Entropy: Wikis


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Ice melting is a common example of "entropy increasing",[1] described in 1862 by Rudolf Clausius as an increase in the disgregation of the molecules of the body of ice.[2]
Entropy articles

Entropy is a measure of how organized or disorganized a system is: "Gain of entropy eventually is nothing more nor less than loss of information"[3]

Entropy is an important part of the second law of thermodynamics. Thermodynamic systems consist of objects, e.g. atoms or molecules, which "carry" energy. In applied thermodynamics, as a matter of convention, entropy is measured in Joules of energy per kelvin (a unit of temperature). If thermodynamic systems are described using thermal energy instead of temperature, then entropy is just a number by which the thermal energy in the system is multiplied. The resulting energy is an energy for which no information is available which would be required to convert the energy in technical systems from one form (e.g. electrical) into another form (e.g. mechanical).

In technical applications, machines basically are energy conversion devices. Thus, such devices only can be driven by convertible energy. The same applies to biological organisms. The product of thermal energy (or the equivalents of thermal energy) and entropy is "already converted energy". This is the reason why Rudolf Clausius in 1850 coined the term "entropy" based on the Greek εντροπία [entropía], from εν~ [en~] and τροπή [tropē] (turn, conversion).[4]


Thermodynamics and Statistical Mechanics

There are two related definitions of entropy: the thermodynamic definition and the statistical mechanics definition. The thermodynamic definition essentially describes how to measure the entropy of an isolated system in thermodynamic equilibrium. Importantly, it makes no reference to the microscopic nature of matter. The statistical definition was developed by Ludwig Boltzmann in the 1870s by analyzing the statistical behavior of the microscopic components of the system. Boltzmann went on to show that this definition of entropy was equivalent to the thermodynamic entropy to within a constant number which has since been known as Boltzmann's constant. In summary, the thermodynamic definition of entropy is the fundamental definition of entropy, while the statistical definition of entropy extends the concept, providing an explanation and a deeper understanding of its nature.

Thermodynamic entropy is a non-conserved state function that is of great importance in the sciences of physics and chemistry.[5][6] Historically, the concept of entropy evolved in order to explain why some processes are spontaneous and others are not; systems tend to progress in the direction of increasing entropy.[7] Entropy is as such a function of a system's tendency towards spontaneous change.[8][7] For isolated systems, entropy never decreases.[6] This fact has several important consequences in science: first, it prohibits "perpetual motion" machines; and second, it suggests an arrow of time. Increases in entropy correspond to irreversible changes in a system, because some energy must be expended as waste heat, limiting the amount of work a system can do.[9][10][11][9][12][13]

In statistical mechanics, entropy is essentially a measure of the number of ways in which a system may be arranged, often taken to be a measure of "disorder" (the higher the entropy, the higher the disorder).[14][15][16][5][17][11][10] This definition describes the entropy as a measure of the number of possible microscopic configurations of the individual atoms and molecules of the system (microstates) which would give rise to the observed macroscopic state (macrostate) of the system.

An everyday example of entropy can be seen in mixing salt and pepper in a bag. Separate clusters of salt and pepper will tend to progress to a mixture if the bag is shaken. Furthermore, this process is thermodynamically irreversible. The separation of the mixture into separate salt and pepper clusters via the random process of shaking is statistically improbable and practically impossible because the mixture has higher entropy.

Entropy and the Second Law

The second law of thermodynamics states that the total entropy of any system cannot decrease other than by increasing the entropy of some other system. Hence, in a system isolated from its environment, the entropy of that system cannot decrease. It follows that heat cannot flow from a colder body to a hotter body without the application of work (the imposition of order) to the colder body. Secondly, it is impossible for any device operating on a cycle to produce net work from a single temperature reservoir; the production of net work requires flow of heat from a hotter reservoir to a colder reservoir. As a result, there is no possibility of a "perpetual motion" system. Finally, it follows that a reduction in the increase of entropy in a specified process, such as a chemical reaction, means that it is energetically more efficient.

It follows from the second law of thermodynamics that the entropy of a system that is not isolated may decrease. An air conditioner, for example, may cool the air in a room, thus reducing the entropy of the air of that system. The heat expelled from the room (the system), involved in the operation of the air conditioner, will always make a bigger contribution to the entropy of the environment than will the decrease of the entropy of the air of that system. Thus, the total of entropy of the room plus the entropy of the environment increases, in agreement with the second law of thermodynamics.

In mechanics, the second law in conjunction with the fundamental thermodynamic relation places limits on a system's ability to do useful work.[18] The entropy change of a system at temperature T absorbing an infinitesimal amount of heat δq in a reversible way, is given by \frac{\delta q}{T}. More explicitly, an energy TRS is not available to do useful work, where TR is the temperature of the coldest accessible reservoir or heat sink external to the system. For further discussion, see Exergy.

Statistical mechanics demonstrates that entropy is governed by probability, thus allowing for a decrease in disorder even in a closed system. Although this is possible, such an event has a small probability of occurring, making it unlikely. Even if such event were to occur, it would result in a transient decrease that would affect only a limited number of particles in the system.[19]

Definitions and descriptions

Thermodynamic entropy is more generally defined from a statistical thermodynamics viewpoint, in which the molecular nature of matter is explicitly considered. Alternatively entropy can be defined from a classical thermodynamics viewpoint, in which the molecular interactions are not considered and instead the system is viewed from perspective of the gross motion of very large masses of molecules and the molecular behavior of individual molecules is averaged and obscured. Historically, the classical thermodynamics definition developed first, and it has more recently been extended in the area of non-equilibrium thermodynamics.


Statistical thermodynamics

Statistical mechanics views entropy as the amount of uncertainty (or "mixedupness" in the phrase of Gibbs) which remains about a system, after its observable macroscopic properties (such as temperature, pressure and volume) have been taken into account. For a given set of macroscopic variables, the entropy measures the degree to which the probability of the system is spread out over different possible microstates. In contrast to the macrostate, which characterizes plainly observable average quantities, a microstate specifies all molecular details about the system including the position and velocity of every molecule. The more such states available to the system with appreciable probability, the greater the entropy.

