Physics:Entropy
Entropy  

Common symbols  S 
SI unit  joules per kelvin (J⋅K^{−1}) 
In SI base units  kg⋅m^{2}⋅s^{−2}⋅K^{−1} 
Thermodynamics 

Entropy is a scientific concept as well as a measurable physical property that is most commonly associated with a state of disorder, randomness, or uncertainty. The term and the concept are used in diverse fields, from classical thermodynamics, where it was first recognized, to the microscopic description of nature in statistical physics, and to the principles of information theory. It has found farranging applications in chemistry and physics, in biological systems and their relation to life, in cosmology, economics, sociology, weather science, climate change, and information systems including the transmission of information in telecommunication.^{[1]}
The thermodynamic concept was referred to by Scottish scientist and engineer Macquorn Rankine in 1850 with the names thermodynamic function and heatpotential.^{[2]} In 1865, German physicist Rudolf Clausius, one of the leading founders of the field of thermodynamics, defined it as the quotient of an infinitesimal amount of heat to the instantaneous temperature. He initially described it as transformationcontent, in German Verwandlungsinhalt, and later coined the term entropy from a Greek word for transformation. Referring to microscopic constitution and structure, in 1862, Clausius interpreted the concept as meaning disgregation.^{[3]}
A consequence of entropy is that certain processes are irreversible or impossible, aside from the requirement of not violating the conservation of energy, the latter being expressed in the first law of thermodynamics. Entropy is central to the second law of thermodynamics, which states that the entropy of isolated systems left to spontaneous evolution cannot decrease with time, as they always arrive at a state of thermodynamic equilibrium, where the entropy is highest.
Austrian physicist Ludwig Boltzmann explained entropy as the measure of the number of possible microscopic arrangements or states of individual atoms and molecules of a system that comply with the macroscopic condition of the system. He thereby introduced the concept of statistical disorder and probability distributions into a new field of thermodynamics, called statistical mechanics, and found the link between the microscopic interactions, which fluctuate about an average configuration, to the macroscopically observable behavior, in form of a simple logarithmic law, with a proportionality constant, the Boltzmann constant, that has become one of the defining universal constants for the modern International System of Units (SI).
In 1948, Bell Labs scientist Claude Shannon developed similar statistical concepts of measuring microscopic uncertainty and multiplicity to the problem of random losses of information in telecommunication signals. Upon John von Neumann's suggestion, Shannon named this entity of missing information in analogous manner to its use in statistical mechanics as entropy, and gave birth to the field of information theory. This description has been identified as a universal definition of the concept of entropy.^{[4]}
History
In his 1803 paper, Fundamental Principles of Equilibrium and Movement, the French mathematician Lazare Carnot proposed that in any machine, the accelerations and shocks of the moving parts represent losses of moment of activity; in any natural process there exists an inherent tendency towards the dissipation of useful energy. In 1824, building on that work, Lazare's son, Sadi Carnot, published Reflections on the Motive Power of Fire, which posited that in all heatengines, whenever "caloric" (what is now known as heat) falls through a temperature difference, work or motive power can be produced from the actions of its fall from a hot to cold body. He used an analogy with how water falls in a water wheel. That was an early insight into the second law of thermodynamics.^{[5]} Carnot based his views of heat partially on the early 18thcentury "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.^{[6]} Carnot reasoned that if the body of the working substance, such as a body of steam, is returned to its original state at the end of a complete engine cycle, "no change occurs in the condition of the working body".
The first law of thermodynamics, deduced from the heatfriction experiments of James Joule in 1843, expresses the concept of energy, and its conservation in all processes; the first law, however, is unsuitable to separately quantify the effects of friction and dissipation.
In the 1850s and 1860s, German physicist Rudolf Clausius objected to the supposition that no change occurs in the working body, and gave that 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.^{[7]} He described his observations as a dissipative use of energy, resulting in a transformationcontent (Verwandlungsinhalt in German), of a thermodynamic system or working body of chemical species during a change of state.^{[7]} That was in contrast to earlier views, based on the theories of Isaac Newton, that heat was an indestructible particle that had mass. Clausius discovered that the nonusable energy increases as steam proceeds from inlet to exhaust in a steam engine. From the prefix en, as in 'energy', and from the Greek word τροπή [tropē], which is translated in an established lexicon as turning or change^{[8]} and that he rendered in German as Verwandlung, a word often translated into English as transformation, in 1865 Clausius coined the name of that property as entropy.^{[9]} The word was adopted into the English language in 1868.
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 as proportional to the natural logarithm of the number of microstates such a gas could occupy. The proportionality constant in this definition, called the Boltzmann constant, has become one of the defining universal constants for the modern International System of Units (SI). Henceforth, the essential problem in statistical thermodynamics has been to determine the distribution of a given amount of energy E over N identical systems. Constantin Carathéodory, a Greek mathematician, linked entropy with a mathematical definition of irreversibility, in terms of trajectories and integrability.
Etymology
In 1865, Clausius named the concept of "the differential of a quantity which depends on the configuration of the system," entropy (Entropie) after the Greek word for 'transformation'.^{[10]} He gave "transformational content" (Verwandlungsinhalt) as a synonym, paralleling his "thermal and ergonal content" (Wärme und Werkinhalt) as the name of [math]\displaystyle{ U }[/math], but preferring the term entropy as a close parallel of the word energy, as he found the concepts nearly "analogous in their physical significance."^{[10]} This term was formed by replacing the root of ἔργον ('ergon', 'work') by that of τροπή ('tropy', 'transformation').^{[9]}
In more detail, Clausius explained his choice of "entropy" as a name as follows:^{[11]}
I prefer going to the ancient languages for the names of important scientific quantities, so that they may mean the same thing in all living tongues. I propose, therefore, to call S the entropy of a body, after the Greek word "transformation". I have designedly coined the word entropy to be similar to energy, for these two quantities are so analogous in their physical significance, that an analogy of denominations seems to me helpful.
Leon Cooper added that in this way "he succeeded in coining a word that meant the same thing to everybody: nothing."^{[11]}
Definitions and descriptions
Willard Gibbs, Graphical Methods in the Thermodynamics of Fluids^{[12]}
The concept of entropy is described by two principal approaches, the macroscopic perspective of classical thermodynamics, and the microscopic description central to statistical mechanics. The classical approach defines entropy in terms of macroscopically measurable physical properties, such as bulk mass, volume, pressure, and temperature. The statistical definition of entropy defines it in terms of the statistics of the motions of the microscopic constituents of a system – modeled at first classically, e.g. Newtonian particles constituting a gas, and later quantummechanically (photons, phonons, spins, etc.). The two approaches form a consistent, unified view of the same phenomenon as expressed in the second law of thermodynamics, which has found universal applicability to physical processes.
State variables and functions of state
Many thermodynamic properties are defined by physical variables that define a state of thermodynamic equilibrium; these are state variables. State variables depend only on the equilibrium condition, not on the path evolution to that state. State variables can be functions of state, also called state functions, in a sense that one state variable is a mathematical function of other state variables. Often, if some properties of a system are determined, they are sufficient to determine the state of the system and thus other properties' values. For example, temperature and pressure of a given quantity of gas determine its state, and thus also its volume via the ideal gas law. A system composed of a pure substance of a single phase at a particular uniform temperature and pressure is determined, and is thus a particular state, and has not only a particular volume but also a specific entropy.^{[13]} The fact that entropy is a function of state makes it useful. In the Carnot cycle, the working fluid returns to the same state that it had at the start of the cycle, hence the change or line integral of any state function, such as entropy, over this reversible cycle is zero.
Reversible process
Total entropy may be conserved during a reversible process. The entropy change [math]\displaystyle{ d S }[/math] of the system (not including the surroundings) is welldefined as heat [math]\displaystyle{ \delta Q_\text{rev} }[/math] transferred to the system divided by the system temperature [math]\displaystyle{ T }[/math], [math]\displaystyle{ d S = \frac{\delta Q_\text{rev}}{T} }[/math]. A reversible process is a quasistatic one that deviates only infinitesimally from thermodynamic equilibrium and avoids friction or other dissipation. Any process that happens quickly enough to deviate from thermal equilibrium cannot be reversible, total entropy increases, and the potential for maximum work to be done in the process is also lost.^{[14]} For example, in the Carnot cycle, while the heat flow from the hot reservoir to the cold reservoir represents an increase in entropy, the work output, if reversibly and perfectly stored in some energy storage mechanism, represents a decrease in entropy that could be used to operate the heat engine in reverse and return to the previous state; thus the total entropy change may still be zero at all times if the entire process is reversible. An irreversible process increases the total entropy of system and surroundings.^{[15]}
Carnot cycle
The concept of entropy arose from Rudolf Clausius's study of the Carnot cycle.^{[16]} In a Carnot cycle, heat Q_{H} is absorbed isothermally at temperature T_{H} from a 'hot' reservoir and given up isothermally as heat Q_{C} to a 'cold' reservoir at T_{C}. According to Carnot's principle, work can only be produced by the system when there is a temperature difference, and the work should be some function of the difference in temperature and the heat absorbed (Q_{H}). Carnot did not distinguish between Q_{H} and Q_{C}, since he was using the incorrect hypothesis that caloric theory was valid, and hence heat was conserved (the incorrect assumption that Q_{H} and Q_{C} were equal in magnitude) when, in fact, Q_{H} is greater than the magnitude of Q_{C}.^{[17]}^{[18]} Through the efforts of Clausius and Kelvin, it is now known that the maximum work that a heat engine can produce is the product of the Carnot efficiency and the heat absorbed from the hot reservoir:
[math]\displaystyle{ W = \left(\frac{T_\text{H}T_\text{C}}{T_\text{H}}\right)Q_\text{H} = \left(1\frac{T_\text{C}}{T_\text{H}} \right) Q_\text{H} }[/math] 

( ) 
To derive the Carnot efficiency, which is 1 − T_{C}/T_{H} (a number less than one), Kelvin had to evaluate the ratio of the work output to the heat absorbed during the isothermal expansion with the help of the Carnot–Clapeyron equation, which contained an unknown function called the Carnot function. The possibility that the Carnot function could be the temperature as measured from a zero point of temperature was suggested by Joule in a letter to Kelvin. This allowed Kelvin to establish his absolute temperature scale.^{[19]} It is also known that the net work W produced by the system in one cycle is the net heat absorbed, which is the sum (or difference of the magnitudes) of the heat Q_{H} > 0 absorbed from the hot reservoir and the waste heat Q_{C} < 0 given off to the cold reservoir:^{[20]}
[math]\displaystyle{ W=Q_\text{H}+Q_\text{C} }[/math] 

( ) 
Since the latter is valid over the entire cycle, this gave Clausius the hint that at each stage of the cycle, work and heat would not be equal, but rather their difference would be the change of a state function that would vanish upon completion of the cycle. The state function was called the internal energy central to the first law of thermodynamics.^{[21]}
Now equating (1) and (2) gives^{[22]}^{[20]}
[math]\displaystyle{ \frac{Q_\text{H}}{T_\text{H}} +\frac{Q_\text{C}}{T_\text{C}} = 0 }[/math]
This implies that there is a function of state whose change is Q/T and that is conserved over a complete cycle of the Carnot cycle. Clausius called this state function entropy. One can see that entropy was discovered through mathematics rather than through laboratory results. It is a mathematical construct and has no easy physical analogy. This makes the concept somewhat obscure or abstract, akin to how the concept of energy arose.
Clausius then asked what would happen if there should be less work produced by the system than that predicted by Carnot's principle. The righthand side of the first equation would be the upper bound of the work output by the system, which would now be converted into an inequality
[math]\displaystyle{ W \lt \left(1  \frac{T_\text{C}}{T_\text{H}}\right) Q_\text{H} }[/math]
When the second equation is used to express the work as a net or total heat exchanged in a cycle, we get
[math]\displaystyle{ Q_\text{H}+Q_\text{C}\lt \left(1\frac{T_\text{C}}{T_\text{H}}\right)Q_\text{H} }[/math] or [math]\displaystyle{ Q_\text{C}\gt \frac{T_\text{C}}{T_\text{H}}Q_\text{H} }[/math]
So more heat is given up to the cold reservoir than in the Carnot cycle. If we denote the entropy changes by ΔS_{i} = Q_{i}/T_{i} for the two stages of the process, then the above inequality can be written as a decrease in the entropy
[math]\displaystyle{ \Delta S_\text{H}+ \Delta S_\text{C}\lt 0 }[/math] or [math]\displaystyle{ \Delta S_\text{H} \lt  \Delta S_\text{C} = \Delta S_\text{C} }[/math]
The magnitude of the entropy that leaves the system is greater than the entropy that enters the system, implying that some irreversible process prevents the cycle from producing the maximum amount of work predicted by the Carnot equation.
The Carnot cycle and efficiency are useful because they define the upper bound of the possible work output and the efficiency of any classical thermodynamic heat engine. Other cycles, such as the Otto cycle, Diesel cycle and Brayton cycle, can be analyzed from the standpoint of the Carnot cycle. Any machine or cyclic process that converts heat to work and is claimed to produce an efficiency greater than the Carnot efficiency is not viable because it violates the second law of thermodynamics. For very small numbers of particles in the system, statistical thermodynamics must be used. The efficiency of devices such as photovoltaic cells requires an analysis from the standpoint of quantum mechanics.
Classical thermodynamics
The thermodynamic definition of entropy was developed in the early 1850s by Rudolf Clausius and essentially describes how to measure the entropy of an isolated system in thermodynamic equilibrium with its parts. Clausius created the term entropy as an extensive thermodynamic variable that was shown to be useful in characterizing the Carnot cycle. Heat transfer along the isotherm steps of the Carnot cycle was found to be proportional to the temperature of a system (known as its absolute temperature). This relationship was expressed in increments of entropy equal to the ratio of incremental heat transfer divided by temperature, which was found to vary in the thermodynamic cycle but eventually return to the same value at the end of every cycle. Thus it was found to be a function of state, specifically a thermodynamic state of the system.
While Clausius based his definition on a reversible process, there are also irreversible processes that change entropy. Following the second law of thermodynamics, entropy of an isolated system always increases for irreversible processes. The difference between an isolated system and closed system is that energy may not flow to and from an isolated system, but energy flow to and from a closed system is possible. Nevertheless, for both closed and isolated systems, and indeed, also in open systems, irreversible thermodynamics processes may occur.
According to the Clausius equality, for a reversible cyclic process: [math]\displaystyle{ \oint \frac{\delta Q_\text{rev}}{T} = 0 }[/math]. This means the line integral [math]\displaystyle{ \int_L \frac{\delta Q_\text{rev}}{T} }[/math] is pathindependent.
So we can define a state function S called entropy, which satisfies [math]\displaystyle{ d S = \frac{\delta Q_\text{rev}}{T} }[/math].
To find the entropy difference between any two states of a system, the integral must be evaluated for some reversible path between the initial and final states.^{[23]} Since entropy is a state function, the entropy change of the system for an irreversible path is the same as for a reversible path between the same two states.^{[24]} However, the heat transferred to or from, and the entropy change of, the surroundings is different.
We can only obtain the change of entropy by integrating the above formula. To obtain the absolute value of the entropy, we need the third law of thermodynamics, which states that S = 0 at absolute zero for perfect crystals.
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. In any process where the system gives up energy ΔE, and its entropy falls by ΔS, a quantity at least T_{R} ΔS of that energy must be given up to the system's surroundings as heat (T_{R} is the temperature of the system's external surroundings). Otherwise the process cannot go forward. In classical thermodynamics, the entropy of a system is defined only if it is in physical thermodynamic equilibrium (but chemical equilibrium is not required: the entropy of a mixture of two moles of hydrogen and one mole of oxygen at 1 bar pressure and 298 K is welldefined).
Statistical mechanics
The statistical definition was developed by Ludwig Boltzmann in the 1870s by analyzing the statistical behavior of the microscopic components of the system. Boltzmann showed that this definition of entropy was equivalent to the thermodynamic entropy to within a constant factor—known as Boltzmann's constant. In summary, the thermodynamic definition of entropy provides the experimental definition of entropy, while the statistical definition of entropy extends the concept, providing an explanation and a deeper understanding of its nature.
The interpretation of entropy in statistical mechanics is the measure of uncertainty, disorder, 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 are available to the system with appreciable probability, the greater the entropy. In statistical mechanics, entropy is a measure of the number of ways a system can be arranged, often taken to be a measure of "disorder" (the higher the entropy, the higher the disorder).^{[25]}^{[26]}^{[27]} This definition describes the entropy as being proportional to the natural logarithm of the number of possible microscopic configurations of the individual atoms and molecules of the system (microstates) that could cause the observed macroscopic state (macrostate) of the system. The constant of proportionality is the Boltzmann constant.
Boltzmann's constant, and therefore entropy, have dimensions of energy divided by temperature, which has a unit of joules per kelvin (J⋅K^{−1}) in the International System of Units (or kg⋅m^{2}⋅s^{−2}⋅K^{−1} in terms of base units). The entropy of a substance is usually given as an intensive property – either entropy per unit mass (SI unit: J⋅K^{−1}⋅kg^{−1}) or entropy per unit amount of substance (SI unit: J⋅K^{−1}⋅mol^{−1}).
Specifically, entropy is a logarithmic measure of the number of system states with significant probability of being occupied:
 [math]\displaystyle{ S = k_{\mathrm{B}}\sum_i p_i \log p_i, }[/math]
([math]\displaystyle{ p_i }[/math] is the probability that the system is in [math]\displaystyle{ i }[/math]th state, usually given by the Boltzmann distribution; if states are defined in a continuous manner, the summation is replaced by an integral over all possible states) or, equivalently, the expected value of the logarithm of the probability that a microstate is occupied
 [math]\displaystyle{ S = k_{\mathrm{B}} \langle\log p\rangle }[/math]
where k_{B} is the Boltzmann constant, equal to 1.38065×10^{−23} J/K. The summation is over all the possible microstates of the system, and p_{i} is the probability that the system is in the ith microstate.^{[28]} This definition assumes that the basis set of states has been picked so that there is no information on their relative phases. In a different basis set, the more general expression is
 [math]\displaystyle{ S = k_{\mathrm{B}} \operatorname{Tr}(\widehat{\rho} \log(\widehat{\rho})), }[/math]
where [math]\displaystyle{ \widehat{\rho} }[/math] is the density matrix, [math]\displaystyle{ \operatorname{Tr} }[/math] is trace and [math]\displaystyle{ \log }[/math] is the matrix logarithm. This density matrix formulation is not needed in cases of thermal equilibrium so long as the basis states are chosen to be energy eigenstates. For most 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 what has been called the fundamental assumption of statistical thermodynamics or the fundamental postulate in statistical mechanics, among system microstates of the same energy (degenerate microstates) each microstate is assumed to be populated with equal probability; this assumption is usually justified for an isolated system in equilibrium.^{[29]} Then for an isolated system p_{i} = 1/Ω, where Ω is the number of microstates whose energy equals the system's energy, and the previous equation reduces to
 [math]\displaystyle{ S = k_{\mathrm{B}} \log \Omega. }[/math]
In thermodynamics, such a system is one in which the volume, number of molecules, and internal energy are fixed (the microcanonical ensemble).
For a given thermodynamic system, the excess entropy is defined as the entropy minus that of an ideal gas at the same density and temperature, a quantity that is always negative because an ideal gas is maximally disordered.^{[30]} This concept plays an important role in liquidstate theory. For instance, Rosenfeld's excessentropy scaling principle^{[31]}^{[32]} states that reduced transport coefficients throughout the twodimensional phase diagram are functions uniquely determined by the excess entropy.^{[33]}^{[34]}
The most general interpretation of entropy is as a measure of the extent of uncertainty about a system. The equilibrium state of a system maximizes the entropy because it does not reflect all information about the initial conditions, except for the conserved variables. This uncertainty is not of the everyday subjective kind, but rather the uncertainty inherent to the experimental method and interpretative model.^{[35]}
The interpretative model has a central role in determining entropy. The qualifier "for a given set of macroscopic variables" above has deep implications: if two observers use different sets of macroscopic variables, they see 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.^{[36]}
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.
