Next Article in Journal
Information Theory for Human and Social Processes
Next Article in Special Issue
Cusp of Non-Gaussian Density of Particles for a Diffusing Diffusivity Model
Previous Article in Journal
Machine Learning Algorithms for Prediction of the Quality of Transmission in Optical Networks
Previous Article in Special Issue
Impact of Feature Choice on Machine Learning Classification of Fractional Anomalous Diffusion

Extracting Work Optimally with Imprecise Measurements

Grupo Interdisciplinar de Sistemas Complejos, Facultad de Ciencias Físicas, 28040 Madrid, Spain
Departamento de Estructura de la Materia, Física Térmica y Electrónica, Universidad Complutense de Madrid, 28040 Madrid, Spain
Author to whom correspondence should be addressed.
Entropy 2021, 23(1), 8;
Received: 30 October 2020 / Revised: 18 December 2020 / Accepted: 19 December 2020 / Published: 23 December 2020
(This article belongs to the Special Issue Recent Advances in Single-Particle Tracking: Experiment and Analysis)
Measurement and feedback allows for an external agent to extract work from a system in contact with a single thermal bath. The maximum amount of work that can be extracted in a single measurement and the corresponding feedback loop is given by the information that is acquired via the measurement, a result that manifests the close relation between information theory and stochastic thermodynamics. In this paper, we show how to reversibly confine a Brownian particle in an optical tweezer potential and then extract the corresponding increase of the free energy as work. By repeatedly tracking the position of the particle and modifying the potential accordingly, we can extract work optimally, even with a high degree of inaccuracy in the measurements. View Full-Text
Keywords: confinement; information theory; Brownian particle; stochastic thermodynamics confinement; information theory; Brownian particle; stochastic thermodynamics
Show Figures

Figure 1

MDPI and ACS Style

Dinis, L.; Parrondo, J.M.R. Extracting Work Optimally with Imprecise Measurements. Entropy 2021, 23, 8.

AMA Style

Dinis L, Parrondo JMR. Extracting Work Optimally with Imprecise Measurements. Entropy. 2021; 23(1):8.

Chicago/Turabian Style

Dinis, Luis, and Juan M.R. Parrondo 2021. "Extracting Work Optimally with Imprecise Measurements" Entropy 23, no. 1: 8.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop