J
John Bechhoefer
Researcher at Simon Fraser University
Publications - 139
Citations - 8411
John Bechhoefer is an academic researcher from Simon Fraser University. The author has contributed to research in topics: DNA replication & Liquid crystal. The author has an hindex of 36, co-authored 133 publications receiving 7487 citations. Previous affiliations of John Bechhoefer include University of Chicago & University of British Columbia.
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Journal Article
Critical Behavior of the Banded-Unbanded Spherulite Transition in a Mixture of Ethylene Carbonate with Polyacrylonitrile
TL;DR: It is shown that the band spacing diverges with a power-law form showing scaling over nearly two decades, and it is observed that the bands disorder as the transition point is approached.
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Comment on "direct measurement of the oscillation frequency in an optical-tweezers trap by parametric excitation".
TL;DR: Experimental evidence shows no peak in the variance, in contrast to the observations of Joykutty et al, and suggests that the observations are not due to parametric resonance and suggest that some other feature of their apparatus must be responsible for the data that they present.
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Bayesian Information Engine that Optimally Exploits Noisy Measurements.
TL;DR: In this paper , an information engine consisting of an optically trapped, heavy bead in water was experimentally realized, and the device raised the trap center after a favorable "up" thermal fluctuation, thereby increasing the bead's average gravitational potential energy.
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Using Nematic Director Fluctuations as a Sensitive Probe of the Nematic-Smectic-a Phase Transition in Liquid Crystals
Anand Yethiraj,John Bechhoefer +1 more
TL;DR: In this article, the nematic-smectic-A (NA) phase transition in liquid crystals has been studied using an extremely sensitive optical method, and it has been shown that the NA transition in the liquid crystal 8CB is clearly first order.
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Inferring potential landscapes from noisy trajectories of particles within an optical feedback trap
TL;DR: In this article , a Bayesian method to infer potentials from trajectories corrupted by Markovian measurement noise without assuming prior functional form on the potentials is proposed, which is validated on 1D and 2D experimental trajectories for particles in a feedback trap.