Evidence map›Paper›PMID 32081861›Full record

ArticleNature communications2020

Efficiency fluctuations and noise induced refrigerator-to-heater transition in information engines.

Govind Paneru, Sandipan Dutta, Takahiro Sagawa, Tsvi Tlusty, Hyuk Kyu Pak

Abstract read
In one paragraph

Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Maximizing power and velocity of an information engine.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Govind PaneruCenter for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, 44919, Republic of Korea.ORCID http://orcid.org/0000-0002-2830-7982
Sandipan DuttaCenter for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, 44919, Republic of Korea.ORCID http://orcid.org/0000-0002-4530-6682
Takahiro SagawaDepartment of Applied Physics, University of Tokyo, Tokyo, 113-8656, Japan.
Tsvi TlustyCenter for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, 44919, Republic of Korea. tsvitlusty@gmail.com.ORCID http://orcid.org/0000-0002-9662-3725
Hyuk Kyu PakCenter for Soft and Living Matter, Institute for Basic Science (IBS), Ulsan, 44919, Republic of Korea. hyuk.k.pak@gmail.com.ORCID http://orcid.org/0000-0001-9808-0616

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding noisy information engines is a fundamental problem of non-equilibrium physics, particularly in biomolecular systems agitated by thermal and active fluctuations in the cell. By the generalized second law of thermodynamics, the efficiency of these engines is bounded by the mutual information passing through their noisy feedback loop. Yet, direct measurement of the interplay between mutual information and energy has so far been elusive. To allow such examination, we explore here the entire phase-space of a noisy colloidal information engine, and study efficiency fluctuations due to the stochasticity of the mutual information and extracted work. We find that the average efficiency is maximal for non-zero noise level, at which the distribution of efficiency switches from bimodal to unimodal, and the stochastic efficiency often exceeds unity. We identify a line of anomalous, noise-driven equilibrium states that defines a refrigerator-to-heater transition, and test the generalized integral fluctuation theorem for continuous engines.

Identifiers

PMID32081861
PMCPMC7035421

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.