More specifically, entropy is a logarithmic measure of the density of states:

S = - k_{B}\sum_i P_i \ln P_i \!

where kB = 1.38065×10−23 J K−1 is the Boltzmann constant, the summation is over all the microstates the system can be in, and the Pi are the probabilities for the system to be in the ith microstate. For almost all practical purposes, this can be taken as the fundamental definition of entropy since all other formulas for S can be mathematically derived from it, but not vice versa. (In some rare and recondite situations, a generalization of this formula may be needed to account for quantum coherence effects, but in any situation where a classical notion of probability makes sense, the above is the entropy.)

In what has been called "the most famous equation of statistical thermodynamics", the entropy of a system in which all states, of number Ω, are equally likely, is given by

S = k_{B} \ln \Omega,\!

In thermodynamics, such a system is one in which the volume, number of molecules, and internal energy are fixed (the microcanonical ensemble). The entropy is expressed in units of J·K−1.

In essence, the most general interpretation of entropy is as a measure of our uncertainty about a system. The equilibrium state of a system maximizes the entropy because we have lost all information about the initial conditions except for the conserved variables; maximizing the entropy maximizes our ignorance about the details of the system.[20] This uncertainty is not of the everyday subjective kind, but rather the uncertainty inherent to the experimental method and interpretative model.

The interpretative model has a central role in determining entropy. The qualifier "for a given set of macroscopic variables" above has very deep implications: if two observers use different sets of macroscopic variables, then they will observe different entropies. For example, if observer A uses the variables U, V and W, and observer B uses U, V, W, X, then, by changing X, observer B can cause an effect that looks like a violation of the second law of thermodynamics to observer A. In other words: the set of macroscopic variables one chooses must include everything that may change in the experiment, otherwise one might see decreasing entropy![21]

In general, entropy can be defined for any Markov processes with reversible dynamics and the detailed balance property.

In Boltzmann's 1896 Lectures on Gas Theory, he showed that this expression gives a measure of entropy for systems of atoms and molecules in the gas phase, thus providing a measure for the entropy of classical thermodynamics.

Classical thermodynamics

Conjugate variables
of thermodynamics
Pressure Volume
(Stress) (Strain)
Temperature Entropy
Chem. potential Particle no.

From a macroscopic perspective, in classical thermodynamics the entropy is interpreted as a state function of a thermodynamic system: that is, a property depending only on the current state of the system, independent of how that state came to be achieved. The state function has the important property that, when multiplied by a reference temperature, it can be understood as a measure of the amount of energy in a physical system that cannot be used to do thermodynamic work; i.e., work mediated by thermal energy[citation needed]. More precisely, in any process where the system gives up energy ΔE, and its entropy falls by ΔS, a quantity at least TR ΔS of that energy must be given up to the system's surroundings as unusable heat (TR is the temperature of the system's external surroundings). Otherwise the process will not go forward. In classical thermodynamics, the entropy of a system is defined only if it is in thermodynamic equilibrium.

In 1862, Clausius stated the second law of thermodynamics (which he called the “theorem respecting the equivalence-values of the transformations”), as follows:

The algebraic sum of all the transformations occurring in a cyclical process can only be positive, or, as an extreme case, equal to nothing.

Quantitatively, Clausius states the mathematical expression for this theorem is as follows. Let δq be an element of the heat given up by the body to any reservoir of heat during its own changes, heat which it may absorb from a reservoir being here reckoned as negative, and T the absolute temperature of the body at the moment of giving up this heat, then the equation:

\oint \frac{\delta q}{T} \ge 0

must hold good for every cyclical process which is in any way possible, and the condition of equality for this equation will hold true for any reversible cyclical process.

This is the essential formulation of the second law and one of the original forms of the concept of entropy. It can be seen that the dimensions of entropy are energy divided by temperature, which is the same as the dimensions of Boltzmann's constant (kB) and heat capacity. The SI unit of entropy is "joule per kelvin" (J K−1). In this manner, the quantity ΔS is utilized as a type of internal energy, which accounts for the effects of irreversibility, in the energy balance equation for any given system. In the Gibbs free energy equation, ΔG = ΔHTΔS, for example, which is a formula commonly utilized to determine if chemical reactions will occur spontaneously, the free energy related to entropy changes, TΔS, is subtracted from the "total" system enthalpy ΔH to give the "free" energy ΔG of the system.

In a thermodynamic system, pressure, density, and temperature tend to become uniform over time because this equilibrium state has higher probability (more possible combinations of microstates) than any other; see statistical mechanics. In the ice melting example, the difference in temperature between a warm room (the surroundings) and cold glass of ice and water (the system and not part of the room), begins to be equalized as portions of the heat energy from the warm surroundings spread out to the cooler system of ice and water.

A thermodynamic system

Over time the temperature of the glass and its contents and the temperature of the room become equal. The entropy of the room has decreased as some of its energy has been dispersed to the ice and water. However, as calculated in the example, the entropy of the system of ice and water has increased more than the entropy of the surrounding room has decreased. In an isolated system such as the room and ice water taken together, the dispersal of energy from warmer to cooler always results in a net increase in entropy. Thus, when the "universe" of the room and ice water system has reached a temperature equilibrium, the entropy change from the initial state is at a maximum. The entropy of the thermodynamic system is a measure of how far the equalization has progressed.

A special case of entropy increase, the entropy of mixing, occurs when two or more different substances are mixed. If the substances are at the same temperature and pressure, there will be no net exchange of heat or work - the entropy change will be entirely due to the mixing of the different substances. At a statistical mechanical level, this results due to the change in available volume per particle with mixing. [22]

Entropy versus heat and temperature

Loosely speaking, when a system's energy is divided into its "useful" energy (energy that can be used, for example, to push a piston), and its "useless energy" (that energy which cannot be used to do external work), then entropy can be used to estimate the "useless", "stray", or "lost" energy, which depends on the entropy of the system and the absolute temperature of the surroundings. As the "useful" and "useless" energy both depend on the surroundings, neither one is a function of the state of the system, and both can be quite tricky to quantify. This stands in contrast to the system's Gibbs free energy, Helmholtz free energy, entropy, and temperature, all of which are well-defined functions of state. The Gibbs and Helmholtz free energies depend on the temperature of the system (not the surroundings), and do not purport to measure the "useful" energy.

