0, +∞); Rn).
  2. both
    1. X is a martingale with respect to P (and its own natural filtration); and
    2. for all 1 ≤ ij ≤ n, Xi(t)Xj(t) −δijt is a martingale with respect to P (and its own natural filtration), where δij denotes the Kronecker delta.

Spectral content

The spectral content of a stochastic process can be found from the power spectral density, formally defined as

,

where stands for the expected value. The power spectral density of Brownian motion is found to be[29]

.

where is the diffusion coefficient of . For naturally occurring signals, the spectral content can be found from the power spectral density of a single realization, with finite available time, i.e.,

,

which for an individual realization of a Brownian motion trajectory,[30] it is found to have expected value

and variance [30]

.

For sufficiently long realization times, the expected value of the power spectrum of a single trajectory converges to the formally defined power spectral density , but its coefficient of variation tends to . This implies the distribution of is broad even in the infinite time limit.

Riemannian manifold

Brownian motion on a sphere

The infinitesimal generator (and hence characteristic operator) of a Brownian motion on Rn is easily calculated to be ½Δ, where Δ denotes the Laplace operator. In image processing and computer vision, the Laplacian operator has been used for various tasks such as blob and edge detection. This observation is useful in defining Brownian motion on an m-dimensional Riemannian manifold (Mg): a Brownian motion on M is defined to be a diffusion on M whose characteristic operator in local coordinates xi, 1 ≤ i ≤ m, is given by ½ΔLB, where ΔLB is the Laplace–Beltrami operator given in local coordinates by

where [gij] = [gij]−1 in the sense of the inverse of a square matrix.

The narrow escape problem is a ubiquitous problem in biology, biophysics and cellular biology which has the following formulation: a Brownian particle (ion, molecule, or protein) is confined to a bounded domain (a compartment or a cell) by a reflecting boundary, except for a small window through which it can escape. The narrow escape problem is that of calculating the mean escape time. This time diverges as the window shrinks, thus rendering the calculation a singular perturbation problem.

  • Brownian bridge: a Brownian motion that is required to "bridge" specified values at specified times
  • Brownian covariance
  • Brownian dynamics
  • Brownian motion of sol particles
  • Brownian motor
  • Brownian noise (Martin Gardner proposed this name for sound generated with random intervals. It is a pun on Brownian motion and white noise.)
  • Brownian ratchet
  • Brownian surface
  • Brownian tree
  • Brownian web
  • Rotational Brownian motion
  • Clinamen
  • Complex system
  • Continuity equation
  • Diffusion equation
  • Geometric Brownian motion
  • Itō diffusion: a generalisation of Brownian motion
  • Langevin equation
  • Lévy arcsine law
  • Local time (mathematics)
  • Many-body problem
  • Marangoni effect
  • Nanoparticle tracking analysis
  • Narrow escape problem
  • Osmosis
  • Random walk
  • Schramm–Loewner evolution
  • Single particle tracking
  • Statistical mechanics
  • Surface diffusion: a type of constrained Brownian motion.
  • Thermal equilibrium
  • Thermodynamic equilibrium
  • Tyndall effect: physical chemistry phenomenon where particles are involved; used to differentiate between the different types of mixtures.
  • Ultramicroscope

