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This summary is machine-generated.

Anisotropic DNA origami rotors can function as Brownian motors, harnessing random fluctuations for directed motion. At higher electrical field amplitudes, they transition to deterministic electrical motor behavior.

Keywords:
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Area of Science:

  • Molecular nanotechnology
  • Nanoscale devices
  • Biophysics

Background:

  • Molecular devices with anisotropic periodic potentials can act as Brownian motors.
  • Directed rotatory movement was previously shown in DNA origami rotors with ratchet-like obstacles.
  • Brownian motors utilize random fluctuations for directed motion when subjected to external forces.

Purpose of the Study:

  • To investigate if the intrinsic anisotropy of DNA origami rotors is sufficient for motor movement.
  • To explore the transition between Brownian motor and deterministic electrical motor behavior.
  • To characterize the influence of external switching field amplitude and frequency on rotor dynamics.

Main Methods:

  • Utilizing DNA origami rotors with intrinsic anisotropy.
  • Applying an external electrical switching field.
  • Characterizing rotor movement by analyzing angular speed dependence on field amplitude and frequency.
  • Modeling rotor dynamics using a stochastic model.

Main Results:

  • Intrinsic anisotropy of DNA origami rotors is sufficient to induce motor movement.
  • At low field amplitudes, rotors behave as Brownian motors.
  • At higher field amplitudes, rotors exhibit deterministic overdamped electrical motor behavior.
  • Rotor angular speed initially increases with amplitude and frequency, then peaks and decreases.
  • Stochastic modeling accurately describes the observed rotor movement.

Conclusions:

  • DNA origami rotors can operate as Brownian motors solely based on their intrinsic anisotropy.
  • The system exhibits a transition from stochastic Brownian motion to deterministic electrical motor behavior with increasing driving field strength.
  • The study provides a comprehensive characterization of rotor dynamics and validates a stochastic model for its description.