When heat is added to a system at high temperature, the increase in entropy is small. When heat is added to a system at low temperature, the increase in entropy is great. This can be quantified as follows: in thermal systems, changes in the entropy can be ascertained by observing the temperature while observing changes in energy. This is restricted to situations where thermal conduction is the only form of energy transfer (in contrast to frictional heating and other dissipative processes). It is further restricted to systems at or near thermal equilibrium. In systems held at constant temperature, the change in entropy, ΔS, is given by the equation

\Delta S = \frac{Q}{T},

where Q is the amount of heat absorbed by the system in an isothermal and reversible process in which the system goes from one state to another, and T is the absolute temperature at which the process is occurring.[23]

If the temperature of the system is not constant, then the relationship becomes a differential equation:

dS = \frac{\delta q}{T}.

Then the total change in entropy for a transformation is:

 \Delta S = \int \frac{ \delta q }{T}.

This thermodynamic approach to calculating the entropy is subject to several narrow restrictions which must be respected. In contrast, the fundamental statistical definition of entropy applies to any system, including systems far from equilibrium, and including experiments where "heat" and "temperature" are undefinable. In situations where the thermodynamic approach is valid, it can be shown to be consistent with the fundamental statistical definition.

In any case, the statistical definition of entropy remains the fundamental definition, from which all other definitions and all properties of entropy can be derived.


Rudolf Clausius, originator of the concept of entropy

The first law of thermodynamics, formalized through the heat-friction experiments of James Joule in 1843, deals with the concept of energy, which is conserved in all processes; the first law, however, lacks in its ability to quantify the effects of friction and dissipation.

Entropy began with the work of French mathematician Lazare Carnot who in his 1803 paper Fundamental Principles of Equilibrium and Movement proposed that in any machine the accelerations and shocks of the moving parts all represent losses of moment of activity. In other words, in any natural process there exists an inherent tendency towards the dissipation of useful energy. Building on this work, in 1824 Lazare's son Sadi Carnot published Reflections on the Motive Power of Fire in which he set forth the view that in all heat-engines whenever "caloric", or what is now known as heat, falls through a temperature difference, that work or motive power can be produced from the actions of the "fall of caloric" between a hot and cold body. This was an early insight into the second law of thermodynamics.[24]

Carnot based his views of heat partially on the early 18th century "Newtonian hypothesis" that both heat and light were types of indestructible forms of matter, which are attracted and repelled by other matter, and partially on the contemporary views of Count Rumford who showed in 1789 that heat could be created by friction as when cannon bores are machined.[25] Accordingly, Carnot reasoned that if the body of the working substance, such as a body of steam, is brought back to its original state (temperature and pressure) at the end of a complete engine cycle, that "no change occurs in the condition of the working body". This latter comment was amended in his foot notes, and it was this comment that led to the development of entropy.[citation needed]

In the 1850s and 1860s, German physicist Rudolf Clausius gravely objected to this latter supposition, i.e. that no change occurs in the working body, and gave this "change" a mathematical interpretation by questioning the nature of the inherent loss of usable heat when work is done, e.g. heat produced by friction.[26] Clausius described entropy as the transformation-content, i.e. dissipative energy use, of a thermodynamic system or working body of chemical species during a change of state.[26] This was in contrast to earlier views, based on the theories of Isaac Newton, that heat was an indestructible particle that had mass.

Later, scientists such as Ludwig Boltzmann, Josiah Willard Gibbs, and James Clerk Maxwell gave entropy a statistical basis. In 1877, Boltzmann visualized a probabilistic way to measure the entropy of an ensemble of ideal gas particles, in which he defined entropy to be proportional to the logarithm of the number of microstates such a gas could occupy. Henceforth, the essential problem in statistical thermodynamics, i.e. according to Erwin Schrödinger, has been to determine the distribution of a given amount of energy E over N identical systems. Carathéodory linked entropy with a mathematical definition of irreversibility, in terms of trajectories and integrability.

Consequences and applications

The arrow of time

Entropy is the only quantity in the physical sciences that seems to imply a particular direction for time, sometimes called an arrow of time. As we go "forward" in time, the second law of thermodynamics states that the entropy of an isolated system tends to increase or remain the same; it will not decrease. Hence, from one perspective, entropy measurement is thought of as a kind of clock.

The fundamental thermodynamic relation

The entropy of a system depends on its internal energy and the external variables, such as the volume. In the thermodynamic limit this fact leads to an equation relating the change in the internal energy to changes in the entropy and the external variables. This relation is known as the fundamental thermodynamic relation. If the volume is the only external variable, this relation is:

dE = TdSPdV

Since the internal energy is fixed when one specifies the entropy and the volume, this relation is valid even if the change from one state of thermal equilibrium to another with infinitesimally larger entropy and volume happens in a non-quasistatic way (so during this change the system may be very far out of thermal equilibrium and then the entropy, pressure and temperature may not exist).

The fundamental thermodynamic relation implies many thermodynamic identities that are valid in general, independent of the microscopic details of the system. Important examples are the Maxwell relations and the relations between heat capacities.

Entropy in chemical thermodynamics

Thermodynamic entropy is central in chemical thermodynamics, enabling changes to be quantified and the outcome of reactions predicted. The second law of thermodynamics states that entropy in the combination of a system and its surroundings (or in an isolated system by itself) increases during all spontaneous chemical and physical processes. The Clausius equation of δqrev/T = ΔS introduces the measurement of entropy change, ΔS. Entropy change describes the direction and quantifies the magnitude of simple changes such as heat transfer between systems – always from hotter to cooler spontaneously.[27] Thus, when a mole of substance at 0 K is warmed by its surroundings to 298 K, the sum of the incremental values of qrev/T constitute each element's or compound's standard molar entropy, a fundamental physical property and an indicator of the amount of energy stored by a substance at 298 K.[28][29] Entropy change also measures the mixing of substances as a summation of their relative quantities in the final mixture.[30]

Entropy is equally essential in predicting the extent of complex chemical reactions, i.e. whether a process will go as written or proceed in the opposite direction. For such applications, ΔS must be incorporated in an expression that includes both the system and its surroundings, ΔSuniverse = ΔSsurroundings + ΔS system. This expression becomes, via some steps, the Gibbs free energy equation for reactants and products in the system: ΔG [the Gibbs free energy change of the system] = ΔH [the enthalpy change] −T ΔS [the entropy change].[28]

Entropy balance equation for open systems

In chemical engineering, the principles of thermodynamics are commonly applied to "open systems", i.e. those in which heat, work, and mass flow across the system boundary. In a system in which there are flows of both heat (\dot{Q}) and work, i.e. \dot{W}_S (shaft work) and P(dV/dt) (pressure-volume work), across the system boundaries, the heat flow, but not the work flow, causes a change in the entropy of the system. This rate of entropy change is \dot{Q}/T, where T is the absolute thermodynamic temperature of the system at the point of the heat flow. If, in addition, there are mass flows across the system boundaries, the total entropy of the system will also change due to this convected flow.