Entropy of a system
Entropy arises directly from the Carnot cycle. It can also be described as the reversible heat divided by temperature. Entropy is a fundamental function of state.
In a thermodynamic system, pressure and temperature tend to become uniform over time because the equilibrium state has higher probability (more possible combinations of microstates) than any other state.
As an example, for a glass of ice water in air at room temperature, the difference in temperature between the warm room (the surroundings) and the cold glass of ice and water (the system and not part of the room) decreases as portions of the thermal energy from the warm surroundings spread to the cooler system of ice and water. Over time the temperature of the glass and its contents and the temperature of the room become equal. In other words, the entropy of the room has decreased as some of its energy has been dispersed to the ice and water, of which the entropy has increased.
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.
Thermodynamic entropy is a nonconserved state function that is of great importance in the sciences of physics and chemistry.^{[25]}^{[37]} Historically, the concept of entropy evolved to explain why some processes (permitted by conservation laws) occur spontaneously while their time reversals (also permitted by conservation laws) do not; systems tend to progress in the direction of increasing entropy.^{[38]}^{[39]} For isolated systems, entropy never decreases.^{[37]} This fact has several important consequences in science: first, it prohibits "perpetual motion" machines; and second, it implies the arrow of entropy has the same direction as the arrow of time. Increases in the total entropy of system and surroundings correspond to irreversible changes, because some energy is expended as waste heat, limiting the amount of work a system can do.^{[25]}^{[26]}^{[40]}^{[41]}
Unlike many other functions of state, entropy cannot be directly observed but must be calculated. Absolute standard molar entropy of a substance can be calculated from the measured temperature dependence of its heat capacity. The molar entropy of ions is obtained as a difference in entropy from a reference state defined as zero entropy. The second law of thermodynamics states that the entropy of an isolated system must increase or remain constant. Therefore, entropy is not a conserved quantity: for example, in an isolated system with nonuniform temperature, heat might irreversibly flow and the temperature become more uniform such that entropy increases.^{[42]} Chemical reactions cause changes in entropy and system entropy, in conjunction with enthalpy, plays an important role in determining in which direction a chemical reaction spontaneously proceeds.
One dictionary definition of entropy is that it is "a measure of thermal energy per unit temperature that is not available for useful work" in a cyclic process. For instance, a substance at uniform temperature is at maximum entropy and cannot drive a heat engine. A substance at nonuniform temperature is at a lower entropy (than if the heat distribution is allowed to even out) and some of the thermal energy can drive a heat engine.
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 is no net exchange of heat or work – the entropy change is 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.^{[43]}
Equivalence of definitions
Proofs of equivalence between the definition of entropy in statistical mechanics (the Gibbs entropy formula [math]\displaystyle{ S = k_{\mathrm{B}}\sum_i p_i \log p_i }[/math]) and in classical thermodynamics ([math]\displaystyle{ d S = \frac{\delta Q_\text{rev}}{T} }[/math] together with the fundamental thermodynamic relation) are known for the microcanonical ensemble, the canonical ensemble, the grand canonical ensemble, and the isothermal–isobaric ensemble. These proofs are based on the probability density of microstates of the generalized Boltzmann distribution and the identification of the thermodynamic internal energy as the ensemble average [math]\displaystyle{ U=\left\langle E_{i}\right\rangle }[/math].^{[44]} Thermodynamic relations are then employed to derive the wellknown Gibbs entropy formula. However, the equivalence between the Gibbs entropy formula and the thermodynamic definition of entropy is not a fundamental thermodynamic relation but rather a consequence of the form of the generalized Boltzmann distribution.^{[45]}
Furthermore, it has been shown that the definitions of entropy in statistical mechanics is the only entropy that is equivalent to the classical thermodynamics entropy under the following postulates:^{[46]}
 The probability density function is proportional to some function of the ensemble parameters and random variables.
 Thermodynamic state functions are described by ensemble averages of random variables.
 At infinite temperature, all the microstates have the same probability.
Second law of thermodynamics
The second law of thermodynamics requires that, in general, the total entropy of any system does not decrease other than by increasing the entropy of some other system. Hence, in a system isolated from its environment, the entropy of that system tends not to decrease. It follows that heat cannot flow from a colder body to a hotter body without the application of work 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, or a single expanding reservoir undergoing adiabatic cooling, which performs adiabatic work. As a result, there is no possibility of a perpetual motion machine. 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), which the air conditioner transports and discharges to the outside air, always makes a bigger contribution to the entropy of the environment than 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.^{[47]} The entropy change of a system at temperature [math]\displaystyle{ T }[/math] absorbing an infinitesimal amount of heat [math]\displaystyle{ \delta q }[/math] in a reversible way, is given by [math]\displaystyle{ \delta q / T }[/math]. More explicitly, an energy [math]\displaystyle{ T_R S }[/math] is not available to do useful work, where [math]\displaystyle{ T_R }[/math] 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 an isolated system. Although this is possible, such an event has a small probability of occurring, making it unlikely.^{[48]}
The applicability of a second law of thermodynamics is limited to systems in or sufficiently near equilibrium state, so that they have defined entropy.^{[49]} Some inhomogeneous systems out of thermodynamic equilibrium still satisfy the hypothesis of local thermodynamic equilibrium, so that entropy density is locally defined as an intensive quantity. For such systems, there may apply a principle of maximum time rate of entropy production.^{[50]}^{[51]} It states that such a system may evolve to a steady state that maximizes its time rate of entropy production. This does not mean that such a system is necessarily always in a condition of maximum time rate of entropy production; it means that it may evolve to such a steady state.^{[52]}^{[53]}
Applications
The fundamental thermodynamic relation
The entropy of a system depends on its internal energy and its external parameters, such as its volume. In the thermodynamic limit, this fact leads to an equation relating the change in the internal energy [math]\displaystyle{ U }[/math] to changes in the entropy and the external parameters. This relation is known as the fundamental thermodynamic relation. If external pressure [math]\displaystyle{ p }[/math] bears on the volume [math]\displaystyle{ V }[/math] as the only external parameter, this relation is:
 [math]\displaystyle{ dU = T \, dS  p \, dV }[/math]
Since both internal energy and entropy are monotonic functions of temperature [math]\displaystyle{ T }[/math], implying that 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 nonquasistatic way (so during this change the system may be very far out of thermal equilibrium and then the wholesystem 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 an isolated system – the combination of a subsystem under study and its surroundings – increases during all spontaneous chemical and physical processes. The Clausius equation of [math]\displaystyle{ \delta q_{\text{rev}} / T = \Delta S }[/math] introduces the measurement of entropy change, [math]\displaystyle{ \Delta S }[/math]. Entropy change describes the direction and quantifies the magnitude of simple changes such as heat transfer between systems – always from hotter to cooler spontaneously.
The thermodynamic entropy therefore has the dimension of energy divided by temperature, and the unit joule per kelvin (J/K) in the International System of Units (SI).
Thermodynamic entropy is an extensive property, meaning that it scales with the size or extent of a system. In many processes it is useful to specify the entropy as an intensive property independent of the size, as a specific entropy characteristic of the type of system studied. Specific entropy may be expressed relative to a unit of mass, typically the kilogram (unit: J⋅kg^{−1}⋅K^{−1}). Alternatively, in chemistry, it is also referred to one mole of substance, in which case it is called the molar entropy with a unit of J⋅mol^{−1}⋅K^{−1}.
Thus, when one mole of substance at about 0 K is warmed by its surroundings to 298 K, the sum of the incremental values of [math]\displaystyle{ q_{\text{rev}} / T }[/math] constitute each element's or compound's standard molar entropy, an indicator of the amount of energy stored by a substance at 298 K.^{[54]}^{[55]} Entropy change also measures the mixing of substances as a summation of their relative quantities in the final mixture.^{[56]}
Entropy is equally essential in predicting the extent and direction of complex chemical reactions. For such applications, [math]\displaystyle{ \Delta S }[/math] must be incorporated in an expression that includes both the system and its surroundings, [math]\displaystyle{ \Delta S_{\text{universe}} = \Delta S_{\text{surroundings}} + \Delta S_{\text{system}} }[/math]. This expression becomes, via some steps, the Gibbs free energy equation for reactants and products in the system: [math]\displaystyle{ \Delta G }[/math] [the Gibbs free energy change of the system] [math]\displaystyle{ = \Delta H }[/math] [the enthalpy change] [math]\displaystyle{  T\,\Delta S }[/math] [the entropy change].^{[54]}
World's technological capacity to store and communicate entropic information
A 2011 study in Science (journal) estimated the world's technological capacity to store and communicate optimally compressed information normalized on the most effective compression algorithms available in the year 2007, therefore estimating the entropy of the technologically available sources.^{[57]} The author's estimate that human kind's technological capacity to store information grew from 2.6 (entropically compressed) exabytes in 1986 to 295 (entropically compressed) exabytes in 2007. The world's technological capacity to receive information through oneway broadcast networks was 432 exabytes of (entropically compressed) information in 1986, to 1.9 zettabytes in 2007. The world's effective capacity to exchange information through twoway telecommunication networks was 281 petabytes of (entropically compressed) information in 1986, to 65 (entropically compressed) exabytes in 2007.^{[57]}
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. Flows of both heat ([math]\displaystyle{ \dot{Q} }[/math]) and work, i.e. [math]\displaystyle{ \dot{W}_\text{S} }[/math] (shaft work) and [math]\displaystyle{ P(dV/dt) }[/math] (pressurevolume work), across the system boundaries, in general cause changes in the entropy of the system. Transfer as heat entails entropy transfer [math]\displaystyle{ \dot{Q}/T }[/math], where [math]\displaystyle{ T }[/math] is the absolute thermodynamic temperature of the system at the point of the heat flow. If there are mass flows across the system boundaries, they also influence the total entropy of the system. This account, in terms of heat and work, is valid only for cases in which the work and heat transfers are by paths physically distinct from the paths of entry and exit of matter from the system.^{[58]}^{[59]}
To derive a generalized entropy balanced equation, we start with the general balance equation for the change in any extensive quantity [math]\displaystyle{ \theta }[/math] in a thermodynamic system, a quantity that may be either conserved, such as energy, or nonconserved, such as entropy. The basic generic balance expression states that [math]\displaystyle{ d\theta/dt }[/math], i.e. the rate of change of [math]\displaystyle{ \theta }[/math] in the system, equals the rate at which [math]\displaystyle{ \theta }[/math] enters the system at the boundaries, minus the rate at which [math]\displaystyle{ \theta }[/math] leaves the system across the system boundaries, plus the rate at which [math]\displaystyle{ \theta }[/math] is generated within the system. For an open thermodynamic system in which heat and work are transferred by paths separate from the paths for transfer of matter, using this generic balance equation, with respect to the rate of change with time [math]\displaystyle{ t }[/math] of the extensive quantity entropy [math]\displaystyle{ S }[/math], the entropy balance equation is:^{[60]}^{[61]}^{[note 1]}
 [math]\displaystyle{ \frac{dS}{dt} = \sum_{k=1}^K \dot{M}_k \hat{S}_k + \frac{\dot{Q}}{T} + \dot{S}_\text{gen} }[/math]
where
 [math]\displaystyle{ \sum_{k=1}^K \dot{M}_k \hat{S}_k }[/math] is the net rate of entropy flow due to the flows of mass into and out of the system (where [math]\displaystyle{ \hat{S} }[/math] is entropy per unit mass).
