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Visually Based Characterization of the Incipient Particle Motion in Regular Substrates: From Laminar to Turbulent Conditions
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Characteristic features of self-avoiding active Brownian polymers under linear shear flow
Arindam Panda1, Roland G Winkler2, Sunil P Singh1
1Department of Physics, Indian Institute of Science Education and Research, Bhopal 462 066, Madhya Pradesh, India. arindam19@iiserb.ac.in.
Soft Matter
|October 31, 2023
Summary
Active noise significantly alters flexible polymer behavior under shear flow, changing stretching, alignment, and viscosity. These active polymers exhibit distinct scaling laws compared to passive ones, especially at high activity and shear rates.
Area of Science:
- Polymer Physics
- Soft Matter Physics
- Computational Physics
Background:
- Understanding polymer dynamics under flow is crucial for materials science.
- Active noise introduces non-equilibrium effects not present in passive systems.
- Excluded-volume interactions play a key role in polymer conformation.
Purpose of the Study:
- To investigate the impact of active noise on a flexible linear polymer's behavior under shear flow.
- To analyze changes in conformational and dynamical properties due to active noise.
- To compare simulation results with analytical predictions.
Main Methods:
- Brownian dynamics simulations were employed.
- Excluded-volume interactions were included.
- The effects of varying activity and shear rates were studied.
Main Results:
- Active noise reduces shear-induced stretching and causes non-monotonic behavior.
- Polymer compression in the gradient direction scales as ~WiPe-3/4, differing from passive systems.
- Shear-induced alignment and viscosity exhibit distinct scaling laws compared to passive polymers.
- Non-monotonic zero-shear viscosity behavior was observed with increasing activity.
Conclusions:
- Active noise fundamentally alters polymer dynamics and conformation under shear flow.
- The study reveals new scaling behaviors for active polymers in shear flow.
- Simulation results align well with analytical predictions, validating the model.
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