Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Free Energy Changes for Nonstandard States03:25

Free Energy Changes for Nonstandard States

13.8K
The free energy change for a process taking place with reactants and products present under nonstandard conditions (pressures other than 1 bar; concentrations other than 1 M) is related to the standard free energy change according to this equation:
13.8K
Oscillations about an Equilibrium Position01:04

Oscillations about an Equilibrium Position

7.2K
Stability is an important concept in oscillation. If an equilibrium point is stable, a slight disturbance of an object that is initially at the stable equilibrium point will cause the object to oscillate around that point. For an unstable equilibrium point, if the object is disturbed slightly, it will not return to the equilibrium point. There are three conditions for equilibrium points—stable, unstable, and half-stable. A half-stable equilibrium point is also unstable, but is named so...
7.2K
The Entropy as a State Function01:14

The Entropy as a State Function

67
Consider an arbitrary process that moves between two specific states (A and B) in a cyclic manner. This process is reversible and broken down into smaller parts that each follow a Carnot cycle. A Carnot cycle has two isothermal (constant temperature) processes. During these processes, the ratio of the amount of heat transferred to their respective temperature remains constant. The other two processes in the Carnot cycle are also reversible but adiabatic, which means they occur without any heat...
67
Second Law of Thermodynamics02:49

Second Law of Thermodynamics

28.0K
In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Processes that involve an increase in entropy of the system (ΔS > 0) are very often spontaneous; however, examples to the contrary are plentiful. By expanding consideration of entropy changes to include the surroundings, a significant conclusion regarding the relation between this property and spontaneity may be reached. In thermodynamic models, the...
28.0K
Entropy02:39

Entropy

37.5K
Salt particles that have dissolved in water never spontaneously come back together in solution to reform solid particles. Moreover, a gas that has expanded in a vacuum remains dispersed and never spontaneously reassembles. The unidirectional nature of these phenomena is the result of a thermodynamic state function called entropy (S). Entropy is the measure of the extent to which the energy is dispersed throughout a system, or in other words, it is proportional to the degree of disorder of a...
37.5K
Entropy01:18

Entropy

3.8K
The first law of thermodynamics is quantitatively formulated via an equation relating the internal energy of a system, the heat exchanged by it, and the work done on it. A quantitative formulation of the second law of thermodynamics leads to defining a state function, the entropy.
When an ideal gas expands isothermally, the disorder in the gas increases. From the molecular perspective, the gas molecules have more volume to move around in.
Consider an infinitesimal step in the expansion, which...
3.8K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Evidence-Based Clinical Recommendations for the Appropriate Use of Diagnostic Tests in Pediatric Allergology: Focus on Asthma, Rhinoconjunctivitis, and Keratoconjunctivitis Vernal.

Journal of clinical medicine·2026
Same author

Integrated covariances as excess observables weighted by currents and activities.

Physical review. E·2026
Same author

Estimating the Post-Mortem Interval Under Extreme Heat Environments: A Climate-Adaptive Case Series Based on Artificial Intelligence-Supported Diagnostics.

Diagnostics (Basel, Switzerland)·2026
Same author

Generative artificial intelligence in forensic medicine: a pilot study on AI-simulated medico-legal reports in healthcare liability cases.

International journal of legal medicine·2026
Same author

Synchronization of thermodynamically consistent stochastic phase oscillators.

Physical review. E·2026
Same author

Nonequilibrium fluctuation-response relations for state-current correlations.

Physical review. E·2026
Same journal

Erratum: Low-dimensional model for adaptive networks of spiking neurons [Phys. Rev. E 111, 014422 (2025)].

Physical review. E·2026
Same journal

Disentangling the effects of many-body forces on depletion interactions.

Physical review. E·2026
Same journal

Charge transport and mode transition in dual-energy electron beam diodes.

Physical review. E·2026
Same journal

Optimization of multisite reactions in complex compartmentalized media.

Physical review. E·2026
Same journal

Origin of geometric cohesion in nonconvex granular materials: Interplay between interdigitation and rotational constraints enhancing frictional stability.

Physical review. E·2026
Same journal

Interaction of walkers with a standing Faraday wave.

Physical review. E·2026
See all related articles

Related Experiment Video

Updated: Mar 21, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

9.1K

Nonequilibrium fluctuation-response relations for state observables.

Krzysztof Ptaszyński1, Timur Aslyamov2, Massimiliano Esposito2

  • 1Institute of Molecular Physics, Polish Academy of Sciences, Mariana Smoluchowskiego 17, 60-179 Poznań, Poland.

Physical Review. E
|March 20, 2026
PubMed
Summary
This summary is machine-generated.

We derived fluctuation-response relations for Markov jump processes, connecting system fluctuations to external perturbations. These findings establish bounds on state observable fluctuations and offer insights into their topological origins.

More Related Videos

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts
08:43

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

Published on: December 1, 2018

12.1K
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

15.1K

Related Experiment Videos

Last Updated: Mar 21, 2026

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids
11:03

An Analog Macroscopic Technique for Studying Molecular Hydrodynamic Processes in Dense Gases and Liquids

Published on: December 4, 2017

9.1K
A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts
08:43

A Fluorescence Fluctuation Spectroscopy Assay of Protein-Protein Interactions at Cell-Cell Contacts

Published on: December 1, 2018

12.1K
Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators
09:23

Quantum State Engineering of Light with Continuous-wave Optical Parametric Oscillators

Published on: May 30, 2014

15.1K

Area of Science:

  • Non-equilibrium statistical mechanics
  • Theoretical chemistry
  • Physical chemistry

Background:

  • Time-integrated state observables are crucial for understanding systems in chemical sensing and fluorescence spectroscopy.
  • These observables quantify the system's time spent in specific states.

Purpose of the Study:

  • To derive exact fluctuation-response relations for Markov jump processes in non-equilibrium steady states.
  • To establish bounds on fluctuations of time-integrated state observables.
  • To elucidate the mechanistic origin and topological dependence of these fluctuations.

Main Methods:

  • Derivation of exact identities connecting fluctuations and responses.
  • Analysis of Markov jump processes in non-equilibrium steady states.
  • Application of fluctuation-response relations to derive bounds.

Main Results:

  • Established fluctuation-response relations for time-integrated state observables.
  • Derived upper and lower bounds on the fluctuations of these observables.
  • Demonstrated that fluctuation properties depend solely on system topology.

Conclusions:

  • The derived identities offer a deeper mechanistic understanding of fluctuations.
  • Topological properties significantly influence fluctuation characteristics.
  • These findings are relevant for model inference using experimental data, particularly in chemical sensing and spectroscopy.