 [math]\displaystyle{ \frac{\dot{Q}}{T} }[/math] is the rate of entropy flow due to the flow of heat across the system boundary.
 [math]\displaystyle{ \dot{S}_\text{gen} }[/math] is the rate of entropy production within the system. This entropy production arises from processes within the system, including chemical reactions, internal matter diffusion, internal heat transfer, and frictional effects such as viscosity occurring within the system from mechanical work transfer to or from the system.
If there are multiple heat flows, the term [math]\displaystyle{ \dot{Q}/T }[/math] is replaced by [math]\displaystyle{ \sum \dot{Q}_j/T_j, }[/math] where [math]\displaystyle{ \dot{Q}_j }[/math] is the heat flow and [math]\displaystyle{ T_j }[/math] is the temperature at the [math]\displaystyle{ j }[/math]th heat flow port into the system.
Note that the nomenclature "entropy balance" is misleading and often deemed inappropriate because entropy is not a conserved quantity. In other words, the term [math]\displaystyle{ \dot{S}_\text{gen} }[/math] is never a known quantity but always a derived one based on the expression above. Therefore, the open system version of the second law is more appropriately described as the "entropy generation equation" since it specifies that [math]\displaystyle{ \dot{S}_\text{gen} \ge 0 }[/math], with zero for reversible processes or greater than zero for irreversible ones.
Entropy change formulas for simple processes
For certain simple transformations in systems of constant composition, the entropy changes are given by simple formulas.^{[62]}
Isothermal expansion or compression of an ideal gas
For the expansion (or compression) of an ideal gas from an initial volume [math]\displaystyle{ V_0 }[/math] and pressure [math]\displaystyle{ P_0 }[/math] to a final volume [math]\displaystyle{ V }[/math] and pressure [math]\displaystyle{ P }[/math] at any constant temperature, the change in entropy is given by:
 [math]\displaystyle{ \Delta S = n R \ln \frac{V}{V_0} =  n R \ln \frac{P}{P_0} . }[/math]
Here [math]\displaystyle{ n }[/math] is the amount of gas (in moles) and [math]\displaystyle{ R }[/math] is the ideal gas constant. These equations also apply for expansion into a finite vacuum or a throttling process, where the temperature, internal energy and enthalpy for an ideal gas remain constant.
Cooling and heating
For pure heating or cooling of any system (gas, liquid or solid) at constant pressure from an initial temperature [math]\displaystyle{ T_0 }[/math] to a final temperature [math]\displaystyle{ T }[/math], the entropy change is
 [math]\displaystyle{ \Delta S = n C_P \ln \frac{T}{T_0}. }[/math]
provided that the constantpressure molar heat capacity (or specific heat) C_{P} is constant and that no phase transition occurs in this temperature interval.
Similarly at constant volume, the entropy change is
 [math]\displaystyle{ \Delta S = n C_v \ln \frac{T}{T_0}, }[/math]
where the constantvolume molar heat capacity C_{v} is constant and there is no phase change.
At low temperatures near absolute zero, heat capacities of solids quickly drop off to near zero, so the assumption of constant heat capacity does not apply.^{[63]}
Since entropy is a state function, the entropy change of any process in which temperature and volume both vary is the same as for a path divided into two steps – heating at constant volume and expansion at constant temperature. For an ideal gas, the total entropy change is^{[64]}
 [math]\displaystyle{ \Delta S = nC_v \ln \frac{T}{T_0} + nR \ln \frac{V}{V_0}. }[/math]
Similarly if the temperature and pressure of an ideal gas both vary,
 [math]\displaystyle{ \Delta S = nC_P \ln \frac{T}{T_0}  nR \ln \frac{P}{P_0}. }[/math]
Phase transitions
Reversible phase transitions occur at constant temperature and pressure. The reversible heat is the enthalpy change for the transition, and the entropy change is the enthalpy change divided by the thermodynamic temperature.^{[65]} For fusion (melting) of a solid to a liquid at the melting point T_{m}, the entropy of fusion is
 [math]\displaystyle{ \Delta S_\text{fus} = \frac{\Delta H_\text{fus}}{T_\text{m}}. }[/math]
Similarly, for vaporization of a liquid to a gas at the boiling point T_{b}, the entropy of vaporization is
 [math]\displaystyle{ \Delta S_\text{vap} = \frac{\Delta H_\text{vap}}{T_\text{b}}. }[/math]
Approaches to understanding entropy
As a fundamental aspect of thermodynamics and physics, several different approaches to entropy beyond that of Clausius and Boltzmann are valid.
Standard textbook definitions
The following is a list of additional definitions of entropy from a collection of textbooks:
 a measure of energy dispersal at a specific temperature.
 a measure of disorder in the universe or of the availability of the energy in a system to do work.^{[66]}
 a measure of a system's thermal energy per unit temperature that is unavailable for doing useful work.^{[67]}
In Boltzmann's analysis in terms of constituent particles, entropy is a measure of the number of possible microscopic states (or microstates) of a system in thermodynamic equilibrium.
Order and disorder
Entropy is often loosely associated with the amount of order or disorder, or of 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. In this direction, several recent authors have derived exact entropy formulas to account for and measure disorder and order in atomic and molecular assemblies.^{[68]}^{[69]}^{[70]} One of the simpler entropy order/disorder formulas is that derived in 1984 by thermodynamic physicist Peter Landsberg, based on a combination of thermodynamics and information theory arguments. He 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:^{[69]}^{[70]}
 [math]\displaystyle{ \text{Disorder}={C_\text{D}\over C_\text{I}}.\, }[/math]
Similarly, the total amount of "order" in the system is given by:
 [math]\displaystyle{ \text{Order}=1{C_\text{O}\over C_\text{I}}.\, }[/math]
In which C_{D} is the "disorder" capacity of the system, which is the entropy of the parts contained in the permitted ensemble, C_{I} is the "information" capacity of the system, an expression similar to Shannon's channel capacity, and C_{O} is the "order" capacity of the system.^{[68]}
Energy dispersal
File:Ultra slowmotion video of glass tea cup smashed on concrete floor.webm The concept of entropy can be described qualitatively as a measure of energy dispersal at a specific temperature.^{[71]} 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.^{[72]} As the second law of thermodynamics shows, in an isolated system internal portions at different temperatures 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^{[73]} (compare discussion in next section). Physical chemist Peter Atkins, in his textbook Physical Chemistry, introduces entropy with the statement that "spontaneous changes are always accompanied by a dispersal of energy or matter and often both".^{[74]}
Relating entropy to energy usefulness
Following on from the above, it is possible (in a thermal context) to regard lower entropy as an indicator or measure of the effectiveness or usefulness of a particular quantity of energy.^{[75]} This is because energy supplied at a higher temperature (i.e. with low entropy) tends to be more useful than the same amount of energy available at a lower temperature. Mixing a hot parcel of a fluid with a cold one produces a parcel of intermediate temperature, in which the overall increase in entropy represents a "loss" that can never be replaced.
Thus, the fact that the entropy of the universe is steadily increasing, means that its total energy is becoming less useful: eventually, this leads to the "heat death of the Universe."^{[76]}
Entropy and adiabatic accessibility
A definition of entropy based entirely on the relation of adiabatic accessibility between equilibrium states was given by E.H.Lieb and J. Yngvason in 1999.^{[77]} This approach has several predecessors, including the pioneering work of Constantin Carathéodory from 1909^{[78]} and the monograph by R. Giles.^{[79]} In the setting of Lieb and Yngvason one starts by picking, for a unit amount of the substance under consideration, two reference states [math]\displaystyle{ X_0 }[/math] and [math]\displaystyle{ X_1 }[/math] such that the latter is adiabatically accessible from the former but not vice versa. Defining the entropies of the reference states to be 0 and 1 respectively the entropy of a state [math]\displaystyle{ X }[/math] is defined as the largest number [math]\displaystyle{ \lambda }[/math] such that [math]\displaystyle{ X }[/math] is adiabatically accessible from a composite state consisting of an amount [math]\displaystyle{ \lambda }[/math] in the state [math]\displaystyle{ X_1 }[/math] and a complementary amount, [math]\displaystyle{ (1\lambda) }[/math], in the state [math]\displaystyle{ X_0 }[/math]. A simple but important result within this setting is that entropy is uniquely determined, apart from a choice of unit and an additive constant for each chemical element, by the following properties: It is monotonic with respect to the relation of adiabatic accessibility, additive on composite systems, and extensive under scaling.
