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Related Concept Videos

Second Law of Thermodynamics02:49

Second Law of Thermodynamics

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...
Entropy Changes Accompanying Specific Processes01:21

Entropy Changes Accompanying Specific Processes

Entropy, a measure of disorder in a system, changes during phase transitions like freezing or boiling. At the transition temperature Ttrs, where two phases are in equilibrium, the phase transition is a reversible process. The entropy change can be calculated from a substance's enthalpy of transition using the equation ΔStrs = ΔtrsH /Ttrs.When a perfect gas expands isothermally from one volume to another, entropy increases logarithmically with volume. Conversely, isothermal compression results...
Entropy within the Cell01:22

Entropy within the Cell

A living cell's primary tasks of obtaining, transforming, and using energy to do work may seem simple. However, the second law of thermodynamics explains why these tasks are harder than they appear. None of the energy transfers in the universe are completely efficient. In every energy transfer, some amount of energy is lost in a form that is unusable. In most cases, this form is heat energy. Thermodynamically, heat energy is defined as the energy transferred from one system to another that is...
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
The Second Law of Thermodynamics01:14

The Second Law of Thermodynamics

In the quest to identify a property that may reliably predict the spontaneity of a process, a promising candidate has been identified: entropy. Scientists refer to the measure of randomness or disorder within a system as entropy. High entropy means high disorder and low energy. To better understand entropy, think of a student’s bedroom. If no energy or work were put into it, the room would quickly become messy. It would exist in a very disordered state, one of high entropy. Energy must be put...
Entropy02:39

Entropy

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...

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Updated: Jun 8, 2026

Sealable Femtoliter Chamber Arrays for Cell-free Biology
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Sealable Femtoliter Chamber Arrays for Cell-free Biology

Published on: March 11, 2015

Physiological Heterogeneity: Fractals Link Determinism and Randomness in Structures and Functions.

James B Bassingthwaighte1

  • 1Dr. Bassingthwaighte is in the Center for Bioengineering, University of Washington WD-12, Seattle, WA 98195, USA.

News in Physiological Sciences : an International Journal of Physiology Produced Jointly by the International Union of Physiological Sciences and the American Physiological Society
|September 28, 2011
PubMed
Summary
This summary is machine-generated.

Fractals offer a way to measure biological system complexity across different scales. This approach helps characterize spatial and temporal variations in organs and reactions, integrating physiological knowledge.

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Area of Science:

  • Physiology
  • Complex Systems Science
  • Biophysics

Background:

  • Biological systems exhibit spatial and temporal variations in concentrations, flows, and reaction rates.
  • These variations often appear to increase as the scale of observation is refined.
  • Characterizing this heterogeneity independently of scale is a significant challenge in physiology.

Purpose of the Study:

  • To introduce fractal geometry as a method for characterizing biological heterogeneity across different scales.
  • To demonstrate how fractal properties can describe both geometric and kinetic aspects of physiological systems.
  • To explore the potential of fractals in integrating diverse physiological knowledge.

Main Methods:

  • Applying fractal analysis to describe systems where features adhere to the same rules across multiple scales.
  • Utilizing fractal geometry to model spatial variations in organ concentrations or flows.
  • Employing fractal concepts to analyze temporal variations in reaction rates or flows.

Main Results:

  • Fractal analysis provides a robust framework for quantifying heterogeneity irrespective of observational scale.
  • Fractal descriptions efficiently capture complex geometric and kinetic patterns observed in biological systems.
  • The fractal approach facilitates a more unified understanding of physiological processes.

Conclusions:

  • Fractal geometry is a powerful tool for understanding and quantifying biological heterogeneity.
  • This scale-independent characterization enhances the integration of physiological knowledge.
  • Fractals offer a unified perspective on the complex spatial and temporal dynamics within biological systems.