  1. ^ Meyburg, Jan Philipp; Diesing, Detlef (2017). "Teaching the Growth, Ripening, and Agglomeration of Nanostructures in Computer Experiments". Journal of Chemical Education. 94 (9): 1225–1231. Bibcode:2017JChEd..94.1225M. doi:10.1021/acs.jchemed.6b01008.
  2. ^ a b Feynman, R. (1964). "The Brownian Movement". The Feynman Lectures of Physics, Volume I. pp. 41–1.
  3. ^ a b Einstein, Albert (1905). "Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen" [On the Movement of Small Particles Suspended in Stationary Liquids Required by the Molecular-Kinetic Theory of Heat] (PDF). Annalen der Physik (in German). 322 (8): 549–560. Bibcode:1905AnP...322..549E. doi:10.1002/andp.19053220806.
  4. ^ "The Nobel Prize in Physics 1926". NobelPrize.org. Retrieved 29 May 2019.
  5. ^ Knight, Frank B. (1 February 1962). "On the random walk and Brownian motion". Transactions of the American Mathematical Society. 103 (2): 218. doi:10.1090/S0002-9947-1962-0139211-2. ISSN 0002-9947.
  6. ^ "Donsker invariance principle - Encyclopedia of Mathematics". encyclopediaofmath.org. Retrieved 28 June 2020.
  7. ^ Perrin, Jean (1914). Atoms. London : Constable. p. 115.
  8. ^ Tabor, D. (1991). Gases, Liquids and Solids: And Other States of Matter (3rd ed.). Cambridge: Cambridge University Press. p. 120. ISBN 978-0-521-40667-3.
  9. ^ Mandelbrot, B.; Hudson, R. (2004). The (Mis)behavior of Markets: A Fractal View of Risk, Ruin, and Reward. Basic Books. ISBN 978-0-465-04355-2.
  10. ^ a b Einstein, Albert (1956) [1926]. Investigations on the Theory of the Brownian Movement (PDF). Dover Publications. Retrieved 25 December 2013.
  11. ^ Stachel, J., ed. (1989). "Einstein's Dissertation on the Determination of Molecular Dimensions" (PDF). The Collected Papers of Albert Einstein, Volume 2. Princeton University Press.
  12. ^ Lavenda, Bernard H. (1985). Nonequilibrium Statistical Thermodynamics. John Wiley & Sons. p. 20. ISBN 978-0-471-90670-4.
  13. ^ a b Thomson, J. J. (1904). Electricity and Matter. Yale University Press. pp. 80–83.
  14. ^ Nernst, Walther (1888). "Zur Kinetik der in Lösung befindlichen Körper". Zeitschrift für Physikalische Chemie (in German). 9: 613–637.
  15. ^ Leveugle, J. (2004). La Relativité, Poincaré et Einstein, Planck, Hilbert. Harmattan. p. 181.
  16. ^ Townsend, J.E.S. (1915). Electricity in Gases. Clarendon Press. p. 254.
  17. ^ See P. Clark 1976, p. 97
  18. ^ See P. Clark 1976 for this whole paragraph
  19. ^ Smoluchowski, M. M. (1906). "Sur le chemin moyen parcouru par les molécules d'un gaz et sur son rapport avec la théorie de la diffusion" [On the average path taken by gas molecules and its relation with the theory of diffusion]. Bulletin International de l'Académie des Sciences de Cracovie (in French): 202.
  20. ^ See p. 535 in Sommerfeld, A. (1917). "Zum Andenken an Marian von Smoluchowski" [In Memory of Marian von Smoluchowski]. Physikalische Zeitschrift (in German). 18 (22): 533–539.
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  • Brown, Robert (1828). "A brief account of microscopical observations made in the months of June, July and August, 1827, on the particles contained in the pollen of plants; and on the general existence of active molecules in organic and inorganic bodies" (PDF). Philosophical Magazine. 4 (21): 161–173. doi:10.1080/14786442808674769. Also includes a subsequent defense by Brown of his original observations, Additional remarks on active molecules.
  • Chaudesaigues, M. (1908). "Le mouvement brownien et la formule d'Einstein" [Brownian motion and Einstein's formula]. Comptes Rendus (in French). 147: 1044–6.
  • Clark, P. (1976). "Atomism versus thermodynamics". In Howson, Colin (ed.). Method and appraisal in the physical sciences. Cambridge University Press. ISBN 978-0521211109.
  • Cohen, Ruben D. (1986). "Self Similarity in Brownian Motion and Other Ergodic Phenomena" (PDF). Journal of Chemical Education. 63 (11): 933–934. Bibcode:1986JChEd..63..933C. doi:10.1021/ed063p933.
  • Dubins, Lester E.; Schwarz, Gideon (15 May 1965). "On Continuous Martingales". Proceedings of the National Academy of Sciences of the United States of America. 53 (3): 913–916. Bibcode:1965PNAS...53..913D. doi:10.1073/pnas.53.5.913. JSTOR 72837. PMC 301348. PMID 16591279.
  • Einstein, A. (1956). Investigations on the Theory of Brownian Movement. New York: Dover. ISBN 978-0-486-60304-9. Retrieved 6 January 2014.
  • Henri, V. (1908). "Études cinématographique du mouvement brownien" [Cinematographic studies of Brownian motion]. Comptes Rendus (in French) (146): 1024–6.
  • Lucretius, On The Nature of Things, translated by William Ellery Leonard. (on-line version, from Project Gutenberg. See the heading 'Atomic Motions'; this translation differs slightly from the one quoted).
  • Nelson, Edward, (1967). Dynamical Theories of Brownian Motion. (PDF version of this out-of-print book, from the author's webpage.) This is primarily a mathematical work, but the first four chapters discuss the history of the topic, in the era from Brown to Einstein.
  • Pearle, P.; Collett, B.; Bart, K.; Bilderback, D.; Newman, D.; Samuels, S. (2010). "What Brown saw and you can too". American Journal of Physics. 78 (12): 1278–1289. arXiv:1008.0039. Bibcode:2010AmJPh..78.1278P. doi:10.1119/1.3475685. S2CID 12342287.
  • Perrin, J. (1909). "Mouvement brownien et réalité moléculaire" [Brownian movement and molecular reality]. Annales de chimie et de physique. 8th series. 18: 5–114.
    • See also Perrin's book "Les Atomes" (1914).
  • von Smoluchowski, M. (1906). "Zur kinetischen Theorie der Brownschen Molekularbewegung und der Suspensionen". Annalen der Physik. 21 (14): 756–780. Bibcode:1906AnP...326..756V. doi:10.1002/andp.19063261405.
  • Svedberg, T. (1907). Studien zur Lehre von den kolloiden Losungen.
  • Theile, T. N.
    • Danish version: "Om Anvendelse af mindste Kvadraters Methode i nogle Tilfælde, hvor en Komplikation af visse Slags uensartede tilfældige Fejlkilder giver Fejlene en 'systematisk' Karakter".
    • French version: "Sur la compensation de quelques erreurs quasi-systématiques par la méthodes de moindre carrés" published simultaneously in Vidensk. Selsk. Skr. 5. Rk., naturvid. og mat. Afd., 12:381–408, 1880.

  • Einstein on Brownian Motion
  • Discusses history, botany and physics of Brown's original observations, with videos
  • "Einstein's prediction finally witnessed one century later" : a test to observe the velocity of Brownian motion
  • Large-Scale Brownian Motion Demonstration