During steady-state continuous operation, an entropy balance applied to an open system accounts for system entropy changes related to heat flow and mass flow across the system boundary.

To derive a generalized entropy balanced equation, we start with the general balance equation for the change in any extensive quantity Θ in a thermodynamic system, a quantity that may be either conserved, such as energy, or non-conserved, such as entropy. The basic generic balance expression states that dΘ/dt, i.e. the rate of change of Θ in the system, equals the rate at which Θ enters the system at the boundaries, minus the rate at which Θ leaves the system across the system boundaries, plus the rate at which Θ is generated within the system. Using this generic balance equation, with respect to the rate of change with time of the extensive quantity entropy S, the entropy balance equation for an open thermodynamic system is:[31]

\frac{dS}{dt} = \sum_{k=1}^K \dot{M}_k \hat{S}_k + \frac{\dot{Q}}{T} + \dot{S}_{gen}


\sum_{k=1}^K \dot{M}_k \hat{S}_k = the net rate of entropy flow due to the flows of mass into and out of the system (where \hat{S} = entropy per unit mass).
\frac{\dot{Q}}{T} = the rate of entropy flow due to the flow of heat across the system boundary.
\dot{S}_{gen} = the rate of internal generation of entropy within the system.

Note, also, that if there are multiple heat flows, the term \dot{Q}/T is to be replaced by \sum \dot{Q}_j/T_j, where \dot{Q}_j is the heat flow and Tj is the temperature at the jth heat flow port into the system.

Entropy in quantum mechanics (von Neumann entropy)

In quantum statistical mechanics, the concept of entropy was developed by John von Neumann and is generally referred to as "von Neumann entropy", namely S = - k\mathrm{Tr} \rho \ln \rho \!.

where ρ is the density matrix and Tr is the trace operator.

This upholds the correspondence principle, because in the classical limit, i.e. whenever the classical notion of probability applies, this expression is equivalent to the familiar classical definition of entropy,

S = − k PilnPi

Von Neumann established a rigorous mathematical framework for quantum mechanics with his work Mathematische Grundlagen der Quantenmechanik. He provided in this work a theory of measurement, where the usual notion of wave collapse is described as an irreversible process (the so called von Neumann or projective measurement). Using this concept, in conjunction with the density matrix he extended the classical concept of entropy into the quantum domain.

It is well known that a Shannon based definition of information entropy leads in the classical case to the Boltzmann entropy. It is tempting to regard the Von Neumann entropy as the corresponding quantum mechanical definition. But the latter is problematic from quantum information point of view. Consequently Stotland, Pomeransky, Bachmat and Cohen have introduced a new definition of entropy that reflects the inherent uncertainty of quantum mechanical states. This definition allows to distinguish between the minimum uncertainty entropy of pure states, and the excess statistical entropy of mixtures.[32]

Approaches to understanding entropy

Order and disorder

Entropy has often been loosely associated with the amount of order, disorder, and/or chaos in a thermodynamic system. The traditional qualitative description of entropy is that it refers to changes in the status quo of the system and is a measure of "molecular disorder" and the amount of wasted energy in a dynamical energy transformation from one state or form to another.[33] In this direction, a number of authors, in recent years, have derived exact entropy formulas to account for and measure disorder and order in atomic and molecular assemblies.[34][35][36][37] One of the simpler entropy order/disorder formulas is that derived in 1984 by thermodynamic physicist Peter Landsberg, which is based on a combination of thermodynamics and information theory arguments. Landsberg argues that when constraints operate on a system, such that it is prevented from entering one or more of its possible or permitted states, as contrasted with its forbidden states, the measure of the total amount of “disorder” in the system is given by the following expression:[36][37]

\mbox{Disorder}={C_D\over C_I}.\,

Similarly, the total amount of "order" in the system is given by:

\mbox{Order}=1-{C_O\over C_I}.\,

In which CD is the "disorder" capacity of the system, which is the entropy of the parts contained in the permitted ensemble, CI is the "information" capacity of the system, an expression similar to Shannon's channel capacity, and CO is the "order" capacity of the system.[35]

Energy dispersal

The concept of entropy can be described qualitatively as a measure of energy dispersal at a specific temperature.[38] Similar terms have been in use from early in the history of classical thermodynamics, and with the development of statistical thermodynamics and quantum theory, entropy changes have been described in terms of the mixing or "spreading" of the total energy of each constituent of a system over its particular quantized energy levels.

Ambiguities in the terms disorder and chaos, which usually have meanings directly opposed to equilibrium, contribute to widespread confusion and hamper comprehension of entropy for most students.[39] As the second law of thermodynamics shows, in an isolated system internal portions at different temperatures will tend to adjust to a single uniform temperature and thus produce equilibrium. A recently developed educational approach avoids ambiguous terms and describes such spreading out of energy as dispersal, which leads to loss of the differentials required for work even though the total energy remains constant in accordance with the first law of thermodynamics.[40] Physical chemist Peter Atkins, for example, who previously wrote of dispersal leading to a disordered state, now writes that "spontaneous changes are always accompanied by a dispersal of energy", and has discarded 'disorder' as a description.[27][41]

Ice melting example

The illustration for this article is a classic example in which entropy increases in a small "universe", a thermodynamic system consisting of the "surroundings" (the warm room) and "system" (glass, ice, cold water). In this universe, some thermal energy δQ from the warmer room surroundings (at 298 K or 25 °C) will spread out to the cooler system of ice and water at its constant temperature T of 273 K (0 °C), the melting temperature of ice. The entropy of the system will change by the amount dS = δQ/T, in this example δQ/273 K. (The thermal energy δQ for this process is the energy required to change water from the solid state to the liquid state, and is called the enthalpy of fusion, i.e. the ΔH for ice fusion.) The entropy of the surroundings will change by an amount dS = −δQ/298 K. So in this example, the entropy of the system increases, whereas the entropy of the surroundings decreases.

