Jove
Visualize
Contact Us

Related Concept Videos

Entropy02:39

Entropy

34.9K
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...
34.9K
Entropy07:32

Entropy

19.0K
Source: Ketron Mitchell-Wynne, PhD, Asantha Cooray, PhD, Department of Physics & Astronomy, School of Physical Sciences, University of California, Irvine, CA
The second law of thermodynamics is a fundamental law of nature. It states that the entropy of a system always increases over time or remains constant in ideal cases when a system is in a steady state or undergoing a "reversible process." If the system is undergoing an irreversible process, the entropy of the system will...
19.0K
Entropy01:18

Entropy

3.5K
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...
3.5K
Coordination Number and Geometry02:57

Coordination Number and Geometry

18.9K
For transition metal complexes, the coordination number determines the geometry around the central metal ion. Table 1 compares coordination numbers to molecular geometry. The most common structures of the complexes in coordination compounds are octahedral, tetrahedral, and square planar.
18.9K
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

45.4K
VSEPR Theory for Determination of Electron Pair Geometries
45.4K
Standard Entropy Change for a Reaction03:00

Standard Entropy Change for a Reaction

24.0K
Entropy is a state function, so the standard entropy change for a chemical reaction (ΔS°rxn) can be calculated from the difference in standard entropy between the products and the reactants.
24.0K

You might also read

Related Articles

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

Sort by
Same author

A hybrid fuzzy dissimilarity histogram and automatic fuzzy clustering framework for robust color image segmentation.

Scientific reports·2026
Same author

Machine learning based power control in cellular and cell-free massive MIMO systems.

Scientific reports·2026
Same author

Improved lung nodule segmentation with a squeeze excitation dilated attention based residual UNet.

Scientific reports·2025
Same author

All-optical half-subtractor based on photonic crystals.

Applied optics·2019
Same author

Introduction of a Simple Algorithm to Create Synthetic-computed Tomography of the Head from Magnetic Resonance Imaging.

Journal of medical signals and sensors·2019
Same author

Efficient point cloud lossless data compression method based on an embedded Gray code structured light pattern sequence.

Applied optics·2018
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 Experiment Video

Updated: Jan 19, 2026

Entropy; State Property and the Second Law of Thermodynamics
07:32

Entropy; State Property and the Second Law of Thermodynamics

Published on: April 30, 2023

19.0K

Three-dimensional range geometry compression via reduced entropy encoding of the image.

Hossein Rashidizad, Mohmmad Morad Sheikhi, Gholamreza Akbarizadeh

    Applied Optics
    |September 11, 2019
    PubMed
    Summary

    This study introduces a novel method for 3D data compression by analyzing image data structure and redundancy. This approach significantly enhances lossless compression efficiency, achieving a 0.29 bit per point ratio for smooth surfaces.

    More Related Videos

    Entropy and Boltzmann's Theory of Microstates
    02:39

    Entropy and Boltzmann's Theory of Microstates

    34.9K
    Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
    09:23

    Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

    Published on: August 16, 2017

    8.6K

    Related Experiment Videos

    Last Updated: Jan 19, 2026

    Entropy; State Property and the Second Law of Thermodynamics
    07:32

    Entropy; State Property and the Second Law of Thermodynamics

    Published on: April 30, 2023

    19.0K
    Entropy and Boltzmann's Theory of Microstates
    02:39

    Entropy and Boltzmann's Theory of Microstates

    34.9K
    Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans
    09:23

    Quantification of Information Encoded by Gene Expression Levels During Lifespan Modulation Under Broad-range Dietary Restriction in C. elegans

    Published on: August 16, 2017

    8.6K

    Area of Science:

    • Computer Vision
    • Data Compression
    • Geometric Modeling

    Background:

    • Three-dimensional (3D) data compression is crucial for efficient storage and transmission.
    • Existing methods embed 3D range geometry into 2D images, improving compression ratios.
    • Maximizing image capacity (color channels, bit depth) and using advanced algorithms like Free Lossless Image Format (FLIF) are current focuses.

    Purpose of the Study:

    • To propose a new approach for 3D data compression that prioritizes understanding image data structure and redundancy.
    • To demonstrate the superiority of this method over conventional compression algorithms.
    • To achieve significant improvements in lossless compression for 3D data.

    Main Methods:

    • Analyzing the inherent structure and redundancy within image data.
    • Modifying the process of 2D image creation and compression.
    • Exploiting reduced information entropy for enhanced compression.

    Main Results:

    • A significant reduction in the entropy of image information was achieved.
    • The proposed method demonstrates robustness and effectiveness in lossless compression.
    • An impressive compression ratio of 0.29 bit per point was obtained for objects with continuous smooth surfaces.

    Conclusions:

    • Modifying the 2D image creation and compression process by analyzing data structure offers superior lossless compression.
    • This approach unlocks new opportunities for improving 3D data compression efficiency.
    • The experimental results validate the proposed method's effectiveness and robustness.