Thermal noise in aqueous quadrupole micro- and nano-traps
© Park and Krstić; licensee Springer. 2012
- Received: 16 September 2011
- Accepted: 27 February 2012
- Published: 27 February 2012
Recent simulations and experiments with aqueous quadrupole micro-traps have confirmed a possibility for control and localization of motion of a charged particle in a water environment, also predicting a possibility of further reduction of the trap size to tens of nano-meters for trapping charged bio-molecules and DNA segments. We study the random thermal noise due to Brownian motion in water which significantly influences the trapping of particles in an aqueous environment. We derive the exact, closed-form expressions for the thermal fluctuations of position and velocity of a trapped particle and thoroughly examine the properties of the rms for the fluctuations as functions of the system parameters and time. The instantaneous signal transferring mechanism between the velocity and position fluctuations could not be achieved in the previous phase-average approaches.
- aqueous quadrupole trap
- Brownian motion
- random thermal noise
- rms fluctuation
- charged bio-molecule
- DNA sequencing
Conventional quadrupole Paul traps [1, 2] are used to confine the charged particles (e.g., atomic and molecular ions) to narrow three-dimensional (3-D) or two-dimensional (2-D) regions by the combination of static (DC) and radio-frequency (rf, AC) oscillating electric fields in vacuum or in gaseous environment. Their applications include mass spectrometry , quantum information processing [4, 5], micro-dynamical sensors , etc. While 3-D trap confines the charged particles to the trap center, the 2-D (so-called linear) Paul trap confines the particles to the trap axis.
The aqueous Paul nano-trap (APT) is a quadrupole trapping device for the confinement of nano-sized objects in water (and possible electrolyte) using rf electric field. Recent theoretical [7–10] and experimental [11, 12] studies show feasibility of the aqueous Paul traps for localization and control of the motion of charged micro- and nano-particles. Presence of aqueous and possible electrolytic [10, 11] environment is of the key importance for chemical stability of charged bio-molecules. In particular, control of translocation of a single-stranded DNA by APT may improve the performance of the third generation of DNA sequencing devices through synthetic nano-pores [13, 14]. A bio-molecule translocation application determines our interest to a linear Paul trap. The influence of the thermal fluctuations in the dense water environment to the linear (2-D) Paul micro- and nano-trap functions is the main focus of this paper.
where ξ is the friction coefficient of a non-slip spherical particle in Stokes' drag, ξ = 6πηap, η is the viscosity of medium and ap is the radius of the particle. According to the fluctuation-dissipation theorem , the magnitude of the random force is proportional to kBTξ[17, 18] where kB is the Boltzmann constant and T is the liquid temperature. The viscosity of water (8.9 × 10-4 Pa·s) is about 50 times larger than of air (1.78 × 10-5 Pa·s at T = 298 K), i.e., a particle in water experiences about 50 times larger random force than in the air. Therefore, understanding the functions of the Paul trap filled with water (or more general, with a high viscous medium) requires, in addition to the stability analysis based on the mean motion of particle, also a detailed understanding of its response to the thermal fluctuations. Although the mean motion may be stable, i.e., converging to the trap center [10–12], a presence of large thermal fluctuations of the particle may suppress or even prevent its localization and control.
The fluctuations of a charged micro-particle in gaseous quadrupole Paul trap have been studied intensively in the past. Arnolds et al. numerically computed the fluctuation of position by using Langevin equation  and Fokker-Planck equation . They found that the numerical results are in a good agreement with their experimental data in air at atmospheric pressure in tens of Hz range of the applied AC frequencies. Thus, they trapped a few micrometer-sized particles in a millimeter-sized Paul trap (2r 0 = 9 mm) using an AC electric bias of V = 1.0 V and Ω = 60 Hz, resulting in less than 1.0 μm fluctuations. Blatt et al.  and Zerbe et al.  computed the thermal fluctuations of position and velocity by using Fokker-Planck equation in a gas medium in the limit of small b-parameters. Joos and Lindner  derived the series expansions of the thermal fluctuations of position and velocity from the Langevin equation in the limit of small q parameters.
In the present study, we solve the relevant Langevin equation in a closed form analytically in terms of integrals of Mathieu functions [24, 25]. The derived formulas are quite general and applicable to arbitrary range of trap parameters of an aqueous quadrupole trap, enabling us to fully analyze the transient behavior of the thermal fluctuations, their power spectrum density (PSD), position and velocity fluctuations, as well as their covariance.