It is important to realize that the decrease in the entropy of the surrounding room is less than the increase in the entropy of the ice and water: the room temperature of 298 K is larger than 273 K and therefore the ratio, (entropy change), of δQ/298 K for the surroundings is smaller than the ratio (entropy change), of δQ/273 K for the ice+water system. To find the entropy change of our "universe", we add up the entropy changes for its constituents: the surrounding room and the ice+water. The total entropy change is positive; this is always true in spontaneous events in a thermodynamic system and it shows the predictive importance of entropy: the final net entropy after such an event is always greater than was the initial entropy.

As the temperature of the cool water rises to that of the room and the room further cools imperceptibly, the sum of the δQ/T over the continuous range, at many increments, in the initially cool to finally warm water can be found by calculus. The entire miniature "universe", i.e. this thermodynamic system, has increased in entropy. Energy has spontaneously become more dispersed and spread out in that "universe" than when the glass of ice water was introduced and became a "system" within it.

Notice that the system will reach a point where the room, the glass and the contents of the glass will be at the same temperature. In this situation, nothing else can happen: although thermal energy does exist in the room (in fact, the amount of thermal energy is the same as in the beginning, since it is a closed system), it is now unable to do useful *work*, as there is no more heat. Unless an external event intervenes (thus breaking the definition of a closed system), the room is destined to remain in the same condition for all eternity. Therefore, following the same reasoning but considering the whole universe as our "room", we reach a similar conclusion: that, at a certain point in the distant future, the whole universe will be a uniform, isothermic and inert body of matter, in which there will be no available energy to do work. This condition is known as the "heat death of the Universe".

Topics in entropy

Entropy and life

For nearly a century and a half, beginning with Clausius' 1863 memoir "On the Concentration of Rays of Heat and Light, and on the Limits of its Action", much writing and research has been devoted to the relationship between thermodynamic entropy and the evolution of life. The argument that life feeds on negative entropy or negentropy as asserted in the 1944 book What is Life? by physicist Erwin Schrödinger served as a further stimulus to this research. Recent writings have used the concept of Gibbs free energy to elaborate on this issue.[42]

In the 1982 textbook Principles of Biochemistry by American biochemist Albert Lehninger, for example, it is argued that the "order" produced within cells as they grow and divide is more than compensated for by the "disorder" they create in their surroundings in the course of growth and division. In short, according to Lehninger, "living organisms preserve their internal order by taking from their surroundings free energy, in the form of nutrients or sunlight, and returning to their surroundings an equal amount of energy as heat and entropy."[43]

Evolution related definitions:

  • Negentropy - a shorthand colloquial phrase for negative entropy.[44]
  • Ectropy - a measure of the tendency of a dynamical system to do useful work and grow more organized.[33]
  • Extropy – a metaphorical term defining the extent of a living or organizational system's intelligence, functional order, vitality, energy, life, experience, and capacity and drive for improvement and growth.
  • Ecological entropy - a measure of biodiversity in the study of biological ecology.

In a study titled “Natural selection for least action” published in the Proceedings of The Royal Society A., Ville Kaila and Arto Annila of the University of Helsinki describe how the second law of thermodynamics can be written as an equation of motion to describe evolution, showing how natural selection and the principle of least action can be connected by expressing natural selection in terms of chemical thermodynamics. In this view, evolution explores possible paths to level differences in energy densities and so increase entropy most rapidly. Thus, an organism serves as an energy transfer mechanism, and beneficial mutations allow successive organisms to transfer more energy within their environment.[45]

Entropy and cosmology

Since a finite universe is an isolated system then, by the Second Law of Thermodynamics, its total entropy is constantly increasing. It has been speculated, since the 19th century, that the universe is fated to a heat death in which all the energy ends up as a homogeneous distribution of thermal energy, so that no more work can be extracted from any source.

If the universe can be considered to have generally increasing entropy, then—as Roger Penrose has pointed out—gravity plays an important role in the increase because gravity causes dispersed matter to accumulate into stars, which collapse eventually into black holes. The entropy of a black hole is proportional to the surface area of the black hole's event horizon.[46] Jacob Bekenstein and Stephen Hawking have shown that black holes have the maximum possible entropy of any object of equal size. This makes them likely end points of all entropy-increasing processes, if they are totally effective matter and energy traps. Hawking has, however, recently changed his stance on this aspect.

The role of entropy in cosmology remains a controversial subject. Recent work has cast some doubt on the heat death hypothesis and the applicability of any simple thermodynamic model to the universe in general. Although entropy does increase in the model of an expanding universe, the maximum possible entropy rises much more rapidly, moving the universe further from the heat death with time, not closer. This results in an "entropy gap" pushing the system further away from the posited heat death equilibrium.[47] Other complicating factors, such as the energy density of the vacuum and macroscopic quantum effects, are difficult to reconcile with thermodynamical models, making any predictions of large-scale thermodynamics extremely difficult.[citation needed]

The entropy gap is widely believed to have been originally opened up by the early rapid exponential expansion of the universe.

Other relations

Although the concept of entropy was originally a thermodynamic construct, it has been adapted in other fields of study, including information theory, psychodynamics, thermoeconomics, and evolution.[35][48][49]

Entropy and information theory

In information theory, entropy is the measure of the amount of information that is missing before reception and is sometimes referred to as Shannon entropy.[50] Shannon entropy is a broad and general concept which finds applications in information theory as well as thermodynamics. It was originally devised by Claude Shannon in 1948 to study the amount of information in a transmitted message. The definition of the information entropy is, however, quite general, and is expressed in terms of a discrete set of probabilities pi:

H(X) = -\sum_{i=1}^n {p(x_i) \log_b p(x_i)},

In the case of transmitted messages, these probabilities were the probabilities that a particular message was actually transmitted, and the entropy of the message system was a measure of how much information was in the message. For the case of equal probabilities (i.e. each message is equally probable), the Shannon entropy (in bits) is just the number of yes/no questions needed to determine the content of the message.