Entropy in quantum mechanics
In quantum statistical mechanics, the concept of entropy was developed by John von Neumann and is generally referred to as "von Neumann entropy",
 [math]\displaystyle{ S =  k_\mathrm{B}\operatorname{Tr} ( \rho \log \rho ) , }[/math]
where ρ is the density matrix and Tr is the trace operator.
This upholds the correspondence principle, because in the classical limit, when the phases between the basis states used for the classical probabilities are purely random, this expression is equivalent to the familiar classical definition of entropy,
 [math]\displaystyle{ S =  k_\mathrm{B}\sum_i p_i \, \log \, p_i, }[/math]
i.e. in such a basis the density matrix is diagonal.
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 function collapse is described as an irreversible process (the socalled 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.
Information theory
Conversation between Claude Shannon and John von Neumann regarding what name to give to the attenuation in phoneline signals^{[80]}
When viewed in terms of information theory, the entropy state function is the amount of information in the system that is needed to fully specify the microstate of the system. Entropy is the measure of the amount of missing information before reception.^{[81]} Often called Shannon entropy, it was originally devised by Claude Shannon in 1948 to study the size of information of a transmitted message. The definition of information entropy is expressed in terms of a discrete set of probabilities [math]\displaystyle{ p_i }[/math] so that
 [math]\displaystyle{ H(X) = \sum_{i=1}^n p(x_i) \log p(x_i). }[/math]
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 the average size of information of a message. For the case of equal probabilities (i.e. each message is equally probable), the Shannon entropy (in bits) is just the number of binary questions needed to determine the content of the message.^{[28]}
Most researchers consider information entropy and thermodynamic entropy directly linked to the same concept,^{[82]}^{[83]}^{[84]}^{[85]}^{[86]} while others argue that they are distinct.^{[87]} Both expressions are mathematically similar. If [math]\displaystyle{ W }[/math] is the number of microstates that can yield a given macrostate, and each microstate has the same a priori probability, then that probability is [math]\displaystyle{ 1 = p = 1/W }[/math]. The Shannon entropy (in nats) is:
 [math]\displaystyle{ H = \sum_{i=1}^W p \log (p)= \log (W) }[/math] and if entropy is measured in units of [math]\displaystyle{ k }[/math] per nat, then the entropy is given by:
 [math]\displaystyle{ H = k \log (W) }[/math]
which is the Boltzmann entropy formula, where [math]\displaystyle{ k }[/math] is Boltzmann's constant, which may be interpreted as the thermodynamic entropy per nat. Some authors argue for dropping the word entropy for the [math]\displaystyle{ H }[/math] function of information theory and using Shannon's other term, "uncertainty", instead.^{[88]}
Measurement
The entropy of a substance can be measured, although in an indirect way. The measurement, known as entropymetry,^{[89]} is done on a closed system (with particle number N and volume V being constants) and uses the definition of temperature^{[90]} in terms of entropy, while limiting energy exchange to heat ([math]\displaystyle{ dU \rightarrow dQ }[/math]).
 [math]\displaystyle{ T := \left(\frac{\partial U}{\partial S}\right)_{V,N} \Rightarrow \cdots \Rightarrow \; dS=dQ/T }[/math]
The resulting relation describes how entropy changes [math]\displaystyle{ dS }[/math] when a small amount of energy [math]\displaystyle{ dQ }[/math] is introduced into the system at a certain temperature [math]\displaystyle{ T }[/math].
The process of measurement goes as follows. First, a sample of the substance is cooled as close to absolute zero as possible. At such temperatures, the entropy approaches zero – due to the definition of temperature. Then, small amounts of heat are introduced into the sample and the change in temperature is recorded, until the temperature reaches a desired value (usually 25 °C). The obtained data allows the user to integrate the equation above, yielding the absolute value of entropy of the substance at the final temperature. This value of entropy is called calorimetric entropy.^{[91]}
Interdisciplinary applications
Although the concept of entropy was originally a thermodynamic concept, it has been adapted in other fields of study,^{[92]} including information theory, psychodynamics, thermoeconomics/ecological economics, and evolution.^{[68]}^{[93]}^{[94]}^{[95]}^{[96]}
Philosophy and theoretical physics
Entropy is the only quantity in the physical sciences that seems to imply a particular direction of progress, sometimes called an arrow of time. As time progresses, the second law of thermodynamics states that the entropy of an isolated system never decreases in large systems over significant periods of time. Hence, from this perspective, entropy measurement is thought of as a clock in these conditions.
Biology
Chiavazzo et al. proposed that where cave spiders choose to lay their eggs can be explained through entropy minimization.^{[97]}
Entropy has been proven useful in the analysis of base pair sequences in DNA. Many entropybased measures have been shown to distinguish between different structural regions of the genome, differentiate between coding and noncoding regions of DNA, and can also be applied for the recreation of evolutionary trees by determining the evolutionary distance between different species.^{[98]}
Cosmology
Assuming that a finite universe is an isolated system, the second law of thermodynamics states that its total entropy is continually 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.^{[99]}^{[100]}^{[101]} 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 entropyincreasing processes, if they are totally effective matter and energy traps.^{[102]} However, the escape of energy from black holes might be possible due to quantum activity (see Hawking radiation).
The role of entropy in cosmology remains a controversial subject since the time of Ludwig Boltzmann. 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.^{[103]}^{[104]}^{[105]} This results in an "entropy gap" pushing the system further away from the posited heat death equilibrium.^{[106]} 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 largescale thermodynamics extremely difficult.^{[107]}
Current theories suggest the entropy gap to have been originally opened up by the early rapid exponential expansion of the universe.^{[108]}
Economics
Romanian American economist Nicholas GeorgescuRoegen, a progenitor in economics and a paradigm founder of ecological economics, made extensive use of the entropy concept in his magnum opus on The Entropy Law and the Economic Process.^{[83]} Due to GeorgescuRoegen's work, the laws of thermodynamics now form an integral part of the ecological economics school.^{[109]}^{:204f}^{[110]}^{:29–35} Although his work was blemished somewhat by mistakes, a full chapter on the economics of GeorgescuRoegen has approvingly been included in one elementary physics textbook on the historical development of thermodynamics.^{[111]}^{:95–112}
In economics, GeorgescuRoegen's work has generated the term 'entropy pessimism'.^{[112]}^{:116} Since the 1990s, leading ecological economist and steadystate theorist Herman Daly – a student of GeorgescuRoegen – has been the economics profession's most influential proponent of the entropy pessimism position.^{[113]}^{:545f}^{[114]}
See also
 Autocatalytic reactions and order creation
 Boltzmann entropy – a type of Gibbs entropy, which neglects internal statistical correlations in the overall particle distribution
 Brownian ratchet
 Clausius–Duhem inequality
 Configuration entropy
 Conformational entropy – associated with the physical arrangement of a polymer chain that assumes a compact or globular state in solution
 Departure function
 Enthalpy
 Entropic explosion – an explosion in which the reactants expand without releasing much heat
 Entropic force
 Entropy unit
 Entropic value at risk
 Entropy (information theory)
 Entropy (computing)
 Entropy (statistical thermodynamics)
 Entropy and life
 Entropy (order and disorder)
 Entropy of mixing – the change in the entropy when two different chemical substances or components are mixed
 Entropy rate
 Entropy production
 Extropy
 Free entropy – a thermodynamic potential analogous to free energy
 Geometrical frustration
 Gibbs entropy – a precise definition of entropy
 Harmonic entropy
 Heat death of the universe
 Infometrics
 Laws of thermodynamics
 Loop entropy – is the entropy lost upon bringing together two residues of a polymer within a prescribed distance
 Multiplicity function
 Negentropy (negative entropy)
 Orders of magnitude (entropy)
 Phase space
 Principle of maximum entropy
 Residual entropy – the entropy present after a substance is cooled arbitrarily close to absolute zero
 Sackur–Tetrode entropy – the entropy of a monatomic classical ideal gas determined via quantum considerations
 Standard molar entropy – the entropy in one mole of substance under STP
 Stirling's formula
 Thermodynamic databases for pure substances
 Thermodynamic potential
 Thermodynamic equilibrium
 Tsallis entropy – a generalization of the Boltzmann and Gibbs definitions
Notes
 ↑ The overdots represent derivatives of the quantities with respect to time.
References
 ↑ Wehrl, Alfred (19780401). "General properties of entropy". Reviews of Modern Physics 50 (2): 221–260. doi:10.1103/RevModPhys.50.221. Bibcode: 1978RvMP...50..221W. https://link.aps.org/doi/10.1103/RevModPhys.50.221.
 ↑ Truesdell, C. (1980). The Tragicomical History of Thermodynamics 1822–1854, Springer, New York, ISBN:0387904034, page 215.
 ↑ Brush, S.G. (1976). The Kind of Motion We Call Heat: a History of the Kinetic Theory of Gases in the 19th Century, Book 2, Statistical Physics and Irreversible Processes, Elsevier, Amsterdam, ISBN:0444870091, pp. 576–577.
 ↑ Arieh BenNaim, A Farewell to Entropy: Statistical Thermodynamics Based on Information, WorldScientific Publishing Co., Singapore, 2008, ISBN 9789812707062
 ↑ "Carnot, Sadi (1796–1832)". Wolfram Research. 2007. http://scienceworld.wolfram.com/biography/CarnotSadi.html.
 ↑ McCulloch, Richard, S. (1876). Treatise on the Mechanical Theory of Heat and its Applications to the SteamEngine, etc.. D. Van Nostrand.
 ↑ ^{7.0} ^{7.1} Clausius, Rudolf (1850). "Über die bewegende Kraft der Wärme und die Gesetze, welche sich daraus für die Wärmelehre selbst ableiten lassen". Annalen der Physik 155 (3): 368–397. doi:10.1002/andp.18501550306. Bibcode: 1850AnP...155..368C. [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 Physik und Chemie
 ↑ Liddell, H.G., Scott, R. (1843/1978). A Greek–English Lexicon, revised and augmented edition, Oxford University Press, Oxford UK, ISBN:0198642148, pp. 1826–1827.
