The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Brownian Carnot engine
Open accessAbstract
Despite the simplicity of the Carnot cycle, realizing it at the microscale is complicated by the difficulty in implementing adiabatic processes. A clever solution subjects a charged particle to a noisy electrostatic force that mimics a thermal bath. The Carnot cycle imposes a fundamental upper limit to the efficiency of a macroscopic motor operating between two thermal baths1. However, this bound needs to be reinterpreted at microscopic scales, where molecular bio-motors2 and some artificial micro-engines3,4,5 operate. As described by stochastic thermodynamics6,7, energy transfers in microscopic systems are random and thermal fluctuations induce transient decreases of entropy, allowing for possible violations of the Carnot limit8. Here we report an experimental realization of a Carnot engine with a single optically trapped Brownian particle as the working substance. We present an exhaustive study of the energetics of the engine and analyse the fluctuations of the finite-time efficiency, showing that the Carnot bound can be surpassed for a small number of non-equilibrium cycles. As its macroscopic counterpart, the energetics of our Carnot device exhibits basic properties that one would expect to observe in any microscopic energy transducer operating with baths at different temperatures9,10,11. Our results characterize the sources of irreversibility in the engine and the statistical properties of the efficiency—an insight that could inspire new strategies in the design of efficient nano-motors.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Stochastic thermodynamics, fluctuation theorems and molecular machines
Seifert, U. (2012) · Reports on Progress in Physics · doi:10.1088/0034-4885/75/12/126001 · Cited by 3,300
Mechanics of Motor Proteins and the Cytoskeleton
Howard, & Clark (2002) · Applied Mechanics Reviews · doi:10.1115/1.1451234 · Cited by 2,909 · cited 2×
“However, this bound needs to be reinterpreted at microscopic scales, where molecular bio-motors [2] and some artificial micro-engines [3–5] operate.”
BackgroundEfficiency of a Carnot engine at maximum power output
Curzon, F. L., & Ahlborn, B. (1975) · American Journal of Physics · doi:10.1119/1.10023 · Cited by 2,317
A Logic-Gated Nanorobot for Targeted Transport of Molecular Payloads
Douglas, S. M., Bachelet, I., & Church, G. M. (2012) · Science · doi:10.1126/science.1214081 · Cited by 2,180
Molecular biomimetics: nanotechnology through biology
Sarikaya, M., Tamerler, C., Jen, A. K. Y., Schulten, K., & Baneyx, F. (2003) · Nature Materials · doi:10.1038/nmat964 · Cited by 1,595
Toyabe, S., Sagawa, T., Ueda, M., Muneyuki, E., & Sano, M. (2010) · Nature Physics · doi:10.1038/nphys1821 · Cited by 980
Wang, G. M., Sevick, E. M., Mittag, E., Searles, D. J., & Evans, D. J. (2002) · Physical Review Letters · doi:10.1103/physrevlett.89.050601 · Cited by 936
Nanoscale Heat Engine Beyond the Carnot Limit
Roßnagel, J., Abah, O., Schmidt-Kaler, F., Singer, K., & Lutz, E. (2014) · Physical Review Letters · doi:10.1103/physrevlett.112.030602 · Cited by 632
Subkelvin Parametric Feedback Cooling of a Laser-Trapped Nanoparticle
Gieseler, J., Deutsch, B., Quidant, R., & Novotny, L. (2012) · Physical Review Letters · doi:10.1103/physrevlett.109.103603 · Cited by 616
Efficiency at Maximum Power of Low-Dissipation Carnot Engines
Esposito, M., Kawai, R., Lindenberg, K., & Van den Broeck, C. (2010) · Physical Review Letters · doi:10.1103/physrevlett.105.150603 · Cited by 546
Cited by
Optical tweezers — from calibration to applications: a tutorial
Gieseler, J., Gomez-Solano, J. R., Magazzù, A., Pérez Castillo, I., Pérez García, L., Gironella-Torrent, M., Viader-Godoy, X., Ritort, F., Pesce, G., Arzola, A. V., et al. (2021) · Advances in Optics and Photonics · doi:10.1364/aop.394888 · Cited by 282
Cost of counterdiabatic driving and work output
Zheng, Y., Campbell, S., De Chiara, G., & Poletti, D. (2016) · Physical Review A · doi:10.1103/physreva.94.042132 · Cited by 103
Metadata
- Authors
- Martínez, I. A., Roldán, É., Dinis, L., Petrov, D., Parrondo, J. M. R., & Rica, R. A.
- Venue
- Nature Physics
- Published
- 2015-10-26
- Volume
- 12
- Issue
- 1
- Pages
- 67-70
- ISSN
- 1745-2473 (ISSN-L)
- 1745-2481
- OpenAlex
- W1886609033
- Cited by
- 431
- References
- 24
- FWCI
- 21.59
- Type
- journal article
- Language
- en
- Open access
- bronze
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Brownian Carnot engine. https://app.cochat.ai/papers/10.1038/nphys3518
- The paper
- Martínez, I. A., Roldán, É., Dinis, L., Petrov, D., Parrondo, J. M. R., & Rica, R. A. (2016). Brownian Carnot engine. Nature Physics. https://doi.org/10.1038/nphys3518
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.