We consider the linear (2-D) aqueous quadrupole Paul trap because many interesting bio-molecules (e.g., DNA, RNA) are long-charged polymers that could be translocated along the trap axis with localization in the trap center. A generalization to the 3-D Paul trap is obvious and straightforward, and will not be pursued here.
For brevity, R(t) here is the random force component in the x direction. When R(t) = 0, Equation 2 can be reduced, using the transformation , to the Mathieu differential equation, leading to Mathieu functions.
In addition to the charge-dependent electrophoretic force, Q(-∇Φ), a particle in an aqueous environment and in non-uniform electric field could experience the dielectrophoretic (DEP) force due to the difference between dielectric constants and conductivities between particle and the environment. Our analysis showed [10, 26] that the effects of dielectrophoretic force becomes dominant for small values of q (<< 1) (and a) parameters. However, when q > 0.01, the stability of particle is dominated by the electrophoretic force . The Brownian motion including DEP forces is discussed elsewhere .
In this section, the explicit closed-form analytical expressions for thermal fluctuations of position and velocity, and the cross-covariance of position and velocity are derived in terms of integrals of Mathieu functions by solving the equation of motion in Langevin form for a charge particle in an aqueous quadrupole Paul trap.
Thermal fluctuations of the position
where c(a,q,t) and s(a,q,t) are the Mathieu cosine and sine functions, respectively. Hereafter, we use the notation a = -b2 /4. At the RHS of Equation 7, the first two terms express the instantaneous motion of a particle in the Paul trap without influence of random force, while the rest of the equation is due to the thermal fluctuations, i.e., due to the random force R(t). The property of Mathieu functions, c(a,q,t)s'(a,q,t)-s(a,q,t)c'(a,q,t) = 1 , is used in derivation of Equation 7.
Thermal fluctuations of velocity
When q→ 0, at long-time limit. For b→ 0, the velocity fluctuations vanish as the position fluctuations do.
Covariance of position and velocity fluctuations
where the K function is defined in Appendix 3. The influence of the covariance to the fluctuations will be discussed in the next section.
Near the q value, for which the position fluctuation becomes minimum, for example, q = 1.5 for b = 2.0 (Mathieu exponent is μ = 0 + 0.3687i) and q = 3.1 for b = 4.0 (μ = 0 + 1.6262i), the long-time behaviors are not much different except for the reduction of amplitude of the position fluctuations. For (b,q) = (2.0, 1.5) and (4.0, 3.1) as well as for (1.0, 1.0), the Mathieu exponents do not have any real part, and all three fluctuations, , , and , oscillate in phase with angular frequency Ω = 2π /T.
This is exactly the same as the MSD relation for diffusion in absence of the driving field.
As we discussed above, both mean trajectory and its thermal fluctuation amplitude diverge simultaneously in unstable region. The influence of random force to the stability border is negligible. The mean value of has, for all b s, a local minimum (qmin,xx) in the stable region, close to the stability border. The qmin,xx increases with the increase of b. Near stability border, the ratio of maximum to minimum trajectory fluctuation becomes large (approximately 10) for all b s. The temporal histories of σxx, σvv, and σxv for larger b = 4.0 are presented in Figure S2 [see Additional file 1]. With larger b, the minimum values of σxx and σvv are significantly reduced which corresponds to the reduction of minimum σxx with increase of b in Figure 5.
The correlation varies between -1 to 1 since the covariance can be both positive and negative. The variables are positively and negatively correlated for the positive and negative correlation, respectively. Variables x and v could be uncorrelated when correlation is 0. Of course, the larger absolute value of the quantity in Equation 14 indicates the stronger correlation between x and v. It should be averaging of σxv over one period (for example, from A to C in curve c of Figure 6) that gives a numerical zero, unlike the correlation, which does not average to zero since it is a non-linear scaling of σxv by .
In the Figure 6, the zero correlation (covariance) points (A, B, and C) correspond to the minimum σvv positions regardless of σxx. However, the maximum correlation always occurs when σvv has local maximum while σxx is near the mean (A' and B'). In other words, when the velocity fluctuations reach its local maximum, the covariance also becomes maximized, and the velocity fluctuation information is easily transferred to the position fluctuation. The fluctuation embedded in the velocity is very sensitive to the variation of the field (for a given b). Then, the information propagates to the position fluctuation through the covariance. The covariance acts as a diffusion transfer engine (its physical dimension is diffusion).