 ↑ ^{9.0} ^{9.1} Clausius, Rudolf (1865). "Ueber verschiedene für die Anwendung bequeme Formen der Hauptgleichungen der mechanischen Wärmetheorie (Vorgetragen in der naturforsch. Gesellschaft zu Zürich den 24. April 1865)". Annalen der Physik und Chemie 125 (7): 353–400. doi:10.1002/andp.18652010702. Bibcode: 1865AnP...201..353C. https://zenodo.org/record/1423700. "Sucht man für S einen bezeichnenden Namen, so könnte man, ähnlich wie von der Gröſse U gesagt ist, sie sey der Wärme und Werkinhalt des Körpers, von der Gröſse S sagen, sie sey der Verwandlungsinhalt des Körpers. Da ich es aber für besser halte, die Namen derartiger für die Wissenschaft wichtiger Gröſsen aus den alten Sprachen zu entnehmen, damit sie unverändert in allen neuen Sprachen angewandt werden können, so schlage ich vor, die Gröſse S nach dem griechischen Worte ἡ τροπή, die Verwandlung, die Entropie des Körpers zu nennen. Das Wort Entropie habei ich absichtlich dem Worte Energie möglichst ähnlich gebildet, denn die beiden Gröſsen, welche durch diese Worte benannt werden sollen, sind ihren physikalischen Bedeutungen nach einander so nahe verwandt, daſs eine gewisse Gleichartigkeit in der Benennung mir zweckmäſsig zu seyn scheint." (p. 390).
 ↑ ^{10.0} ^{10.1} Gillispie, Charles Coulston (1960). The Edge of Objectivity: An Essay in the History of Scientific Ideas. Princeton University Press. p. 399. ISBN 0691023506. https://archive.org/details/edgeofobjectivit0000gill. "Clausius coined the word entropy for [math]\displaystyle{ S }[/math]: ″I prefer going to the ancient languages for the names of important scientific quantities, so that they may mean the same thing in all living tongues. I propose, accordingly, to call [math]\displaystyle{ S }[/math] the entropy of a body, after the Greek word 'transformation.' I have designedly coined the word entropy to be similar to 'energy,' for these two quantities are so analogous in their physical significance, that an analogy of denomination seemed to me helpful.″"
 ↑ ^{11.0} ^{11.1} Cooper, Leon N. (1968). An Introduction to the Meaning and Structure of Physics. Harper. p. 331.
 ↑ The scientific papers of J. Willard Gibbs in Two Volumes. 1. Longmans, Green, and Co.. 1906. p. 11. https://archive.org/stream/scientificpapers01gibbuoft#page/11/mode/1up. Retrieved 20110226.
 ↑ J. A. McGovern,"2.5 Entropy". http://theory.phy.umist.ac.uk/~judith/stat_therm/node29.html.
 ↑ "6.5 Irreversibility, Entropy Changes, and Lost Work". http://web.mit.edu/16.unified/www/FALL/thermodynamics/notes/node48.html.
 ↑ Lower, Stephen. "What is entropy?". http://www.chem1.com/acad/webtext/thermeq/TE2.html.
 ↑ Lavenda, Bernard H. (2010). "2.3.4". A new perspective on thermodynamics (OnlineAusg. ed.). New York: Springer. ISBN 9781441914309.
 ↑ Carnot, Sadi Carnot (1986). Fox, Robert. ed. Reflexions on the motive power of fire. New York: Lilian Barber Press. pp. 26. ISBN 9780936508160. https://archive.org/details/reflexionsonmoti0000carn.
 ↑ Truesdell, C. (1980). The tragicomical history of thermodynamics 1822–1854. New York: Springer. pp. 78–85. ISBN 9780387904030. https://archive.org/details/tragicomicalhist18221854iiic.
 ↑ Clerk Maxwell, James (2001). Pesic, Peter. ed. Theory of heat. Mineola: Dover Publications. pp. 115–158. ISBN 9780486417356.
 ↑ ^{20.0} ^{20.1} Planck, M. (1945). Treatise on Thermodynamics. Dover Publications. p. §90 & §137. "eqs.(39), (40), & (65)".
 ↑ Rudolf Clausius (1867). The Mechanical Theory of Heat: With Its Applications to the Steamengine and to the Physical Properties of Bodies. J. Van Voorst. p. 28. ISBN 9781498167338. https://books.google.com/books?id=8LIEAAAAYAAJ.
 ↑ Fermi, E. (1956). Thermodynamics. Dover Publications (still in print). p. 48. "eq.(64)".
 ↑ Atkins, Peter; Julio De Paula (2006). Physical Chemistry, 8th ed.. Oxford University Press. p. 79. ISBN 9780198700722.
 ↑ Engel, Thomas; Philip Reid (2006). Physical Chemistry. Pearson Benjamin Cummings. p. 86. ISBN 9780805338423.
 ↑ ^{25.0} ^{25.1} ^{25.2} Licker, Mark D. (2004). McGrawHill concise encyclopedia of chemistry.. New York: McGrawHill Professional. ISBN 9780071439534.
 ↑ ^{26.0} ^{26.1} Sethna, James P. (2006). Statistical mechanics : entropy, order parameters, and complexity. ([OnlineAusg.] ed.). Oxford: Oxford University Press. p. 78. ISBN 9780198566779. https://archive.org/details/statisticalmecha00seth_912.
 ↑ Clark, John O.E. (2004). The essential dictionary of science. New York: Barnes & Noble. ISBN 9780760746165.
 ↑ ^{28.0} ^{28.1} Frigg, R. and Werndl, C. "Entropy – A Guide for the Perplexed". In Probabilities in Physics; Beisbart C. and Hartmann, S. Eds; Oxford University Press, Oxford, 2010
 ↑ Schroeder, Daniel V. (2000). An introduction to thermal physics. San Francisco, CA: Addison Wesley. p. 57. ISBN 9780201380279. https://archive.org/details/introductiontoth00schr_817.
 ↑ Allen, Michael P.; Tildesley, Dominic J. (23 November 2017). "Computer Simulation of Liquids". Oxford Scholarship Online. doi:10.1093/oso/9780198803195.001.0001. ISBN 9780198803195.
 ↑ Rosenfeld, Yaakov (1 June 1977). "Relation between the transport coefficients and the internal entropy of simple systems" (in en). Physical Review A 15 (6): 2545–2549. doi:10.1103/PhysRevA.15.2545. ISSN 05562791. Bibcode: 1977PhRvA..15.2545R.
 ↑ Dyre, Jeppe C. (2018). "Perspective: Excessentropy scaling" (in en). The Journal of Chemical Physics 149 (21): 210901. doi:10.1063/1.5055064. ISSN 00219606. PMID 30525736. Bibcode: 2018JChPh.149u0901D.
 ↑ Bell, Ian H. (2019). "Probing the link between residual entropy and viscosity of molecular fluids and model potentials" (in en). Proceedings of the National Academy of Sciences 116 (10): 4070–4079. doi:10.1073/pnas.1815943116. ISSN 00278424. PMID 30770449.
 ↑ Bell, Ian H.; Dyre, Jeppe C.; Ingebrigtsen, Trond S. (2020). "Excessentropy scaling in supercooled binary mixtures" (in en). Nature Communications 11 (1): 4300. doi:10.1038/s41467020179481. ISSN 20411723. PMID 32855393. Bibcode: 2020NatCo..11.4300B.
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 ↑ Jaynes, E.T. (1992). 'The Gibbs Paradox,' [in Maximum Entropy and Bayesian Methods]. Kluwer Academic: Dordrecht. pp. 1–22. http://www.mdpi.org/lin/entropy/cgibbs.pdf. Retrieved 17 August 2012.
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 ↑ Simon, Donald A. McQuarrie; John D. (1997). Physical chemistry : a molecular approach (Rev. ed.). Sausalito, Calif.: Univ. Science Books. p. 817. ISBN 9780935702996.
 ↑ Haynie, Donald, T. (2001). Biological Thermodynamics. Cambridge University Press. ISBN 9780521791656.
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 ↑ de Rosnay, Joel (1979). The Macroscope – a New World View (written by an M.I.T.trained biochemist). Harper & Row, Publishers. ISBN 9780060110291.
 ↑ McGovern, J. A.. "Heat Capacities". http://theory.phy.umist.ac.uk/~judith/stat_therm/node50.html.
 ↑ BenNaim, Arieh (21 September 2007). "On the SoCalled Gibbs Paradox, and on the Real Paradox". Entropy 9 (3): 132–136. doi:10.3390/e9030133. Bibcode: 2007Entrp...9..132B. http://www.mdpi.org/entropy/papers/e9030132.pdf.
 ↑ Callen, Herbert (2001). Thermodynamics and an Introduction to Thermostatistics (2nd ed.). John Wiley and Sons. ISBN 9780471862567.
 ↑ Gao, Xiang; Gallicchio, Emilio; Roitberg, Adrian (2019). "The generalized Boltzmann distribution is the only distribution in which the GibbsShannon entropy equals the thermodynamic entropy". The Journal of Chemical Physics 151 (3): 034113. doi:10.1063/1.5111333. PMID 31325924. Bibcode: 2019JChPh.151c4113G.
 ↑ Gao, Xiang (March 2022). "The Mathematics of the Ensemble Theory". Results in Physics 34: 105230. doi:10.1016/j.rinp.2022.105230. https://www.sciencedirect.com/science/article/pii/S2211379722000390.
 ↑ Daintith, John (2005). Oxford Dictionary of Physics. Oxford University Press. ISBN 9780192806284.
 ↑ Saha, Arnab; Lahiri, Sourabh; Jayannavar, A. M. (2009). "Entropy production theorems and some consequences". Physical Review E 80 (1): 1–10. doi:10.1103/PhysRevE.80.011117. PMID 19658663. Bibcode: 2009PhRvE..80a1117S.
