Bayesian inference of the viscoelastic properties of a Jeffrey’s fluid using optical tweezers

Paul, S.; Narinder, Narinder; Banerjee, Ayan; Nayak, K. Rajesh; Steindl, Jakob; Bechinger, Clemens

doi:10.1038/s41598-021-81094-x

Cited by 10 publications

(7 citation statements)

References 43 publications

(71 reference statements)

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“…For the memory kernel Γ, we consider the Jeffrey's fluid model of viscoelasticity [30,47,[50][51][52]. According to this model [69][70][71][72]:…”

Section: Resultsmentioning

confidence: 99%

Enhanced directionality of active processes in a viscoelastic bath

Das,

Paul,

Manikandan

et al. 2023

New J. Phys.

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Active fluctuations are known to play a significant role in the intracellular transport of passive objects. However, the effect of viscoelasticity of the environment in shaping such processes is relatively less understood. Here, with a minimal experiment using a driven colloid in a viscoelastic bath, we show that viscoelasticity significantly increases the mean injected power to the passive object (~ 50% compared to a viscous medium), for the same strength of the external driving. Additionally, we observe a notable reduction in negative work fluctuations across a wide range of driving amplitudes. These findings collectively suggest an enhanced directionality in driven processes within a viscoelastic bath, which we attribute to the emergence of interactions between the colloid and the viscoelastic medium.

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“…For the memory kernel Γ, we consider the Jeffrey's fluid model of viscoelasticity [30,47,[50][51][52]. According to this model [69][70][71][72]:…”

Section: Resultsmentioning

confidence: 99%

Enhanced directionality of active processes in a viscoelastic bath

Das,

Paul,

Manikandan

et al. 2023

New J. Phys.

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“…where k B is the Boltzmann constant and T is the temperature of the bath [41]. We consider the Jeffrey's fluid model as a viscoelastic model where the memory Kernel should be [38,[41][42][43]:…”

Section: Theoretical Modelmentioning

confidence: 99%

“…By introducing an auxiliary variable X(t), Eq. (2), in the overdamped limit, can be expressed as a multivariate Ornstein-Uhlenbeck process (see the appendix A) which follows the differential equation [43] Ẏ…”

Section: Theoretical Modelmentioning

confidence: 99%

Work fluctuations of Brownian harmonic oscillators driven by "active-like'' viscoelastic baths

Das¹,

Banerjee²,

Paul³

2023

Preprint

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We experimentally and theoretically investigate the dynamics and work fluctuations of an optically trapped micro-particle in a viscoelastic medium driven by an external Ornstein-Uhlenbeck (OU) noise. Compared to a viscous medium of the same zero-shear viscosity, we find a significant increase (∼ 50%) in the mean transferred power from the external forcing of a fixed noise strength to the particle due to the slow relaxation process of the viscoelastic medium considered in this study. The memory of the viscoelastic medium also leads to a small increase in the effective temperature of the particle and a high fluctuation in the work done by the external forcing. Consequently, as opposed to a viscous medium, negative work-fluctuations reduce extremely fast. This leads to the violation of the work fluctuation theorem (WFT) for a very small external noise, which may even be only ∼ 25% of the thermal noise strength in a non-Markovian viscoelastic bath. We further show that all the experimental findings can be described excellently from the Langevin dynamics of the process. Finally, we formulate the appropriate WFT for such a system from the calculated work probability density function.

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“…which models the rheological response of several viscoelastic fluids, such as wormlike micelles [42,59,60], some polymer solutions [61,62], and to a great extent, the linear viscoelasticity over certain time intervals of intracellular fluids [63,64], block copolymers [65], and λ-phage DNA [42,66], where τ 0 is the relaxation time of their elastic microstructure, whereas η 0 and η ∞ represent the zero-shear viscosity and the background solvent viscosity, respectively. Therefore, the corresponding friction memory kernel is…”

Section: Modelmentioning

confidence: 99%

“…To ensure that the system is always in a time-periodic nonequilibrium steady state independent of the choice of the initial condition, the first 10 4 cycles are left out and the origin of time is shifted to the beginning of the (10 4 + 1)−st cycle. Furthermore, without loss of generality we choose constant values of the low and high-frequencies viscosities that are typical of viscoelastic fluids prepared in aqueous solution in semidilute regimes [42,62,66,70,71]: η 0 = 0.040 Pa s and η ∞ = 0.004 Pa s, which correspond to α = 9. The diameter of the colloidal particle is set to a = 0.5 µm, while the maximum and minimum values of the trap stiffness during the Stirling cycle are chosen as κ M = 5 pN µm −1 and κ m = 1 pN µm −1 , respectively, which are easily accessible with optical tweezers [52].…”

Section: Numerical Solutionmentioning

confidence: 99%

Work Extraction and Performance of Colloidal Heat Engines in Viscoelastic Baths

Gomez-Solano

2021

Front. Phys.

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A colloidal particle embedded in a fluid can be used as a microscopic heat engine by means of a sequence of cyclic transformations imposed by an optical trap. We investigate a model for the operation of such kind of Brownian engines when the surrounding medium is viscoelastic, which endows the particle dynamics with memory friction. We analyze the effect of the relaxation time of the fluid on the performance of the colloidal engine under finite-time Stirling cycles. We find that, due to the frequency-dependence of the friction in viscoelastic fluids, the mean power delivered by the engine and its efficiency can be highly enhanced as compared to those in a viscous environment with the same zero-shear viscosity. In addition, with increasing fluid relaxation time the interval of cycle times at which positive power output can be delivered by the engine broadens. Our results reveal the importance of the transient behavior of the friction experienced by a Brownian heat engine in a complex fluid, which cannot be neglected when driven by thermodynamic cycles of finite duration.

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Bayesian inference of the viscoelastic properties of a Jeffrey’s fluid using optical tweezers

Cited by 10 publications

References 43 publications

Enhanced directionality of active processes in a viscoelastic bath

Enhanced directionality of active processes in a viscoelastic bath

Work fluctuations of Brownian harmonic oscillators driven by "active-like'' viscoelastic baths

Work Extraction and Performance of Colloidal Heat Engines in Viscoelastic Baths

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