Once we choose the combination of b and q parameters which provide the stable trap condition, this could be converted into a desired aqueous trap design. Thus, for a polystyrene micro-particle of diameter of 0.8 μm and charge of 106 Q in a Paul trap of 2r0 = 8.0 μm [9, 10], b = 4.26 and q = 0.45 correspond to 1.0 V AC at 2 MHz. These parameters then yield the characteristic length of random motion (as defined below Equation 12) of = 0.61 nm, while the thermal velocity of molecules is = 3.85 × 10-3 m/s. The actual rms values of the position fluctuations can be obtained from Figure 5, i.e., ≈ 4 nm and ≈ 10-3m/s (using Figure 7). On the other hand, for a bio-particle radius of 5 nm and a charge of 5 Q, with the driving frequency of 300 MHz and AC voltage of 1.2 V for a trap of r0 = 40.5 nm, we found that b = 1.12 × 102 and q = 0.37 for which the particle is still stable . These yield is approximately 2.3 nm, while the thermal velocity of molecules is approximately 2.2 m/s. These values are beyond the calculated scaling curves in Figures 5 and 7, and the actual rms values in this case have to be obtained by explicit integrations of the Mathieu functions in Equation 12.
We derive the closed-form analytical expressions for thermal fluctuations of position and velocity of charged particles in aqueous quadruple Paul trap, as well as their covariance starting from Langevin equation with random force. Unlike the conventional Paul trap in vacuum or in air with small random noise, an aqueous Paul trap exhibits relatively large Brownian fluctuations due to the large viscosity in water, depending also on the trap parameters (the trap size, the particle mass and charge, and external electric trapping field amplitude and frequency). The fluctuations are expressed in terms of integrals of the Mathieu cosine and sine functions and their derivatives, applicable for arbitrary values of the dimensionless trap parameters b and q. In the limiting cases, our results agree well to the values in the literature as well as to the theoretical limits of 'no-external force and 'no-damping'. The thermal fluctuations are still oscillating functions even in the long-time limit. Our approach can be easily extended to the 'colored' noise case .
Since our solution is not based on the phase-average approach, we obtain the instantaneous time-dependent coupling between the position and velocity fluctuations. We find that the correlation between position and velocity fluctuations becomes maximized for the maximum velocity fluctuation. Near the unstable region of the trap parameters, the velocity diverges, and this is transferred to the position fluctuation through the covariance. The covariance acts as a diffusion transfer engine. Even though the phase average of covariance is zero, as also indicated in the previous studies [19, 20], the covariance itself is not zero at every instant of time, causing strong correlation between position and velocity fluctuations in the aqueous Paul trap.
A big advantage of the aqueous Paul trap is to provide a 'virtual nano-pore' for control of a nano-dimensioned DNA segment, while the actual physical size of the trap could be in the range of tens of nm. This significantly reduces the fabrication effort of the nano-pores as well as the problems of the interaction of the bio-molecule with the material walls.
Appendix 1. I(b,q,t1) in Equation 8
where F(a,q,u) is the Floquet solution of Mathieu function and a = -b2/4.
Appendix 2. Details of velocity fluctuation, σvv
where t1 is dimensionless time. It should be noted that J3(b,q,t1) = I(b,q,t1), where I(b,q,t1) defines the square of the position fluctuations. Therefore, the square of velocity fluctuations is defined by the sum of a term proportional to the position fluctuations and two more quantities, J1(b,q,t1) and J2(b,q,t1).
Appendix 3. Details of covariance, σxv
where J2(b,q,t1), and J3(b,q,t1) are defined in Appendix 2.
This research was supported by the US National Human Genome Research Institute of the National Institutes of Health under grant no. 1R21HG004764-01 and by US Department of Energy (DOE) at ORNL managed by a UT-Battelle for the US DOE under contract no. DEAC05-00OR22725 by the US DOE. JHP acknowledges support through ORNL Postdoctoral Program, administered by ORISE. This research was supported by an allocation of advanced computing resources supported by the National Science Foundation.
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