 ↑ Martyushev, L. M.; Seleznev, V. D. (2014). "The restrictions of the maximum entropy production principle". Physica A: Statistical Mechanics and Its Applications 410: 17–21. doi:10.1016/j.physa.2014.05.014. Bibcode: 2014PhyA..410...17M.
 ↑ Ziegler, H. (1983). An Introduction to Thermomechanics. North Holland, Amsterdam..
 ↑ Onsager, Lars (1931). "Reciprocal Relations in Irreversible Processes". Phys. Rev. 37 (4): 405. doi:10.1103/PhysRev.37.405. Bibcode: 1931PhRv...37..405O.
 ↑ Kleidon, A.; et., al. (2005). Nonequilibrium Thermodynamics and the Production of Entropy. Heidelberg: Springer.
 ↑ Belkin, Andrey; et., al. (2015). "Selfassembled wiggling nanostructures and the principle of maximum entropy production". Scientific Reports 5: 8323. doi:10.1038/srep08323. PMID 25662746. Bibcode: 2015NatSR...5E8323B.
 ↑ ^{54.0} ^{54.1} Moore, J. W.; C. L. Stanistski; P. C. Jurs (2005). Chemistry, The Molecular Science. Brooks Cole. ISBN 9780534422011. https://archive.org/details/chemistrymolecul0000moor.
 ↑ Jungermann, A.H. (2006). "Entropy and the Shelf Model: A Quantum Physical Approach to a Physical Property". Journal of Chemical Education 83 (11): 1686–1694. doi:10.1021/ed083p1686. Bibcode: 2006JChEd..83.1686J.
 ↑ Levine, I. N. (2002). Physical Chemistry, 5th ed.. McGrawHill. ISBN 9780072318081. https://archive.org/details/physicalchemistr00levi_1.
 ↑ ^{57.0} ^{57.1} "The World’s Technological Capacity to Store, Communicate, and Compute Information", Martin Hilbert and Priscila López (2011), Science (journal), 332(6025), 60–65; free access to the article through here: martinhilbert.net/WorldInfoCapacity.html
 ↑ Late Nobel Laureate Max Born (8 August 2015). Natural Philosophy of Cause and Chance. BiblioLife. pp. 44, 146–147. ISBN 9781298497406. https://books.google.com/books?id=er85jgEACAAJ.
 ↑ Haase, R. (1971). Thermodynamics. New York: Academic Press. pp. 1–97. ISBN 9780122456015.
 ↑ Pokrovskii, Vladimir (2020) (in English). Thermodynamics of Complex Systems: Principles and applications.. IOP Publishing, Bristol, UK..
 ↑ Sandler, Stanley, I. (1989). Chemical and Engineering Thermodynamics. John Wiley & Sons. ISBN 9780471830504.
 ↑ "GRC.nasa.gov". GRC.nasa.gov. 27 March 2000. http://www.grc.nasa.gov/WWW/k12/Numbers/Math/Mathematical_Thinking/ideal_gases_under_constant.htm.
 ↑ Franzen, Stefan. "Third Law". ncsu.edu. http://www4.ncsu.edu/~franzen/public_html/CH433/lecture/Third_Law.pdf.
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 ↑ Starzak, Michael E. (2010). "Phase Equilibria & Colligative Properties". Energy & Entropy: Equilibrium to Stationary States. Springer Science+Business Media. pp. 138–140. ISBN 9781489983671. https://books.google.com/books?id=cw0QV7l559kC&pg=PA138. Retrieved 5 September 2019.
 ↑ Gribbin, John (1999). Gribbin, Mary. ed. Q is for quantum : an encyclopedia of particle physics. New York: Free Press. ISBN 9780684855783. https://archive.org/details/qisforquantumenc00grib.
 ↑ "Entropy: Definition and Equation". http://www.britannica.com/EBchecked/topic/189035/entropy.
 ↑ ^{68.0} ^{68.1} ^{68.2} Brooks, Daniel R.; Wiley, E. O. (1988). Evolution as entropy : toward a unified theory of biology (2nd ed.). Chicago [etc.]: University of Chicago Press. ISBN 9780226075747.
 ↑ ^{69.0} ^{69.1} Landsberg, P.T. (1984). "Is Equilibrium always an Entropy Maximum?". J. Stat. Physics 35 (1–2): 159–169. doi:10.1007/bf01017372. Bibcode: 1984JSP....35..159L.
 ↑ ^{70.0} ^{70.1} Landsberg, P.T. (1984). "Can Entropy and "Order" Increase Together?". Physics Letters 102A (4): 171–173. doi:10.1016/03759601(84)909344. Bibcode: 1984PhLA..102..171L.
 ↑ Lambert, Frank L.. "A Student's Approach to the Second Law and Entropy". http://entropysite.oxy.edu/students_approach.html.
 ↑ Watson, J.R.; Carson, E.M. (May 2002). "Undergraduate students' understandings of entropy and Gibbs free energy.". University Chemistry Education 6 (1): 4. ISSN 13695614. http://www.rsc.org/images/Vol_6_No1_tcm187042.pdf.
 ↑ Lambert, Frank L. (February 2002). "Disorder – A Cracked Crutch for Supporting Entropy Discussions". Journal of Chemical Education 79 (2): 187. doi:10.1021/ed079p187. Bibcode: 2002JChEd..79..187L.
 ↑ Peter Atkins; Julio de Paula; James Keeler (2019). Atkins' Physical Chemistry 11e: Volume 3: Molecular Thermodynamics and Kinetics. Oxford University Press. p. 89. ISBN 9780198823360. https://books.google.com/books?id=0UKjDwAAQBAJ&pg=PA89.
 ↑ Sandra Saary (23 February 1993). "Book Review of "A Science Miscellany"". Khaleej Times (UAE: Galadari Press): xi. http://dlmcn.com/entropy2.html.
 ↑ Lathia, R; Agrawal, T; Parmar, V; Dobariya, K; Patel, A (20 October 2015). Heat Death (The Ultimate Fate of the Universe). doi:10.13140/rg.2.1.4158.2485.
 ↑ Lieb, Elliott H.; Yngvason, Jakob (March 1999). "The physics and mathematics of the second law of thermodynamics". Physics Reports 310 (1): 1–96. doi:10.1016/S03701573(98)000829. Bibcode: 1999PhR...310....1L.
 ↑ Carathéodory, C. (September 1909). "Untersuchungen über die Grundlagen der Thermodynamik" (in de). Mathematische Annalen 67 (3): 355–386. doi:10.1007/BF01450409. https://zenodo.org/record/1428268.
 ↑ R. Giles (2016). Mathematical Foundations of Thermodynamics: International Series of Monographs on Pure and Applied Mathematics. Elsevier Science. ISBN 9781483184913. https://books.google.com/books?id=oK03BQAAQBAJ.
 ↑ Tribus, M.; McIrvine, E.C. (1971). "Energy and information". Scientific American 224: 178–184.
 ↑ Balian, Roger (2004). "Entropy, a Protean concept". in Dalibard, Jean. Poincaré Seminar 2003: BoseEinstein condensation – entropy. Basel: Birkhäuser. pp. 119–144. ISBN 9783764371166.
 ↑ Brillouin, Leon (1956). Science and Information Theory. ISBN 9780486439181.
 ↑ ^{83.0} ^{83.1} GeorgescuRoegen, Nicholas (1971). The Entropy Law and the Economic Process. Harvard University Press. ISBN 9780674257818. https://archive.org/details/entropylawe00nich.
 ↑ Chen, Jing (2005). The Physical Foundation of Economics – an Analytical Thermodynamic Theory. World Scientific. ISBN 9789812563231.
 ↑ Kalinin, M.I.; Kononogov, S.A. (2005). "Boltzmann's constant". Measurement Techniques 48 (7): 632–636. doi:10.1007/s1101800501959.
 ↑ BenNaim, Arieh (2008). Entropy demystified the second law reduced to plain common sense (Expanded ed.). Singapore: World Scientific. ISBN 9789812832269. https://archive.org/details/entropydemystifi0000benn.
 ↑ Vallino, Joseph J.; Algar, Christopher K.; González, Nuria Fernández; Huber, Julie A. (2013). "Use of Receding Horizon Optimal Control to Solve MaxEPBased (max entropy production) Biogeochemistry Problems". Beyond the Second Law: Entropy Production & Nonequilibrium Systems. Springer. p. 340. ISBN 9783642401534. https://books.google.com/books?id=xF65BQAAQBAJ&pg=PA340. Retrieved 31 August 2019. "Inked page forms pattern w/ information → page entropy < diff page w/ randomized letters. Reduced entropy trivial compared to inked paper entropy. If the paper is burned, it hardly matters in a thermodynamic context if the text contains the meaning of life or only jibberish [sic]."
 ↑ Schneider, Tom, DELILA system (Deoxyribonucleic acid Library Language), (Information Theory Analysis of binding sites), Laboratory of Mathematical Biology, National Cancer Institute, Frederick, MD
 ↑ Kim, Hye Jin; Park, Youngkyu; Kwon, Yoonjin; Shin, Jaeho; Kim, YoungHan; Ahn, HyunSeok; Yazami, Rachid; Choi, Jang Wook (2020). "Entropymetry for nondestructive structural analysis of LiCoO 2 cathodes" (in en). Energy & Environmental Science 13 (1): 286–296. doi:10.1039/C9EE02964H. ISSN 17545692. http://xlink.rsc.org/?DOI=C9EE02964H.
 ↑ Schroeder, Daniel V. (2000). An introduction to thermal physics ([Nachdr.] ed.). San Francisco, CA [u.a.]: Addison Wesley. p. 88. ISBN 9780201380279. https://archive.org/details/introductiontoth00schr_817.
 ↑ "Measuring Entropy". https://www.chem.wisc.edu/deptfiles/genchem/netorial/modules/thermodynamics/entropy/entropy04.htm.
 ↑ Pokrovskii, Vladimir (2020) (in English). Thermodynamics of Complex Systems: Principles and applications.. IOP Publishing, Bristol, UK..
 ↑ Avery, John (2003). Information Theory and Evolution. World Scientific. ISBN 9789812383990.
