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Simulation of a Brownian particle in an optical trap
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Image of Fig. 1.
Fig. 1.

As the time step decreases, we must employ larger values of the Gaussian white noise to approximate the solution of the free diffusion equation [Eq. (3) ] accurately. (a) , (b) 0.5, and (c) 0.1. The corresponding solutions of the finite difference free diffusion equation [Eq. (5) ] in (d)–(f) for (lines) behave similarly. Although these solutions differ because they are specific realizations of a random process, their statistical properties do not change, as can be seen by comparing the shaded areas, which show the regions within one standard deviation of the mean of 10,000 realizations.

Image of Fig. 2.
Fig. 2.

(a) For times smaller or comparable to the inertial time the trajectory of a particle with inertia (solid line) appears smooth. In contrast, in the absence of inertia (dashed line) the trajectory is ragged and discontinuous. (b) For times significantly longer than both the trajectory with inertia (solid line) and without inertia (dashed line) are jagged, because the microscopic details are not resolvable. These trajectories are computed using Eqs. (8) and (10) with and the same realization of the white noise so that the two trajectories can be compared. (c) The velocity autocorrelation function [Eq. (12) ] for a particle with inertia (solid line) decays to zero with the time constant , while for a particle without inertia (dashed line) it drops immediately to zero demonstrating that its velocity is not correlated and does not have a characteristic time scale. (d) A log-log plot of the mean-square displacement [Eq. (14) ] for a particle with inertia (solid line) shows a transition from quadratic behavior at short times to linear behavior at long times, while for a particle without inertia (dashed line) it is always linear. The particle parameters are R = 1 μm, m = 11 pg, , T = 300 K, and are used here and for the numerical solutions shown in the following figures.

Image of Fig. 3.
Fig. 3.

(a) Trajectory of a Brownian particle in an optical trap ( and ). The particle explores an ellipsoidal volume around the center of the trap, as evidenced by the shaded area which represents an equiprobability surface. (b) and (c) The probability distributions of finding the particle in the z- and y-planes follow a two-dimensional Gaussian distribution around the trap center.

Image of Fig. 4.
Fig. 4.

(a) As the trap stiffness increases, the particles become more and more confined as shown by the theoretical (solid curve) and numerical (symbols) variance of the particle position around the trap center in the y-plane and, in particular, by the probability distributions corresponding to (b) , (c) , and (d) .

Image of Fig. 5.
Fig. 5.

(a) The position autocorrelation function of a trapped particle [Eq. (23) ] gives information about the effect of the trap restoring force on the particle motion. As the trap stiffness and, therefore, the restoring force are increased, the characteristic decay time of the position autocorrelation function decreases. (b) The mean square displacement, unlike for the free diffusion case, does not increase indefinitely but reaches a plateau, which also depends on the trap stiffness—the stronger the trap, the sooner the plateau is reached.

Image of Fig. 6.
Fig. 6.

(a) The probability distribution of an optically trapped particle shifts in response to an external force. The black histogram shows the initial distribution and the grey histogram represents the distribution after the application of a constant external force . (b) The position autocorrelation function of the trapped particle [black line, Eq. (23) ] and position cross-correlation function [grey line, Eq. (20) ], are modulated in the presence of a rotational force field such as the one in Eq. (19) with . This modulation demonstrates the presence of the rotational force field even though it is not clear from the trajectory (the inset shows a trajectory during a time interval equal to 0.1 s). (c) Dynamic transitions between the two equilibrium positions in a double-well potential (Kramers transitions) with and [Eq. (21) ].


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752b84549af89a08dbdd7fdb8b9568b5 journal.articlezxybnytfddd
Scitation: Simulation of a Brownian particle in an optical trap