Distribution Functions for Inertial Run‐and‐Tumble Particles in Shear‐Thinning Media

ABSTRACT We study the dynamics of an athermal inertial run‐and‐tumble particle moving through a shear‐thinning medium in one dimension. A symmetric dichotomous noise of strength and flipping rate models the activity of the particle, while the medium's velocity‐dependent viscosity is represented by a Coulomb–tanh function, . Starting from the Fokker–Planck equations corresponding to particle's equation of motion, we analytically derive the steady‐state velocity distribution , and numerically compute the time‐dependent joint probability density function . We also solve the particle's equations of motion to obtain time‐dependent velocity and position distributions, and compare them with the marginal distributions computed from . exhibits multiple transitions as increases for a fixed , eventually approaching a Gaussian‐like profile. We identify these transition points and characterize the degree of non‐Gaussianity by computing the kurtosis.

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Publication Details

Journal
ChemistrySelect
Published
2026-04-01
DOI
https://doi.org/10.1002/slct.202507515
Primary Topic
Micro and Nano Robotics
Type
article
Field-Weighted Citation Impact
0.00

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article

Distribution Functions for Inertial Run‐and‐Tumble Particles in Shear‐Thinning Media

Sayantan Mondal, Prasenjit Das, Subhanker Howlader
ChemistrySelect
Micro and Nano Robotics
article

Distribution Functions for Inertial Run‐and‐Tumble Particles in Shear‐Thinning Media

Sayantan Mondal, Prasenjit Das, Subhanker Howlader
article en

Abstract

ABSTRACT We study the dynamics of an athermal inertial run‐and‐tumble particle moving through a shear‐thinning medium in one dimension. A symmetric dichotomous noise of strength and flipping rate models the activity of the particle, while the medium's velocity‐dependent viscosity is represented by a Coulomb–tanh function, . Starting from the Fokker–Planck equations corresponding to particle's equation of motion, we analytically derive the steady‐state velocity distribution , and numerically compute the time‐dependent joint probability density function . We also solve the particle's equations of motion to obtain time‐dependent velocity and position distributions, and compare them with the marginal distributions computed from . exhibits multiple transitions as increases for a fixed , eventually approaching a Gaussian‐like profile. We identify these transition points and characterize the degree of non‐Gaussianity by computing the kurtosis.

ChemistrySelectVol. 11(14)
Indian Institute of Science Education and Research Mohali (IN)
Indian Institute of Science Education and Research Mohali
Openalex Percentile: Top 18%
Micro and Nano Robotics
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