The question of the link between information entropy and thermodynamic entropy is a hotly debated topic. Some authors argue that there is a link between the two,[51][52][53] while others will argue that they have absolutely nothing to do with each other.[54]

The expressions for the two entropies are very similar. The information entropy H for equal probabilities pi = p is:


where K is a constant which determines the units of entropy. For example, if the units are bits, then K=1/ln(2). The thermodynamic entropy S , from a statistical mechanical point of view was first expressed by Boltzmann:


where p  is the probability of a system being in a particular microstate, given that it is in a particular macrostate, and k  is Boltzmann's constant. It can be seen that one may think of the thermodynamic entropy as Boltzmann's constant, divided by ln(2), times the number of yes/no questions that must be asked in order to determine the microstate of the system, given that we know the macrostate. The link between thermodynamic and information entropy was developed in a series of papers by Edwin Jaynes beginning in 1957.[55]

There are many ways of demonstrating the equivalence of "information entropy" and "physics entropy", that is, the equivalence of "Shannon entropy" and "Boltzmann entropy". Nevertheless, some authors argue for dropping the word entropy for the H function of information theory and using Shannon's other term "uncertainty" instead.[56]

Standard textbook definitions

The following is a list of additional definitions of entropy from a collection of textbooks.

  • a measure of disorder in the universe or of the availability of the energy in a system to do work.[57]

Miscellaneous definitions

  • Entropy unit - a non-S.I. unit of thermodynamic entropy, usually denoted "e.u." and equal to one calorie per Kelvin per mole, or 4.184 Joules per Kelvin per mole.[58]
  • Gibbs entropy - the usual statistical mechanical entropy of a thermodynamic system.
  • Boltzmann entropy - a type of Gibbs entropy, which neglects internal statistical correlations in the overall particle distribution.
  • Tsallis entropy - a generalization of the standard Boltzmann-Gibbs entropy.
  • Standard molar entropy - is the entropy content of one mole of substance, under conditions of standard temperature and pressure.
  • Residual entropy - the entropy present after a substance is cooled arbitrarily close to absolute zero.
  • Entropy of mixing - the change in the entropy when two different chemical substances or components are mixed.
  • Loop entropy - is the entropy lost upon bringing together two residues of a polymer within a prescribed distance.
  • Conformational entropy - is the entropy associated with the physical arrangement of a polymer chain that assumes a compact or globular state in solution.
  • Entropic force - a microscopic force or reaction tendency related to system organization changes, molecular frictional considerations, and statistical variations.
  • Free entropy - an entropic thermodynamic potential analogous to the free energy.
  • Entropic explosion – an explosion in which the reactants undergo a large change in volume without releasing a large amount of heat.
  • Entropy change – a change in entropy dS between two equilibrium states is given by the heat transferred dQrev divided by the absolute temperature T of the system in this interval.[59]
  • Sackur-Tetrode entropy - the entropy of a monatomic classical ideal gas determined via quantum considerations.

Other mathematical definitions

Sociological definitions

The concept of entropy has also entered the domain of sociology, generally as a metaphor for chaos, disorder or dissipation of energy, rather than as a direct measure of thermodynamic or information entropy:

  • Corporate entropy - energy waste as red tape and business team inefficiency, i.e. energy lost to waste.[60] (This definition is comparable to von Clausewitz's concept of friction in war.)
  • Economic entropy – a semi-quantitative measure of the irrevocable dissipation and degradation of natural materials and available energy with respect to economic activity.[52][61]
  • Entropology – the study or discussion of entropy or the name sometimes given to thermodynamics without differential equations.[23][62]
  • Psychological entropy - the distribution of energy in the psyche, which tends to seek equilibrium or balance among all the structures of the psyche.[63]
  • Social entropy – a measure of social system structure, having both theoretical and statistical interpretations, i.e. society (macrosocietal variables) measured in terms of how the individual functions in society (microsocietal variables); also related to social equilibrium.[64]


Any method involving the notion of entropy, the very existence of which depends on the second law of thermodynamics, will doubtless seem to many far-fetched, and may repel beginners as obscure and difficult of comprehension.
Willard Gibbs, Graphical Methods in the Thermodynamics of Fluids (1873)
My greatest concern was what to call it. I thought of calling it ‘information’, but the word was overly used, so I decided to call it ‘uncertainty’. When I discussed it with John von Neumann, he had a better idea. Von Neumann told me, ‘You should call it entropy, for two reasons. In the first place your uncertainty function has been used in statistical mechanics under that name, so it already has a name. In the second place, and more important, nobody knows what entropy really is, so in a debate you will always have the advantage.
–Conversation between Claude Shannon and John von Neumann regarding what name to give to the “measure of uncertainty” or attenuation in phone-line signals (1949)

