Physics I Entropy: Reversibility, Disorder, and Information Prof. WAN, Xin

1st & 2nd Laws of Thermodynamics F The 1st law specifies that we cannot get more energy out of a cyclic process by work than the amount of energy we put in. F The 2nd law states that we cannot break even because we must put more energy in, at the higher temperature, than the net amount of energy we get out by work.

Carnot’s Engine

Efficiency of a Carnot Engine All Carnot engines operating between the same two temperatures have the same efficiency.

An Equality Now putting in the proper signs, negativepositive

A Sum of Carnot Cycles V P adiabats T h,i T c,i Any reversible process can be approximated by a sum of Carnot cycles, hence

Clausius Definition of Entropy F Entropy is a state function, the change in entropy during a process depends only on the end points and is independent of the actual path followed. 1 2 C1C1 C2C2

Return to Inexact Differential Assume Note:is an exact differential. Integrating factor

Digression on Multivariate Calculus F Heat is path dependent. F Therefore, 1/T is really the integrating factor for the differential form of heat. Now we can recast the 1st law of thermodynamics as F Entropy is also a state function, as is the internal energy or volume.

Entropy of an Ideal Gas (1 mole) Integrating from (T 0,V 0 ) to (T, V)

Carnot’s Theorem No real heat engine operating between two energy reservoirs can be more efficient than Carnot’s engine operating between the same two reservoirs. What does this mean? Still, for any engine in a cycle (S is a state function!) negativepositive

Counting the Heat Baths in after a cycle Q' h > 0 Q' c < 0

Counting the Heat Baths in after a cycle Q' h > 0 Q' c < 0 The total entropy of an isolated system that undergoes a change can never decrease.

Example 1: Clausius Statement Irreversible!

Example 2: Kelvin Statement Irreversible!

Example 3: Mixing Water T A A T B B Q TATA TBTB TT Q T A < T B

Example 3: Mixing Water Irreversible! T A A T B B Q TATA TBTB TT Q T A < T B For simplicity, assume

We can only calculate  S with a reversible process! In this case, we replace the free expansion by the isothermal process with the same initial and final states. Example 4: Free Expansion ? Irreversible!

The Second Law in terms of Entropy F The total entropy of an isolated system that undergoes a change can never decrease. –If the process is irreversible, then the total entropy of an isolated system always increases. –In a reversible process, the total entropy of an isolated system remains constant. F The change in entropy of the Universe must be greater than zero for an irreversible process and equal to zero for a reversible process.

Order versus Disorder F Isolated systems tend toward disorder and that entropy is a measure of this disorder. Ordered: all molecules on the left side Disordered: molecules on the left and right

Macrostate versus Microstate F Each of the microstates is equally probable. F Ordered microstate to be very unlikely because random motions tend to distribute molecules uniformly. F There are many more disordered microstates than ordered microstates. F A macrostate corresponding to a large number of equivalent disordered microstates is much more probable than a macrostate corresponding to a small number of equivalent ordered microstates. F How much more probable?

Entropy: A Measure of Disorder We assume that each molecule occupies some microscopic volume V m. suggesting(Boltzmann)

A Similar Probability Problem

Let’s Play Cards F Imagine shuffling a deck of playing cards: –Systems have a natural tendency to become more and more disordered. F Disorder almost always increases is that disordered states hugely outnumber highly ordered states, such that the system inevitably settles down in one of the more disordered states.

Computers are useless. They can only give us answers Pablo Picasso

Information and Entropy F (1927) Bell Labs, Ralph Hartley –Measure for information in a message –Logarithm: 8 bit = 2 8 = 256 different numbers F (1940) Bell Labs, Claude Shannon –“A mathematical theory of communication” –Probability of a particular message But there is no information. You are not winning the lottery.

Information and Entropy F (1927) Bell Labs, Ralph Hartley –Measure for information in a message –Logarithm: 8 bit = 2 8 = 256 different numbers F (1940) Bell Labs, Claude Shannon –“A mathematical theory of communication” –Probability of a particular message Okay, you are going to win the lottery. Now that’s something.

Information and Entropy F (1927) Bell Labs, Ralph Hartley –Measure for information in a message –Logarithm: 8 bit = 2 8 = 256 different numbers F (1940) Bell Labs, Claude Shannon –“A mathematical theory of communication” –Probability of a particular message –Information ~ - log (probability) ~ negative entropy

It is already in use under that name. … and besides, it will give you great edge in debates because nobody really knows what entropy is anyway John von Neumann

Maxwell’s Demon To determine whether to let a molecule through, the demon must acquire information about the state of the molecule. However well prepared, the demon will eventually run out of information storage space and must begin to erase the information it has previously gathered. Erasing information is a thermodynamically irreversible process that increases the entropy of a system.

Landauer’s Principle & Verification F Computation needs to involve heat dissipation only when you do something irreversible with the information. F Lutz group (2012)

Homework (for the 2nd Law) CHAP. 24 Exercises 25, 30, 35 (P565) 4, 8 (P566)

Homework F Reading (downloadable from my website): –Charles Bennett and Rolf Landauer, The fundamental physical limits of computation. –Antoine Bérut et al., Experimental verification of Landauer’s principle linking information and thermodynamics, Nature (2012). –Seth Lloyd, Ultimate physical limits to computation, Nature (2000). Dare to adventure where you have not been!