 ↑ Yockey, Hubert, P. (2005). Information Theory, Evolution, and the Origin of Life. Cambridge University Press. ISBN 9780521802932.
 ↑ Chiavazzo, Eliodoro; Fasano, Matteo; Asinari, Pietro (2013). "Inference of analytical thermodynamic models for biological networks". Physica A: Statistical Mechanics and Its Applications 392 (5): 1122–1132. doi:10.1016/j.physa.2012.11.030. Bibcode: 2013PhyA..392.1122C. https://iris.polito.it/bitstream/11583/2504927/1/BIOaps_final_R01c.pdf.
 ↑ Chen, Jing (2015). The Unity of Science and Economics: A New Foundation of Economic Theory. Springer. doi:10.1007/9781493934669. ISBN 9781493934645. https://www.springer.com/us/book/9781493934645.
 ↑ Chiavazzo, Eliodoro; Isaia, Marco; Mammola, Stefano; Lepore, Emiliano; Ventola, Luigi; Asinari, Pietro; Pugno, Nicola Maria (2015). "Cave spiders choose optimal environmental factors with respect to the generated entropy when laying their cocoon". Scientific Reports 5: 7611. doi:10.1038/srep07611. PMID 25556697. Bibcode: 2015NatSR...5E7611C.
 ↑ Thanos, Dimitrios; Li, Wentian; Provata, Astero (1 March 2018). "Entropic fluctuations in DNA sequences". Physica A: Statistical Mechanics and Its Applications 493: 444–457. doi:10.1016/j.physa.2017.11.119. ISSN 03784371. Bibcode: 2018PhyA..493..444T.
 ↑ von Baeyer, Christian, H. (2003). Information–the New Language of Science. Harvard University Press. ISBN 9780674013872. https://archive.org/details/informationnewla00vonb.
 ↑ Srednicki M (August 1993). "Entropy and area". Phys. Rev. Lett. 71 (5): 666–669. doi:10.1103/PhysRevLett.71.666. PMID 10055336. Bibcode: 1993PhRvL..71..666S.
 ↑ Callaway DJE (April 1996). "Surface tension, hydrophobicity, and black holes: The entropic connection". Phys. Rev. E 53 (4): 3738–3744. doi:10.1103/PhysRevE.53.3738. PMID 9964684. Bibcode: 1996PhRvE..53.3738C.
 ↑ Sarkar, T.K.; SalazarPalma, M.; Mokole, Eric L. (2008). "A Look at the Concept of Channel Capacity from a Maxwellian Viewpoint". Physics of Multiantenna Systems & Broadband Processing. Wiley. p. 162. ISBN 9780470190401. https://books.google.com/books?id=chfvTMRsv38C&pg=PA162. Retrieved 31 August 2019.
 ↑ Layzer, David (1988). Growth of Order in the Universe. MIT Press.
 ↑ Chaisson, Eric J. (2001). Cosmic Evolution: The Rise of Complexity in Nature. Harvard University Press. ISBN 9780674003422. https://archive.org/details/cosmicevolutionr00chai.
 ↑ Complexity and the Arrow of Time. Cambridge University Press. 2013. ISBN 9781107027251.
 ↑ Stenger, Victor J. (2007). God: The Failed Hypothesis. Prometheus Books. ISBN 9781591024811.
 ↑ Benjamin GalOr (1987). Cosmology, Physics and Philosophy. Springer Verlag. ISBN 9780387965260.
 ↑ Albrecht, Andreas (2004). "Cosmic inflation and the arrow of time". in Barrow, John D.. Science and Ultimate Reality: From Quantum to Cosmos. Cambridge, UK: Cambridge University Press. Bibcode: 2002astro.ph.10527A. https://arxiv.org/ftp/astroph/papers/0210/0210527.pdf. Retrieved 28 June 2017none (in honor of John Wheeler's 90th birthday)
 ↑ Cleveland, Cutler J.; Ruth, Matthias (1997). "When, where, and by how much do biophysical limits constrain the economic process? A survey of Nicholas GeorgescuRoegen's contribution to ecological economics". Ecological Economics (Amsterdam: Elsevier) 22 (3): 203–223. doi:10.1016/s09218009(97)000797.
 ↑ Daly, Herman E.; Farley, Joshua (2011) (PDF contains full book). Ecological Economics. Principles and Applications. (2nd ed.). Washington: Island Press. ISBN 9781597266819. http://library.uniteddiversity.coop/Measuring_Progress_and_Eco_Footprinting/Ecological_EconomicsPrinciples_and_Applications.pdf.
 ↑ Schmitz, John E.J. (2007) (Link to the author's science blog, based on his textbook). The Second Law of Life: Energy, Technology, and the Future of Earth As We Know It.. Norwich: William Andrew Publishing. ISBN 9780815515371. https://secondlawoflife.wordpress.com/contents.
 ↑ Ayres, Robert U. (2007). "On the practical limits to substitution". Ecological Economics (Amsterdam: Elsevier) 61: 115–128. doi:10.1016/j.ecolecon.2006.02.011. http://pure.iiasa.ac.at/id/eprint/7800/1/IR05036.pdf.
 ↑ Kerschner, Christian (2010). "Economic degrowth vs. steadystate economy". Journal of Cleaner Production (Amsterdam: Elsevier) 18 (6): 544–551. doi:10.1016/j.jclepro.2009.10.019. http://degrowth.org/wpcontent/uploads/2012/11/Kerschner2010.pdf.
 ↑ Daly, Herman E. (2015). "Economics for a Full World". Scientific American 293 (3): 100–7. doi:10.1038/scientificamerican0905100. PMID 16121860. http://www.greattransition.org/publication/economicsforafullworld. Retrieved 23 November 2016.
Further reading
 Adam, Gerhard; Otto Hittmair (1992). Wärmetheorie. Vieweg, Braunschweig. ISBN 9783528333119.
 Atkins, Peter; Julio De Paula (2006). Physical Chemistry (8th ed.). Oxford University Press. ISBN 9780198700722.
 Baierlein, Ralph (2003). Thermal Physics. Cambridge University Press. ISBN 9780521658386. https://archive.org/details/thermalphysics00ralp.
 BenNaim, Arieh (2007). Entropy Demystified. World Scientific. ISBN 9789812700551.
 Callen, Herbert, B (2001). Thermodynamics and an Introduction to Thermostatistics (2nd ed.). John Wiley and Sons. ISBN 9780471862567.
 Chang, Raymond (1998). Chemistry (6th ed.). New York: McGraw Hill. ISBN 9780071152211. https://archive.org/details/chemistry00chan_0.
 Cutnell, John, D.; Johnson, Kenneth, J. (1998). Physics (4th ed.). John Wiley and Sons, Inc.. ISBN 9780471191131. https://archive.org/details/physi1998cutn.
 Dugdale, J. S. (1996). Entropy and its Physical Meaning (2nd ed.). Taylor and Francis (UK); CRC (US). ISBN 9780748405695.
 Fermi, Enrico (1937). Thermodynamics. Prentice Hall. ISBN 9780486603612.
 Goldstein, Martin; Inge, F (1993). The Refrigerator and the Universe. Harvard University Press. ISBN 9780674753259. https://archive.org/details/refrigeratoruniv0000gold.
 Gyftopoulos, E.P.; G.P. Beretta (2010). Thermodynamics. Foundations and Applications. Dover. ISBN 9780486439327.
 Haddad, Wassim M.; Chellaboina, VijaySekhar; Nersesov, Sergey G. (2005). Thermodynamics – A Dynamical Systems Approach. Princeton University Press. ISBN 9780691123271.
 Johnson, Eric (2018). Anxiety and the Equation: Understanding Boltzmann's Entropy. The MIT Press. ISBN 9780262038614.
 Kroemer, Herbert; Charles Kittel (1980). Thermal Physics (2nd ed.). W. H. Freeman Company. ISBN 9780716710882.
 Lambert, Frank L.; entropysite.oxy.edu
 MüllerKirsten, Harald J. W. (2013). Basics of Statistical Physics (2nd ed.). Singapore: World Scientific. ISBN 9789814449533.
 Penrose, Roger (2005). The Road to Reality: A Complete Guide to the Laws of the Universe. New York: A. A. Knopf. ISBN 9780679454434. https://archive.org/details/roadtorealitycom00penr_0.
 Reif, F. (1965). Fundamentals of statistical and thermal physics. McGrawHill. ISBN 9780070518001. https://archive.org/details/fundamentalsofst00fred.
 Schroeder, Daniel V. (2000). Introduction to Thermal Physics. New York: Addison Wesley Longman. ISBN 9780201380279.
 Serway, Raymond, A. (1992). Physics for Scientists and Engineers. Saunders Golden Subburst Series. ISBN 9780030960260. https://archive.org/details/physicsforscient00serw.
 Sharp, Kim (2019). Entropy and the Tao of Counting: A Brief Introduction to Statistical Mechanics and the Second Law of Thermodynamics (SpringerBriefs in Physics). Springer Nature. ISBN:9783030354596.
 SpiraxSarco Limited, Entropy – A Basic Understanding A primer on entropy tables for steam engineering
 vonBaeyer; Hans Christian (1998). Maxwell's Demon: Why Warmth Disperses and Time Passes. Random House. ISBN 9780679433422.
External links
 Entropy and the Second Law of Thermodynamics – an Alevel physics lecture with 'derivation' of entropy based on Carnot cycle
 Khan Academy: entropy lectures, part of Chemistry playlist
 The Second Law of Thermodynamics and Entropy – Yale OYC lecture, part of Fundamentals of Physics I (PHYS 200)
 Entropy and the Clausius inequality MIT OCW lecture, part of 5.60 Thermodynamics & Kinetics, Spring 2008
 The Discovery of Entropy by Adam Shulman. Hourlong video, January 2013.
 Moriarty, Philip; Merrifield, Michael (2009). "S Entropy". Sixty Symbols. Brady Haran for the University of Nottingham. http://www.sixtysymbols.com/videos/entropy.htm.
 "Entropy" at Scholarpedia
Original source: https://en.wikipedia.org/wiki/ Entropy.
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