See also


  1. ^ Note: In complex systems of molecules, such as at the critical point of water or when salt is added to an ice-water mixture, entropy can either increase or decrease depending on system parameters, such as temperature and pressure. For example, if the spontaneous crystallization of a supercooled liquid takes place under adiabatic conditions the entropy of the resulting crystal will be greater than that of the supercooled liquid (Denbigh, K. (1982). The Principles of Chemical Equilibrium, 4th Ed.). In general, however, when ice melts, the entropy of the two adjoined systems, i.e. the adjacent hot and cold bodies, when thought of as one "universe", increases. Here are some further tutorials: Ice-meltingJCE example; Ice-melting and Entropy Change – example; Ice-melting and Entropy Change – discussions
  2. ^ Clausius, Rudolf (1862). Communicated to the Naturforschende Gesellschaft of Zurich, January 27, 1862; published in the Vierteljahrschrift of this Society, vol. vii. P. 48; in Poggendorff’s Annalen, May 1862, vol. cxvi. p. 73; in the Philosophical Magazine, S. 4. vol. xxiv. pp. 81, 201; and in the Journal des Mathematiques of Paris, S. 2. vol. vii. P. 209.
  3. ^ It was not easy for a person brought up in the ways of classical thermodynamics to come around to the idea that gain of entropy eventually is nothing more nor less than loss of information. Gilbert Newton Lewis, Letter to Irving Langmuir, 5 Aug 1930. Quoted in Nathan Reingold, Science in America: A Documentary History 1900-1939 (1981), 400. Source: www.todayinsci.com
  4. ^ "Entropy". Online Etymology Dictionary. http://www.etymonline.com/index.php?term=entropy. Retrieved 2008-08-05. 
  5. ^ a b McGraw-Hill Concise Encyclopedia of Chemistry, 2004
  6. ^ a b Sandler S. I., Chemical and Engineering Thermodynamics, 3rd Ed. Wiley, New York, 1999 p91
  7. ^ a b McQuarrie D. A., Simon J. D., Physical Chemistry: A Molecular Approach, University Science Books, Sausalito 1997 pp 817.
  8. ^ Haynie, Donald, T. (2001). Biological Thermodynamics. Cambridge University Press. ISBN 0-521-79165-0. 
  9. ^ a b Cutnell, John, D.; Johnson, Kenneth, J. (1998). Physics, 4th edition. John Wiley and Sons, Inc.. ISBN 0-471-19113-2. 
  10. ^ a b Sethna, J. Statistical Mechanics Oxford University Press 2006 p78
  11. ^ a b Oxford Dictionary of Science, 2005
  12. ^ McGraw-Hill Concise Encyclopedia of Chemistry, 2004
  13. ^ de Rosnay, Joel (1979). The Macroscope – a New World View (written by an M.I.T.-trained biochemist). Harper & Row, Publishers. ISBN 0-06-011029-5. 
  14. ^ Baierlein, Ralph (2003). Thermal Physics. Cambridge University Press. ISBN 0-521-65838-1. 
  15. ^ Schroeder, Daniel, R. (2000). Thermal Physics. New York: Addison Wesley Longman. ISBN 0-201-38027-7. 
  16. ^ Chang, Raymond (1998). Chemistry, 6th Ed.. New York: McGraw Hill. ISBN 0-07-115221-0. 
  17. ^ Barnes & Noble's Essential Dictionary of Science, 2004
  18. ^ Daintith, John (2005). Oxford Dictionary of Physics. Oxford University Press. ISBN 0-19-280628-9. 
  19. ^ "Entropy production theorems and some consequences," Physical Review E; Saha, Arnab; Lahiri, Sourabh; Jayannavar, A. M.; The American Physical Society: 14 July 2009, p.1-10
  20. ^ EntropyOrderParametersComplexity.pdf
  21. ^ Jaynes, E. T., "The Gibbs Paradox," In Maximum Entropy and Bayesian Methods; Smith, C. R.; Erickson, G. J.; Neudorfer, P. O., Eds.; Kluwer Academic: Dordrecht, 1992, p.1-22
  22. ^ Ben-Naim, Arieh, On the So-Called Gibbs Paradox, and on the Real Paradox, Entropy, 9, 132-136, 2007 Link
  23. ^ a b Perrot, Pierre (1998). A to Z of Thermodynamics. Oxford University Press. ISBN 0-19-856552-6. 
  24. ^ "Carnot, Sadi (1796-1832)". Wolfram Research. 2007. http://scienceworld.wolfram.com/biography/CarnotSadi.html. Retrieved 2010-02-24. 
  25. ^ McCulloch, Richard, S. (1876). Treatise on the Mechanical Theory of Heat and its Applications to the Steam-Engine, etc.. D. Van Nostrand. 
  26. ^ a b Clausius, Rudolf (1850). On the Motive Power of Heat, and on the Laws which can be deduced from it for the Theory of Heat. Poggendorff's Annalen der Physick, LXXIX (Dover Reprint). ISBN 0-486-59065-8. 
  27. ^ a b c Atkins, Peter; Julio De Paula (2006). Physical Chemistry, 8th edition. Oxford University Press. ISBN 0-19-870072-5. 
  28. ^ a b Moore, J. W.; C. L. Stanistski, P. C. Jurs (2005). Chemistry, The Molecular Science,. Brooks Cole. ISBN 0-534-42201-2. 
  29. ^ Jungermann, A.H. (2006). “Entropy and the Shelf Model: A Quantum Physical Approach to a Physical Property”. Journal of Chemical Education 83: 1686-1694
  30. ^ Levine, I. N. (2002). Physical Chemistry, 5th edition. McGraw-Hill. ISBN 0-07-231808-2. 
  31. ^ Sandler, Stanley, I. (1989). Chemical and Engineering Thermodynamics. John Wiley & Sons. ISBN 0-471-83050-X. 
  32. ^ The information entropy of quantum mechanical states, Europhysics Letters 67, 700 (2004)
  33. ^ a b Haddad, Wassim M.; Chellaboina, VijaySekhar; Nersesov, Sergey G. (2005). Thermodynamics - A Dynamical Systems Approach. Princeton University Press. ISBN 0-691-12327-6. 
  34. ^ Callen, Herbert, B (2001). Thermodynamics and an Introduction to Thermostatistics, 2nd Ed.. John Wiley and Sons. ISBN 0-471-86256-8. 
  35. ^ a b c Brooks, Daniel, R.; Wiley, E.O. (1988). Evolution as Entropy– Towards a Unified Theory of Biology. University of Chicago Press. ISBN 0-226-07574-5. 
  36. ^ a b Landsberg, P.T. (1984). “Is Equilibrium always an Entropy Maximum?” J. Stat. Physics 35: 159-69.
  37. ^ a b Landsberg, P.T. (1984). “Can Entropy and “Order” Increase Together?” Physics Letters 102A:171-173
  38. ^ Frank L. Lambert, A Student’s Approach to the Second Law and Entropy
  39. ^ Carson, E. M. and J. R. Watson (Department of Educational and Professional Studies, Kings College, London), Undergraduate students' understandings of entropy and Gibbs Free energy, University Chemistry Education - 2002 Papers, Royal Society of Chemistry.
  40. ^ Frank L. Lambert, JCE 2002 (79) 187 [Feb] Disorder—A Cracked Crutch for Supporting Entropy Discussions
  41. ^ Atkins, Peter (1984). The Second Law. Scientific American Library. ISBN 0-7167-5004-X. 
  42. ^ Higgs, P. G., & Pudritz, R. E. (2009). “A thermodynamic basis for prebiotic amino acid synthesis and the nature of the first genetic code" Accepted for publication in Astrobiology
  43. ^ Lehninger, Albert (1993). Principles of Biochemistry, 2nd Ed.. Worth Publishers. ISBN 0-87901-711-2. 
  44. ^ Schrödinger, Erwin (1944). What is Life - the Physical Aspect of the Living Cell. Cambridge University Press. ISBN 0-521-42708-8. 
  45. ^ Lisa Zyga (2008-08-11). "Evolution as Described by the Second Law of Thermodynamics". Physorg.com. http://www.physorg.com/news137679868.html. Retrieved 2008-08-14. 
  46. ^ von Baeyer, Christian, H. (2003). Information - the New Language of Science. Harvard University Press. ISBN 0-674-01387-5. Srednicki M (August 1993). "Entropy and area". Phys. Rev. Lett. 71 (5): 666–669. doi:10.1103/PhysRevLett.71.666. PMID 10055336.  Callaway DJE (April 1996). "Surface tension, hydrophobicity, and black holes: The entropic connection". Phys Rev E Stat Phys Plasmas Fluids Relat Interdiscip Topics 53 (4): 3738–3744. doi:10.1103/PhysRevE.53.3738. PMID 9964684. 
  47. ^ Stenger, Victor J. (2007). God: The Failed Hypothesis. Prometheus Books. ISBN 159-102-481-1. 
  48. ^ Avery, John (2003). Information Theory and Evolution. World Scientific. ISBN 981-238-399-9. 
  49. ^ Yockey, Hubert, P. (2005). Information Theory, Evolution, and the Origin of Life.. Cambridge University Press. ISBN 0-521-80293-8. 
  50. ^ Balian, Roger (2003). Entropy – Protean Concept (PDF). Poincaré Seminar 2: 119-45.
  51. ^ Brillouin, Leon (1956). Science and Information Theory. name. ISBN 0-486-43918-6. 
  52. ^ a b Georgescu-Roegen, Nicholas (1971). The Entropy Law and the Economic Process. Harvard University Press. ISBN 0-674-25781-2. 
  53. ^ Chen, Jing (2005). The Physical Foundation of Economics - an Analytical Thermodynamic Theory. World Scientific. ISBN 981-256-323-7. 
  54. ^ Lin, Shu-Kun. (1999). “Diversity and Entropy.” Entropy (Journal), 1[1], 1-3.
  55. ^ "Edwin T. Jaynes - Bibliography". Bayes.wustl.edu. 1998-03-02. http://bayes.wustl.edu/etj/node1.html. Retrieved 2009-12-06. 
  56. ^ Schneider, Tom, DELILA system (Deoxyribonucleic acid Library Language), (Information Theory Analysis of binding sites), Laboratory of Mathematical Biology, National Cancer Institute, FCRDC Bldg. 469. Rm 144, P.O. Box. B Frederick, MD 21702-1201, USA.
  57. ^ Gribbin's Encyclopedia of Particle Physics, 2000
  58. ^ [www.iupac.org/goldbook/E02151.pdf "Entropy unit"]. www.iupac.org/goldbook/E02151.pdf. 
  59. ^ Serway, Raymond, A. (1992). Physics for Scientists and Engineers. Saunders Golden Subburst Series. ISBN 0-03-096026-6. 
  60. ^ DeMarco, Tom; Lister, Timothy (1999). Peopleware: Productive Projects and Teams, 2nd. Ed.. Dorset House Publishing Co.. ISBN 0-932633-43-9. 
  61. ^ Burley, Peter; Foster, John (1994). Economics and Thermodynamics – New Perspectives on Economic Analysis. Kluwer Academic Publishers. ISBN 0-7923-9446-1. 
  62. ^ Example: "Entropology, not anthropology, should be the word for the discipline that devotes itself to the study of the process of disintegration in its most evolved forms." (In A World on Wane, London, 1961, pg. 397; translated by John Russell of Tristes Tropiques by Claude Lévi-Strauss.)
  63. ^ Hall, Calvin S.; Nordby, Vernon J. (1999). A Primer of Jungian Psychology. New York: Meridian. ISBN 0-452-01186-8. 
  64. ^ Bailey, Kenneth, D. (1990). Social Entropy Theory. State University of New York Press. ISBN 0-7914.... 


  • P. Pluch Quantum Probability Theory, PhD Thesis, University of Klagenfurt (2006)

Further reading

  • Ben-Naim, Arieh (2007). Entropy Demystified. World Scientific. ISBN 981-270-055-2. 
  • Carroll, Sean (2010). From Eternity to Here: The Quest for the Ultimate Theory of Time. Dutton. ISBN 0525951334. 
  • Dugdale, J. S. (1996). Entropy and its Physical Meaning (2nd ed.). Taylor and Francis (UK); CRC (US). ISBN 0748405690. 
  • Fermi, Enrico (1937). Thermodynamics. Prentice Hall. ISBN 0-486-60361-X. 
  • Gyftopoulos, E.P.; G.P. Beretta (2005). Thermodynamics. Foundations and Applications. Dover. ISBN 0-486-43932-1. 
  • Kroemer, Herbert; Charles Kittel (1980). Thermal Physics (2nd ed.). W. H. Freeman Company. ISBN 0-7167-1088-9. 
  • Penrose, Roger (2005). The Road to Reality: A Complete Guide to the Laws of the Universe. New York: A.A. Knopf. ISBN 0-679-45443-8. 
  • Reif, F. (1965). Fundamentals of statistical and thermal physics. McGraw-Hill. ISBN 0-07-051800-9. 
  • Goldstein, Martin; Inge, F (1993). The Refrigerator and the Universe. Harvard University Press. ISBN 0-674-75325-9. 
  • vonBaeyer; Hans Christian (1998). Maxwell's Demon: Why Warmth Disperses and Time Passes. Random House. ISBN 0-679-43342-2. 
  • Entropy for beginners

External links

Simple English

Entropy is the name of some different things.

The word Entropy came from the study of heat and energy in the period 1850 to 1900. Some very useful mathematical ideas about probability calculations emerged from the study of entropy. These ideas are now used in Information theory, Chemistry and other areas of study.

  • Information entropy, which is a measure of information communicated by systems that are affected by noise.
  • Thermodynamic entropy, which is part of the science of heat energy and is a measure of how organized or disorganized energy is in a system of atoms or molecules.


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